U.S. GOVERNMENT PUBLISHING OFFICE

32–753 PDF WASHINGTON : 2018

S. HRG. 115–380 

FINTECH: EXAMINING DIGITIZATION, DATA, AND 

TECHNOLOGY 

HEARING 

BEFORE THE

COMMITTEE ON 

BANKING, HOUSING, AND URBAN AFFAIRS 

UNITED STATES SENATE 

ONE HUNDRED FIFTEENTH CONGRESS 

SECOND SESSION 

ON 

EXAMINING FURTHER THE DIGITIZATION, DATA, AND TECHNOLOGY 

ASPECTS OF FINTECH 

SEPTEMBER 18, 2018 

Printed for the use of the Committee on Banking, Housing, and Urban Affairs 

( 

Available at: http://www.govinfo.gov/ 

COMMITTEE ON BANKING, HOUSING, AND URBAN AFFAIRS 

MIKE CRAPO, Idaho, Chairman 

RICHARD C. SHELBY, Alabama 

BOB CORKER, Tennessee 

PATRICK J. TOOMEY, Pennsylvania 

DEAN HELLER, Nevada 

TIM SCOTT, South Carolina 

BEN SASSE, Nebraska 

TOM COTTON, Arkansas 

MIKE ROUNDS, South Dakota 

DAVID PERDUE, Georgia 

THOM TILLIS, North Carolina 

JOHN KENNEDY, Louisiana 

JERRY MORAN, Kansas 

SHERROD BROWN, Ohio 

JACK REED, Rhode Island 

ROBERT MENENDEZ, New Jersey 

JON TESTER, Montana 

MARK R. WARNER, Virginia 

ELIZABETH WARREN, Massachusetts 

HEIDI HEITKAMP, North Dakota 

JOE DONNELLY, Indiana 

BRIAN SCHATZ, Hawaii 

CHRIS VAN HOLLEN, Maryland 

CATHERINE CORTEZ MASTO, Nevada 

DOUG JONES, Alabama 

GREGG RICHARD, Staff Director 

MARK POWDEN, Democratic Staff Director 

JOE CARAPIET, Chief Counsel 

KRISTINE JOHNSON, Economist 

LAURA SWANSON, Democratic Deputy Staff Director 

ELISHA TUKU, Democratic Chief Counsel 

DAWN RATLIFF, Chief Clerk 

CAMERON RICKER, Deputy Clerk 

JAMES GUILIANO, Hearing Clerk 

SHELVIN SIMMONS, IT Director 

JIM CROWELL, Editor 

(II)

C O N T E N T S 

TUESDAY, SEPTEMBER 18, 2018 

Page 

Opening statement of Chairman Crapo ................................................................. 1 

Prepared statement .......................................................................................... 29 

Opening statements, comments, or prepared statements of: 

Senator Brown .................................................................................................. 2 

Prepared statement ................................................................................... 29 

WITNESSES 

Steven Boms, President, Allon Advocacy, LLC, on behalf of Consumer Financial Data Rights .................................................................................................... 4 

Prepared statement .......................................................................................... 30 

Responses to written questions of: 

Senator Brown ........................................................................................... 117 

Senator Scott ............................................................................................. 117 

Stuart Rubinstein, President, Fidelity Wealth Technologies, and Head of Data 

Aggregation ........................................................................................................... 6 

Prepared statement .......................................................................................... 37 

Brian Knight, Director, Innovation and Governance Program, Mercatus Center at George Mason University ......................................................................... 7 

Prepared statement .......................................................................................... 40 

Responses to written questions of: 

Senator Brown ........................................................................................... 118 

Senator Heller ........................................................................................... 119 

Saule T. Omarova, Professor of Law, and Director, Jack Clarke Program 

on Law and Regulations of Financial Institutions and Markets, Cornell 

University ............................................................................................................. 9 

Prepared statement .......................................................................................... 45 

Responses to written questions of: 

Senator Reed .............................................................................................. 119 

ADDITIONAL MATERIAL SUPPLIED FOR THE RECORD

Letter From The American Academy of Actuaries Submitted by Chairman 

Mike Crapo ........................................................................................................... 122 

Statement From Financial Innovation Now Submitted by Chairman Mike 

Crapo ..................................................................................................................... 182 

Letter From Electronic Privacy Information Center Submitted by Senator 

Sherrod Brown ..................................................................................................... 184 

Statement Submitted by Independent Community Bankers of America ............ 187 

(III)

(1) 

FINTECH: EXAMINING DIGITIZATION, DATA, 

AND TECHNOLOGY 

TUESDAY, SEPTEMBER 18, 2018 

U.S. SENATE, 

COMMITTEE ON BANKING, HOUSING, AND URBAN AFFAIRS, 

Washington, DC. 

The Committee met at 10:01 a.m., in room SD–538, Dirksen Senate Office Building, Hon. Mike Crapo, Chairman of the Committee, 

presiding. 

OPENING STATEMENT OF CHAIRMAN MIKE CRAPO 

Chairman CRAPO. This hearing will come to order. 

Today we will hear four very unique perspectives on a segment 

of financial technology, or ‘‘FinTech.’’ 

Almost exactly 1 year ago, the Committee held a hearing to explore the various sectors and applications of FinTech. 

In the short time period between that hearing and this one, 

many developments and innovations have occurred, both in the private sector and on the regulatory front. 

Digitization and data, in particular, are constantly evolving, challenging the way we have traditionally approached and conducted 

oversight of the financial services sector. 

As technology has developed and the ability to readily and cheaply interact with and use data has flourished, we have experienced 

a sort of revolution in the digital era. This digital revolution brings 

with it the promise of increasing consumer choice, inclusion, and 

economic prosperity, among other things. 

Less than a decade ago, the concept of mobile banking, a simple 

transaction, was relatively new. Now consumers have countless options by which to interact with and access their financial information and conduct transactions. 

As this marketplace rapidly develops, so must we constantly 

evaluate our regulatory and oversight framework, much of which 

was designed prior to the digital era. To the extent that there are 

improvements that can be made to better foster and not stifle innovation, we should examine those. 

Although these technological developments are incredibly positive, the increased digitization and ease of collecting, storing, and 

using data presents a new set of challenges and requires our vigilance. 

Many products and services in the FinTech sector revolve around 

big data analytics, data aggregation, and other technologies that 

make use of consumer data. Oftentimes these processes operate in 

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the background, and are not always completely transparent to consumers. 

It is important for consumers to know when their data is being 

collected and how it is being used. It is equally important for the 

companies and the Government alike to act responsibly with this 

data and ensure that it is protected. 

As we have seen in recent years, this can be a challenging task. 

In order to fully embrace the immense benefits that can result from 

technological innovation, we must ensure that proper safeguards 

are in place and consumers are fully informed. 

Today I hope to hear from our witnesses about the ways in which 

FinTech is changing the financial sector and the improvements 

that can be made to ensure the regulatory landscape welcomes that 

innovation; what kind of data is being collected and used and how 

such data is secured and protected; and what the opportunities and 

challenges are going forward. 

Senator Brown. 

OPENING STATEMENT OF SENATOR SHERROD BROWN 

Senator BROWN. Thank you, Mr. Chairman. 

In the run-up to the financial crisis, Wall Street banks bragged 

about innovations that they claimed made the financial system less 

risky and credit more affordable. Some of these innovations were 

in consumer products, like interest-only subprime mortgages. Other 

innovations were happening behind the scenes, like the growth in 

risky collateralized debt obligations and credit default swaps. 

According to the banks, technological advances like increased 

computing power and information sharing through the Internet allowed financial institutions to calculate and mitigate the risks of 

these complex financial innovations. In Washington, banks told 

lawmakers that regulation would hold back progress—they say 

that often on many issues—and make credit more expensive for 

consumers. Rather than look at financial technology with an eye to 

the risks, Federal banking supervisors repealed safety and soundness protections, and they used their authority to override consumer protection laws in several States. 

Eventually, so-called financial innovations led to the biggest economic disaster in almost a century, costing millions of Americans 

their homes, their jobs, and much of their savings. 

Criticizing the bankers and regulators who lost sight of the enormous risks that came with these new innovations, former Fed 

Chair Paul Volcker declared, ‘‘The ATM has been the only useful 

innovation in banking for the past 20 years.’’ 

I am more optimistic about some new technologies benefiting 

consumers rather than just lining Wall Street’s pockets, but I think 

we should look at this Treasury report with the same level of skepticism. 

Rather than learn from past mistakes, the Treasury report embraces the shortsightedness of precrisis regulators. It exalts the 

benefits of ‘‘financial innovation,’’ describes Federal and State regulation as ‘‘cumbersome’’ or as ‘‘barriers to innovation,’’ and recommends gutting important consumer protections, like the CFPB’s 

payday lending rule. It even suggests stripping away what little 

control we as consumers now have over our own personal financial 

3 

data, just a year after Equifax put 148 million Americans’ identities at risk, 5 million in my State alone. 

Just like a dozen years ago, Wall Street banks and big companies 

are making record profits, but working families are struggling just 

to get by. Student loan debt is at record levels; credit card defaults 

are rising. Worker pay is not keeping up with inflation—comments 

from the Administration notwithstanding—but we have managed 

to cut taxes for the richest Americans while CEOs and shareholders have reaped huge windfalls through over half a trillion dollars in stock buybacks. 

Plenty of financial institutions are adopting new technologies 

without running afoul of the law. Rather than focusing on how we 

can weaken the rules for a handful of companies who prefer to be 

called ‘‘FinTechs’’ rather than ‘‘payday lenders,’’ or ‘‘data 

aggregators’’ rather than ‘‘consumer reporting bureaus,’’ Treasury 

should be focused on policies that help working families. 

This is not a partisan issue for me. I raised concerns about relaxing the rules for FinTech firms when Comptroller Curry, appointed 

by President Obama, suggested a special ‘‘FinTech’’ charter almost 

2 years ago. 

The new leaders at the Federal Reserve, the OCC, the FDIC, and 

the CFPB have already made it clear that they are ready to give 

Wall Street whatever it asks for. And they never have enough. And 

the recommendations in this report call for more handouts for financial firms, FinTech or otherwise. 

I am interested, however, to hear from our witnesses about how 

new financial technologies could increase our control over our own 

information, better protect against cyberattacks, or make it easier 

for lenders to ensure they are following the law. And as traditional 

banks partner with technology firms, I think it is important for the 

Committee to consider where gaps in regulation might lead to future systemic risks. 

Thank you, Mr. Chairman, for holding the hearing. 

Chairman CRAPO. Thank you, Senator Brown. And I agree with 

you this is not a partisan issue. We all want to get the benefits of 

what can be developed with this kind of increase in technological 

capacity. But there is significant concern about privacy and protection of data of our consumers that is agreed to on both sides of the 

aisle here, I believe. 

We welcome our witnesses here with us today. We have Mr. Steven Boms, the president of Allon Advocacy, on behalf of the Consumer Financial Data Rights association; Mr. Stuart Rubinstein, 

president of Fidelity Wealth Technologies; Mr. Brian Knight, director of the Innovation and Governance Program at Mercatus Center 

at George Mason University; and Ms. Saule Omarova, who is a professor of law and director of the Jack Clarke Program on the Law 

and Regulation of Financial Institutions and Markets at Cornell 

University. 

We again welcome all of you. We appreciate your being here to 

share your expertise with us. Your written statements will be made 

a part of the record. We ask you to please be very careful to pay 

attention to the 5-minute clock for your oral comments and as you 

are engaged in questioning. The Senators have a 5-minute clock, 

too, and sometimes they run right up to the last second for their 

4 

last question, and when that happens, I ask you to be prompt in 

your responses to those questions. 

With that, Mr. Boms, you may begin. 

STATEMENT OF STEVEN BOMS, PRESIDENT, ALLON ADVOCACY, LLC, ON BEHALF OF CONSUMER FINANCIAL DATA 

RIGHTS 

Mr. BOMS. Thank you, Mr. Chairman. 

Chairman Crapo, Ranking Member Brown, and Members of the 

Committee, thank you for this opportunity to testify today on behalf of the Consumer Financial Data Rights, or CFDR, Group, a 

consortium of approximately 50 aggregators and FinTech firms 

united behind consumers’ rights to access their financial data. 

My testimony this morning also represents the views of the Financial Data and Technology Association, or FDATA, of North 

America, which is the trade association urging the adoption of an 

open banking-like regime in the U.S., Canada, and Mexico. 

The CFDR Group and its members consulted frequently with the 

Treasury Department as it considered the current state of the 

FinTech market. Our engagement was principally focused on the 

crucial issue of consumer-permissioned financial data, which was 

an area of emphasis in the Department’s report and which I would 

like to focus on today. 

A recent White House study concluded that 20 percent of adult 

Americans are underbanked by the traditional financial services 

system and almost 9 million households are entirely unbanked. For 

these consumers, third-party, technology-based tools can provide 

vital, affordable access to a financial system that has left them behind. These tools also help other Americans address the growing 

complexity of the financial system. Most consumers have multiple 

accounts across a variety of products providers. The most basic, 

fundamental first step toward financial health—understanding 

what one has and what one owes—can be needlessly difficult. Technology-powered tools can provide intuitive, accessible platforms 

that enable even the least financially savvy among us to manage 

their finances and improve their economic outcomes. The lifeblood 

of these tools is user-permissioned data access: the right of the consumer or the small business to affirmatively grant access to the application of their choice to connect to or see the financial data. 

Unlike in other jurisdictions globally, there is no legal requirement in the United States stipulating that a financial institution 

must make the consumer’s a small business’ financial data it holds 

available to a third party when the customer provides consent or 

whether restrictions on the consumer’s access to that data are permissible. Consumers are dependent on the financial services providers with which they do business, with disparate outcomes for 

Americans who bank with different financial institutions. The lack 

of a cohesive framework also threatens American competitiveness 

and financial innovation internationally. 

The Treasury Department identified the key outstanding issues 

with regard to user-permissioned data access. I briefly highlight 

five Treasury recommendations for the Committee’s consideration 

here, noting that I provide significantly more reaction in my written testimony. 

5 

Number one, the Bureau of Consumer Financial Protection 

should affirm that third parties properly authorized by consumers 

fall within the definition of ‘‘consumer’’ for the purpose of obtaining 

access to financial account and transaction data. 

Though it may seem self-evident, Section 1033 of Dodd–Frank 

provides that the Bureau has the authority to promulgate a rule 

to ensure end users have electronic access to their online data. But 

the Bureau has thus far declined to do so. Treasury’s affirmation 

that Dodd–Frank provides this right to consumers and small businesses, even in the absence of a Bureau rulemaking, represents a 

significant victory for innovation and for consumer and small business financial empowerment. 

Number two, all regulators should recognize the benefits of consumer access to financial account and transaction data in electronic 

form. 

One of the systemic disadvantages facing the FinTech ecosystem 

in the United States is the immense relative regulatory fragmentation that exists. There are at least eight Federal regulatory agencies with jurisdiction over some portion of financial data access. 

There are, of course, also State regulatory authorities. Treasury 

has called for all agencies to align behind its interpretation of 

Dodd–Frank Section 1033 as an important step toward a level 

playing field and one that could be hastened by congressional engagement. 

Number three, the Bureau should work with the private sector 

to develop best practices on disclosures and terms and conditions 

regarding consumers’ use of products and services. 

The United Kingdom’s Open Banking architecture includes prescriptive consent flows that ensure that a consumer’s or a small 

business’ experience granting or revoking consent to access their 

data to any third party is uniform. These open banking consent 

standards are an excellent starting point for creating best practices 

in the U.S. market. 

Number four, a solution must address resolution of liability for 

data access. The CFDR earlier this year released a set of principles, Secure Open Data Access, or SODA, which called for 

traceability, minimum cyberliability insurance standards, and 

other standards designed to ensure that the entity responsible for 

consumer financial loss as a result of a breach—be it a bank, an 

aggregator, or a FinTech firm—is the entity charged with making 

the end user whole for direct losses resulting from that breach. 

While CFDR members are implementing these principles, regulatory agencies and Treasury could augment and assist this work 

by undertaking efforts to create a more vibrant and affordable 

cyberliability insurance market. 

Number five, address the standardization of data elements as 

part of improving consumers’ access to their data. While the CFDR 

Group and FDATA North America wholeheartedly agree with the 

Department’s recommendation, I would respectfully submit an addendum. The standardization of data elements should be made 

available to the consumer to permit access to third parties of their 

choosing so that all data elements available to the end user in their 

native online banking environment is also available to the third 

party if the consumer consents. This approach would fully enable 

6 

end users to leverage their own financial data to their economic 

benefit, and it would allow for the realization of a competitive, free 

marketplace in which consumers have full transparency into financial products and services offered by FinTech providers and financial services firms alike. 

Thank you again for this opportunity to testify. Though tens of 

millions of American consumers and small businesses are already 

utilizing third-party tools to improve their financial well-being, 

more can be done to harness the power of innovation safely and securely. We stand ready to work with this Committee to identify 

and implement Treasury’s recommendations. 

Thank you. 

Chairman CRAPO. Thank you, Mr. Boms. 

Mr. Rubinstein. 

STATEMENT OF STUART RUBINSTEIN, PRESIDENT, FIDELITY 

WEALTH TECHNOLOGIES, AND HEAD OF DATA AGGREGATION 

Mr. RUBINSTEIN. Thank you, Chairman Crapo, Ranking Member 

Brown, and Members of the Committee. My name is Stuart Rubinstein. I am president of Fidelity Wealth Technologies and head of 

Data Aggregation at Fidelity Investments. Fidelity is a leading provider of investment management, retirement planning, brokerage, 

and other financial services to more than 30 million individuals, institutions, and intermediaries with more than $7 trillion in assets 

under Administration. We are strong supporters of FinTechs and 

are a major FinTech investor. 

I am appearing today to represent Fidelity with a specific focus 

on the topic of financial data aggregation. At Fidelity, we have a 

unique perspective. We are an aggregator ourselves, and we are 

also a source of data to aggregators who act on behalf of our customers. 

Fidelity is a strong believer in the benefits our customers receive 

when they can see a consolidated picture of their finances through 

aggregated data. We have offered aggregation services to our customers for well over a decade, and our customers have been able 

to access their Fidelity data through various third parties since the 

1990s. But the cybersecurity environment has changed over time, 

and risks have become far more pronounced and must be addressed. 

First, most financial data aggregation that occurs today requires 

consumers to disclose their financial institution’s user name and 

password to the third-party aggregator or FinTech. While this process may have worked in the past, it is now antiquated as there are 

new technologies that eliminate any such requirement. Because cybersecurity is of paramount importance, we believe that customers 

should not have to disclose their user name and password in order 

to access any third-party service. 

Second, aggregators using credentials may have access to an entire website or mobile app, which means they can access more data 

than may be necessary to provide their services. For example, a 

simple app that tracks your spending does not need to know your 

investment holdings, but it will have access to that under the current methods. 

7 

Because of the advancement of cyberthreats, Fidelity and others 

in the industry have worked hard on developing a different approach to data aggregation that helps to protect consumers. At Fidelity, we have developed what we believe are five principles for 

empowering consumers to share their data safely with third parties. 

First, consumers should be able to access their financial account 

data where they want it and when they want it and through third 

parties if they so desire. The question becomes not if they can do 

it, but how. 

Two, access must be provided in a safe, secure, and transparent 

manner. 

Three, consumers should provide affirmative consent and directly 

instruct their financial institution to share data with specific third 

parties. 

Four, third parties should access only the financial data that 

they need to provide their product or service. This should not be 

a Trojan horse for the gathering, accumulating, and reselling of 

consumer data. 

And, five, consumers should be able to monitor those account access rights and direct their financial institution to revoke that if 

they so desire. 

In an effort to back up these words with actions, Fidelity announced in November of 2017 a new service based on these principles called ‘‘Fidelity Access.’’ Fidelity Access will allow Fidelity 

customers to provide third-party access to customer data through 

a secure connection without providing log-in credentials to any 

third party. We have also been working with policymakers and industry groups to advance these principles and are pleased that 

many have taken thoughtful approaches to this problem. 

Finally, I would be remiss if I did not mention the most difficult 

issue standing in the way of wider adoption of safer data-sharing 

technologies: the issue of responsibility. We believe companies that 

collect and handle financial data should be responsible for protecting that data and making customers whole if misuse, fraud, or 

theft occurs. As we have been discussing Fidelity Access, we have 

seen aggregators try to limit their liability, some to very small dollar amounts. Fidelity believes firms that obtain and handle consumer aggregated data should be held responsible to protect that 

data from unauthorized use just as we are. Any other standard creates moral hazard and does not incentivize aggregators to take 

their data stewardship responsibilities seriously. 

Thank you again for the opportunity to testify before you today. 

I look forward to answering your questions. 

Chairman CRAPO. Thank you, Mr. Rubinstein. 

Mr. Knight. 

STATEMENT OF BRIAN KNIGHT, DIRECTOR, INNOVATION AND 

GOVERNANCE PROGRAM, MERCATUS CENTER AT GEORGE 

MASON UNIVERSITY 

Mr. KNIGHT. Thank you, Chairman Crapo, Ranking Member 

Brown, and Members of the Committee. My name is Brian Knight, 

and I am the director of the Innovation and Governance Program 

at the Mercatus Center. 

8 

Whether it is a loan to deal with an emergency, moving money 

to a loved one in need, or capital to build a business, access to 

high-quality financial services is essential. Technological innovation in financial services, or FinTech, has the potential to significantly improve this access. 

As the Treasury Department notes, one area where technology 

may dramatically change financial services is in the collection and 

use of data. Technology advances allow financial services firms to 

obtain more data from consumers and process the data in new 

ways, with the goal of providing more accessible, inclusive, and 

cost-effective options. While it is early, there are encouraging signs 

that innovation is, in fact, helping consumers. These include innovative products giving consumers more transparency as to their finances and allowing lenders to offer potential borrowers betterquality credit through innovative underwriting. 

There is also indication that technology is making credit markets 

less discriminatory. This is promising. But there have also been 

concerns raised about potential risks to consumers, including risks 

of privacy and discrimination. These concerns should be taken seriously, and we should react appropriately. But we should be loath 

to rush into regulation without being certain that new regulation 

is necessary. 

As we assess what the Government response to technological innovation should be, we should keep a few things in mind. 

First, we should judge an innovation compared to the status quo, 

not perfection. Innovative financial service products will not be perfect, but they may be better than the alternative. Imposing unduly 

burdensome regulation that hampers innovation and competition 

may ultimately be more harmful to the very consumers that regulation seeks to protect. 

Second, we should acknowledge that existing regulations may address new risks. For example, the requirement that a lender be 

able to explain why it took an adverse action could mitigate against 

a concern that algorithmic underwriting will be unduly opaque. 

There are existing regulatory incentives as well as market incentives for companies to ensure their products are fair and appropriately transparent. 

Third, we should be open to the possibility that in some cases the 

current regulatory system is, in fact, overly burdensome. There 

may be cases where the costs of regulation now exceed the potential benefits or where a regulatory structure that made sense in the 

past has been overtaken by market developments. This does not 

mean that new regulation may not sometimes be needed, but as 

technology changes what is possible with financial services, the optimal level or type of regulation may change. 

FinTech offers exciting possibilities for better, cheaper, and more 

inclusive financial services. We should be mindful of the risks 

posed, but we should not overreact. Instead, we should work to ensure that the legal and regulatory system facilitates innovation and 

competition while preserving consumer protection so that Americans can obtain the best financial services possible. 

I look forward to our discussion, and thank you for your time. 

Chairman CRAPO. Thank you, Mr. Knight. 

Ms. Omarova. 

9 

STATEMENT OF SAULE T. OMAROVA, PROFESSOR OF LAW, 

AND DIRECTOR, JACK CLARKE PROGRAM ON LAW AND REGULATIONS OF FINANCIAL INSTITUTIONS AND MARKETS, 

CORNELL UNIVERSITY 

Ms. OMAROVA. Senators, thank you for inviting me to testify here 

today. My written testimony lays out the details of what I have to 

say, so let me focus on a few big-picture points. 

FinTech is by far the hottest topic in today’s finance. Cryptography, cloud computing, big data analytics are changing financial 

markets by making transacting faster and easier to automate and 

scale up. We have just heard arguments emphasizing the immense 

societal benefits of these changes as long as FinTech innovations 

are not stifled by outdated regulations. 

Let us put these arguments in context. It is quite symbolic that 

we are convened here today almost exactly on the tenth anniversary of Lehman Brothers’ failure that triggered the global financial 

crisis. I do not have to tell you, Senators, what a calamity that crisis was. You lived through that crisis. And for years before the crisis, you and your colleagues probably sat through many hearings 

just like this one listening to many confident and articulate gentlemen with impeccable industry credentials tell you that you should 

not let outdated regulations stifle financial innovation. They told 

you and the American public that innovative products like derivatives and subprime mortgage loans were making the financial system more efficient, resilient, and democratic by enabling better risk 

management, expanding consumer choices, and making credit 

available to low-income Americans. And so risky derivatives and 

predatory subprime loans were allowed to grow unregulated until 

they crashed the financial system 10 years ago. 

Today the same rhetoric of financial innovation and consumer 

choice that brought us the crisis of 2008 returns to the center stage 

in the policy debate on FinTech. Of course, this time it is different. 

It is not about derivatives, but about crypto assets. It is not about 

predatory subprime lending, but about marketplace lending—once 

again new technologies promising to make the system more efficient, resilient, and democratic: to expand consumer choices and to 

give low-income Americans access to financial services. 

The Treasury report adopts this rhetoric and translates it into a 

strategy of significant deregulation in the U.S. banking sector, 

meant to enable banks to form large-scale business partnerships 

and even outright corporate affiliations with technology companies. 

For example, the report advocates for a significant rollback of existing regulations in order to make it easier for the banks to give 

unaffiliated tech companies, data aggregators, cloud service providers, and various FinTech firms much more direct access to their 

customers’ account and transactional data. 

Currently banks are reluctant to allow data-mining businesses to 

get the direct feed of their depositors’ account data because regulations make banks ultimately responsible for the handling of sensitive customer information. For the same reasons of regulatory 

compliance and liability, banks are currently cautious about moving all of their data to the cloud operated by a third party. 

The Treasury characterizes this as a bottleneck in the flow of financial information and calls for a concerted regulatory effort to 

10 

push banks to share their customer data and to outsource its management to third parties much more freely. The claim here is that 

allowing unaffiliated tech companies to access, host, and manage 

bank data will make financial services faster and cheaper for all 

consumers and give consumers control over their financial affairs. 

Of course, banks will benefit from being able to reduce their 

operational and compliance costs and potentially increasing their 

revenues by charging aggregators for direct feeds of customer data. 

And consumers will get the convenience of living in a seamless virtual space where all FinTech apps can just magically connect to all 

of their bank accounts. But this will also expose consumers to tremendous risks. Imagine that your personal bank account data, 

transaction history, and other sensitive information previously 

managed by your local bank is now stored in the cloud and shared 

directly and in real time with multiple data-collecting companies. 

These companies are not regulated under a bank-like regime with 

dedicated supervisors making sure that the data is safe and secure, 

that these companies maintain strong operational controls and do 

not misuse sensitive consumer information. In this environment, it 

is easy to imagine not just one but many Equifax-style catastrophes occurring far more frequently and with far more devastating consequences. 

This is, in fact, a particular kind of a broader problem that our 

system of bank regulation has jealously guarded against since the 

19th century: the potential for excessive concentration of financial 

and market power, if banks are allowed to engage too intimately 

with nonbank commercial businesses. This separation of bank and 

commerce remains a core principle of U.S. banking law to this day. 

The Treasury report, however, calls for measures that will directly 

undermine this longstanding and sensible regime. 

What it frames as low-key technical fixes to how regulators apply 

banking laws is, in fact, opening the door to de facto FinTech conglomeration. If allowed, this new platform trust will be able to monopolize the flow of both money and information and effectively 

take control of our lives not only as economic actors but also as citizens. 

The American Republic of George Washington and Teddy Roosevelt was never meant to become a dystopic company town of this 

kind. As you are deliberating on FinTech as a public policy matter, 

I urge you to stand on guard and not let this become even a remote 

possibility. 

Thank you. 

Chairman CRAPO. Thank you, Ms. Omarova. 

I will start my questions with you, Mr. Knight. While innovations in data have brought many benefits, it has also become 

known that firms may be, in fact I think are, using this data to 

drive social policy and to restrict access to entirely legal, in fact 

sometimes constitutionally protected conduct and do this for reasons of trying to influence social policy unrelated to safety and 

soundness or other concerns that would make these targeted 

groups unfit to do business with. 

Do you think this presents a problem? 

Mr. KNIGHT. Thank you, Senator. I do, and I think it presents 

a couple of problems. The first one, to key in on the data point, is 

11 

to the extent that a financial institution is collecting data that relates to a sensitive or private matter, and particularly the more 

granular the data collection is, the potentially more harmful a 

breach would be. Information that is relatively innocuous at one 

level of detail can become extremely damaging at another level of 

detail. And, of course, depending on how much microtargeting, if 

you will, the bank is doing and the level of detail that the bank 

has stored, if that data is breached, that data is now available and 

people can be harmed more than had the data been recorded at a 

less granular level. 

The second and, I think, bigger issue that we are dealing with 

here is I think our starting point should be that a business can 

choose to do or not do business with anyone they want for whatever reason they want in a free market, and then we are going to 

narrow that for some compelling societal issues like antidiscrimination. The problem is banks are not a free market. For banks, because of public policy, there are barriers to entry; there are barriers 

to exit; there is significant subsidy. And so banks derive part of 

their market power from public power. And so when they choose 

to use their market power in an effort not to do what they have 

been charged to do, which is effectively intermediate credit or provide savings, but instead try to insist or de facto regulate the 

American people in a social policy setting, they are not using their 

market power. They are using public power. And the people who 

are on the receiving end of that do not have the same market protections that they would in a freer market. 

You know, let us take an example of YouTube, which will periodically say, ‘‘We will not cover certain types of videos for social 

policy reasons.’’ Well, you can stand up a YouTube competitor tomorrow. You do not need a Government-granted discretionary charter. And if you were to stand up a competitor to YouTube, YouTube 

does not get special access to Government Internet. It does not get 

insurance. It does not get loans from the Government. There is not 

a presumption that if YouTube is about ready to fail, the Government will bail it out, which is something that banks enjoy versus 

their nonbank competition, and that increases the ability of banks 

to throw market power around that is not derived from anything 

other than Government power. 

Chairman CRAPO. Well, thank you, and I share those concerns. 

I want to shift a little bit here, and to you, Ms. Omarova. I appreciated your testimony on some of the positive aspects that 

FinTech offers consumers. But some of the concerns that you raise 

are also concerns that I share. 

There is an article in today’s Wall Street Journal that highlights 

this intersection, and this is the title of it: ‘‘Facebook and Financial 

Firms Tussled for Years Over Access to User Data’’. This follows 

an August article in the Wall Street Journal entitled, ‘‘Facebook to 

Banks: Give Us Your Data, We Will Give You Our Users’’. The article suggests that data privacy is a sticking point in these conversations. 

Can you discuss the data privacy concerns and the need to better 

understand what kind of data is being collected and used and how 

such data is secured and protected? And I only have about a 

minute left in my time, so I—— 

12 

Ms. OMAROVA. I think this article actually highlights precisely 

what is at stake here. This is not what the Treasury report is suggesting: it is not so much about what current data aggregators do 

with data today. It is about companies like Facebook, and it just 

shows that those big tech companies, platform companies that use 

information as currency in their businesses, once they get their 

hands on the data, on the sensitive bank customers’ data, in any 

way for any reason, they will try to use that data to increase their 

revenues in a variety of spheres. And it will be extremely difficult 

to actually check how they use the data. They use proprietary algorithms to basically hide that from us. And who is going to oversee 

it? Who regulates Facebook for these kinds of issues? Nobody does. 

I am glad that Bank of America and Wells Fargo refused 

Facebook access to their bank customers’ data, but I do not kid myself for a minute that they have done it out of some kind of moral 

respect for customer privacy. They have done it because of the regulations that apply to them today. If we remove those regulations, 

then all of our sensitive financial data will be open to companies 

like Facebook and we will not know how it will be used. 

Chairman CRAPO. Well, thank you, and I share those concerns as 

well. 

Mr. Rubinstein and Mr. Boms, I am out of time, but I am not 

out of questions for you. I might have to submit them if we do not 

get another opportunity. 

Senator Brown. 

Senator BROWN. Thank you, Mr. Chairman. 

Ms. Omarova, thank you for mentioning the tenth anniversary. 

There is, as I remind many of my colleagues here, a bit of collective 

amnesia on this dais and in this Senate, and thank you for always 

reminding me of that. 

I have three questions I would like to get through, and I am 

going to start with you, Ms. Omarova, and if you would give answers as close to yes or no as you can, I will start with her on each 

of the questions and move from my right to my left. 

The Treasury Department and much of the financial industry 

argue that consumers should have the right to share their financial 

data with any third party of their choosing. Do you think this 

should include the right for consumers to require that a FinTech 

or a data aggregator erase all information at that consumer’s request? 

Ms. OMAROVA. Yes, absolutely. And, you know, we have to keep 

in mind, though, that this rhetoric of consumer choice and consumer’s right to share the information also implies the firm’s right 

to share their information, and that is what we need to guard 

against. 

Senator BROWN. Mr. Knight. 

Mr. KNIGHT. Yes, subject to reasonable considerations like law 

enforcement. 

Senator BROWN. OK. Mr. Rubinstein. 

Mr. RUBINSTEIN. Yes, absolutely. Consumers should understand 

why they are sharing their data, and share it for a specific purpose. 

When they no longer have that purpose, they should be able to stop 

sharing it and have it deleted. 

Senator BROWN. Mr. Boms. 

13 

Mr. BOMS. Agreed, subject to applicable regulations and laws. 

Senator BROWN. Thanks. 

Ms. Omarova, it is hard for consumer to understand all the ways 

that financial data might be used by a company they share it with. 

Should there be legal limits on how aggregators use the consumer’s 

financial information in addition to consumer identified limits? 

Ms. OMAROVA. Yes, absolutely. Basically, data aggregators and 

other data platform companies like Facebook should not be allowed 

to engage in a form of ‘‘insider trading’’ once they get access to customer data in one context so they could use it another context. 

Senator BROWN. Mr. Knight, legal limitations? 

Mr. KNIGHT. I believe the limitations should revolve around disclosure and the fact that any consent is knowingly given and the 

consumer has rights to terminate that consent at any time. 

Senator BROWN. Mr. Rubinstein. 

Mr. RUBINSTEIN. Yes, I would agree with that. I think really 

under a disclosure with explicit consent so the consumer knows 

what they are getting into, really understands it, and can control 

it. I do not know that we need a specific legal limitation, though. 

Senator BROWN. Mr. Boms. 

Mr. BOMS. I would echo what the past gentleman said with the 

additional addendum, which is we as an industry, not just FinTech 

but the financial industry, can and should do a lot better on conspicuous disclosures. 

Senator BROWN. OK. So you are saying legal limits. You are saying disclosure should be the emphasis. 

Last question. Companies like Google and Facebook collect enormous amounts of personal information. They also influence what 

information consumers are exposed to. For example, Facebook 

might show payday loan advertisements to servicemembers or to 

minorities, but not its other users. Should fair lending laws be updated to cover not just providing credit products but also their targeted advertisements on social media platforms? Ms. Omarova. 

Ms. OMAROVA. Yes, absolutely. Algorithmic opacity raises a new 

spectrum of discrimination concerns, and we have to guard against 

that. 

Senator BROWN. Mr. Knight. 

Mr. KNIGHT. Senator, that is a great question, and I do not know 

if I can give you an answer in the time limit you would want. If 

you would like to submit a QFR, I am happy to answer it. 

Senator BROWN. I will do that. Thank you. 

Mr. Rubinstein. 

Mr. RUBINSTEIN. Senator, I am sorry. I am not an expert in fair 

lending, and I probably cannot respond to that question. 

Senator BROWN. Could I still send a letter to you and have people at Fidelity answer it? 

Mr. RUBINSTEIN. You can send the letter. We can try. We are not 

lenders, so I do not know that we would have a good answer on 

that one for you. 

Senator BROWN. OK. Mr. Boms. 

Mr. BOMS. Senator, I would echo, I would be happy to respond 

in writing. It is not smuggling that we have discussed with our 

members. 

Senator BROWN. OK. 

14 

Fourth question. Thanks for your promptness, all of you. The biggest four banks control about 45 percent of bank assets. According 

to your testimony, Facebook and Google together capture between 

59 and 73 percent of the online advertising revenue in the U.S. Do 

you think the Treasury report’s recommendation, which many of 

you have cited, favorably would benefit the large incumbents or 

would increase competition? Ms. Omarova. 

Ms. OMAROVA. Well, the increase in competition is another good 

rhetorical choice to, you know, promote deregulation. But, in reality, both the financial sector and the tech sector are the businesses where economies of scale and economies of scope are extremely important. So in reality, what the Treasury report wants 

us to have is the maximum scale and maximum scope of these conglomerates. 

Senator BROWN. So it would benefit the larger—— 

Ms. OMAROVA. It would benefit the large incumbents. 

Senator BROWN. Mr. Knight. 

Mr. KNIGHT. Senator, I believe that it would actually be potentially a mixed benefit. In some cases the largest companies would 

benefit; in some cases the ability of smaller financial institutions 

to plug into large data providers may allow them to compete with 

larger financial services companies. 

Senator BROWN. Mr. Rubinstein. 

Mr. RUBINSTEIN. Yes, Senator, the Treasury report refers to 

APIs, which is tech speak for more secure data-sharing methods. 

I do believe that they actually increase competition. With respect 

to standards, small companies only need to build to one API standard to plug into many interfaces, so, yes, I do think it helps competition. 

Senator BROWN. It would certainly be working against trends, 

but, Mr. Boms. 

Mr. BOMS. And, Senator, I would just say on behalf of many 

smaller financial technology firms, not the Facebooks or Googles of 

the world, there is a very strong view that this would promote competition. 

Senator BROWN. So the smaller guys think it would promote competition? 

Mr. BOMS. Yes, that is correct. 

Senator BROWN. Thank you. 

Chairman CRAPO. Senator Rounds. 

Senator ROUNDS. Thank you, Mr. Chairman. First of all, thank 

you all for being here today. 

One of the common threads that I have noted throughout each 

of your testimonies was the importance of data breach or data security in FinTech. I am really curious about the issue of the importance of or the challenges of a national data breach standard. 

A number of businesses and trade associations have called for 

Congress and the Federal Government to step in and to establish 

one unified data breach standard so businesses could operate 

across State lines; they would not be forced to comply with a patchwork of different regulations. In addition, my colleague in the 

House, Congressman Blaine Luetkemeyer, recently released the 

Consumer Information Notification Requirement Act. This legislation, which has passed the House Financial Services Committee, 

15 

would require Federal regulators to establish a national unified 

data breach standard. 

On the other hand, 31 State Attorneys General have released a 

letter opposing a prior version of a data breach bill in the House 

because it would preempt State laws. 

I would like your thoughts, first of all, on what we are discussing 

right now coming out of the House. And, second of all, is a national 

standard necessary? And if so, how do we balance that with State 

interests? Who would like to begin? 

Ms. OMAROVA. Let me take this on. I think, as a general matter, 

just because a particular standard is unified, universally applied, 

and easier to understand does not necessarily make it the better 

standard. It depends on what the standard is, qualitatively. 

We have the Federal system of regulation in this country because 

we believe in the checks and balances. Sometimes State consumer 

protection laws have to step in more effectively to protect us consumers from abuse by large companies. And sometimes the Federal 

laws do a better job by basically, you know, creating an even playing field for everybody else. 

So, my response to that would be it is not necessarily a bad idea 

to have a unified standard, but the key to that would be that that 

standard creates the maximum protection for the customer’s financial data from various abuses that would likely ensue if we take 

State authorities completely out of the game. 

Senator ROUNDS. Thank you. 

Other thoughts? 

Mr. RUBINSTEIN. I am happy to respond, Senator. 

Senator ROUNDS. Please. 

Mr. RUBINSTEIN. Thank you for the question. We do support a 

Federal breach notification. While a large firm like ours can stay 

on top of the various State laws, speed is often very necessary in 

a breach notification. Being able to understand one law and being 

able to respond quickly to that I think enhances consumer protection, and gets customers and regulators just notified faster. 

Senator ROUNDS. Other thoughts? 

Mr. BOMS. Senator, if I may, I would just add I think certainly 

you would find broad support within the FinTech ecosystem for a 

national standard, provided that it was strong enough and provided 

the right consumer protections. 

Just to juxtapose that with the ecosystem that we have today, it 

is very inconsistent from a regulatory perspective. We have CFDR 

members who are, for example, FFIEC supervised and examined as 

third-party vendors to large financial institutions. We have other 

FinTechs who are State regulated, and so who are not subject to 

the prudential bank regulatory oversight. And so one standard that 

encapsulates best practices I think would be welcomed. 

Mr. KNIGHT. Senator, I cannot speak to Representative 

Luetkemeyer’s bill specifically, but I would also say that when assessing whether or not a Federal standard makes sense, some 

other things to think about are whether or not the patchwork of 

regulations is generating inefficiency that ends up costing consumers money; whether or not there is a disparate treatment 

among competitors, so some people get to leverage one standard, 

some people get to leverage a different standard;, and third, whethVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00019 Fmt 6633 Sfmt 6633 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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er or not we are seeing citizens being de facto regulated by other 

States to a significant degree because, of course, you know, if you 

are a national player, you are going to comply with California even 

if someone in Wisconsin maybe would not support that standard. 

One of the potential advantages of a Federal standard is that 

there is broader political representation in setting it and everyone 

gets a seat at the table, even if you do not end up winning. 

Senator ROUNDS. Is there a process today where a lot of these 

States that have individual offices, in particular Attorneys General 

offices and consumer offices, to where they have—do they have an 

association, so to speak, where they can speak with a unified voice 

in terms of what should be part of a core of a national standard 

that you have worked with? 

Mr. KNIGHT. Well, I have not worked with them on this topic, but 

the National Association of Attorneys General may be a place to go. 

They do work together both on advocacy and on enforcement 

through multi-State enforcement actions. 

Senator ROUNDS. Any of you worked with any one of your associations? No? OK. Thank you. 

Thank you, Mr. Chairman. 

Chairman CRAPO. Thank you, Senator Rounds. 

Senator Reed. 

Senator REED. Well, thank you, Mr. Chairman. And thank you 

for your excellent testimony. 

Mr. Rubinstein, thank you. Very thoughtful comments. We appreciate it. You point out in your written testimony that there are 

significant benefits, but there are also, as you say, very real cybersecurity and privacy risks. Can you project or let us know what 

your fears are about sort of the big problems that are out there 

lurking? 

Mr. RUBINSTEIN. Senator, thank you for the question. Number 

one is the issue of credential sharing, people giving away their IDs 

and passwords. Today when FinTechs or aggregators show up at 

our front door, they log in typically with robotic activity. It is robots that impersonate the customer, basically, same as you sitting 

at your keyboard typing in your ID and password. That only gives 

access to data, and some of that data may be private which you did 

not intend to share. But it also can give access to transactions. If 

you think about that, what does that mean? It means that potentially a robot can come in and move your money to somewhere else. 

That is a risk from having just open access to the website, which 

the current methods have. 

It is difficult for a financial institution to know that that is a 

robot coming in because it looks just like a customer. It is also difficult for the customer then to come back later and say, ‘‘I did not 

authorize that activity,’’ when, in fact, they actually gave their ID 

and password to a third party. Those are real risks that we think 

about each and every day. 

Senator REED. Thank you very much. 

The other aspect of this is that we are at the beginning of a huge 

wave. Eventually the aggregation of data will go way beyond just 

sharing financial information from an institution with customers of 

a place like Facebook. It will go to all the information they collect: 

what websites you are looking at, maybe what potential pharmaVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00020 Fmt 6633 Sfmt 6633 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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ceuticals you are ordering, et cetera. The financial decisions that 

are being made may not be being made by even individual human 

beings, and they might not be made in the financial institutions. 

It will be a machine that is sharing all this data. Is that something 

that you are concerned about? 

Mr. RUBINSTEIN. I think there are great concerns with data that 

flows without the customer’s knowledge and affirmative consent. So 

I think, you know, all that comes in. 

However, we do firmly believe in the customer’s right to share 

their data. It is their data. If they understand that it is being 

shared, understand how it is being used, frankly, if they want to 

participate in selling that data, let them participate. Hopefully they 

will get rewarded for that. But they should be able to turn it off 

at any time, too. 

Senator REED. So in one concept there is the notion that—and 

I think we have said it before—there has to be an opt-in and optout, not just a generic one when you sign up, but constantly as the 

situation changes; that if there is value in your data, then somehow the customer should be able to realize that value, or at least 

make the decision based upon, you know, I am giving something 

up or I am getting something. And then the notion of erasing data 

is critical. Do you agree? 

Mr. RUBINSTEIN. Yes, Senator. Take Fidelity Access, as I mentioned earlier in my comments, as an example. When we use that, 

a customer can actually have a dashboard that they can see which 

third parties they have granted access to their data, so they can 

monitor that on an ongoing basis and with a single click be able 

to revoke that consent. 

Now, that only works—and many financial institutions are building similar things. That only works on the financial institution 

side. Once a consumer shares their data with a third party, we do 

believe that they should be able to get that erased. But that is actually between the third party and the consumer. 

Senator REED. That is where we have to step in and provide 

some type of sensible rule so they can do that. Correct? 

Mr. RUBINSTEIN. I think so, yes. 

Senator REED. Ms. Omarova, in this deregulatory climate, which 

more and more is going to be left to the market, isn’t that an argument for giving people the right to go to court if they feel aggrieved, even more so than today, giving people a private right of 

action if they feel aggrieved? 

Ms. OMAROVA. I suppose so. I think in general, because of the 

complexity of the environment with which we are dealing today 

and because of the complexity of understanding exactly what kind 

of personal data is available to whom and how it could be used and 

the difficulty of monitoring all of that use, I think absolutely every 

lever of control over the use of that data by the big tech companies, 

especially, should be utilized. 

Senator REED. Thank you very much. 

Thank you, Mr. Chairman. 

Chairman CRAPO. Senator Perdue. 

Senator PERDUE. Thank you, Mr. Chairman. 

One of the unintended consequences of the Dodd–Frank law was 

I think it spawned probably—and it is arguable—the greatest peVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00021 Fmt 6633 Sfmt 6633 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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riod of bank consolidation in U.S. history. We have lost 1,700 banks 

in the last decade, and virtually no new banks have been started. 

So I have got a question. 

In that environment, Dr. Omarova, you mentioned earlier—I 

have a question for Mr. Knight first, but I want to come back to 

you on a second question. But Dr. Omarova talked about aggregation, the bigger the banks get, the more important this aggregation 

of data becomes. I am concerned that today in that environment of 

consolidation we have six examining agencies charged with consumer financial protection. One of those is the CFPB. We had the 

Acting Director before this Committee a couple months ago tell us 

there have been at least 240 breaches of data that they are investigating and possibly as many as 800. Any one of those could be 

worse than the Equifax breach. 

So the question I have, as we talk about—Mr. Knight, you talk 

about accessing this data can help banks actually improve services, 

particularly for people who are underserved today, and I agree with 

that. But this unified national data security standard, as we are 

talking about, breach notification that I think we all agree on, how 

would that apply in your mind to these Federal examining agencies 

that have access to this same data? 

Mr. KNIGHT. I apologize. If I understand your question, is the 

concern that there is going to be a breach at the agency level? 

Senator PERDUE. Yeah, we have already been told—there are 240 

CFPB known breaches today, 800 they are investigating, any one 

of which could be worse than the Equifax breach. 

Mr. KNIGHT. I absolutely share that concern, and I think that the 

challenge is if you allow any entity to access data, be it the bank 

or be it a Federal agency, there is that risk. And I think that while 

there are concerns and tools available to punish banks in the case 

of a breach or Equifax in the case of a breach—and we can debate 

whether or not those tools are adequate—it is harder in many respects to go after an agency due to issues like sovereign immunity. 

Senator PERDUE. But should they be held to the same standard 

of data protection that commercial interests are? 

Mr. KNIGHT. At least the same standard, Senator. 

Senator PERDUE. Thank you. 

Dr. Omarova, I have a question about where the United States 

sits with our regulatory environment relative to other countries. In 

Kenya, for example, 93 percent of Kenyans have access to a bank 

account through M–PESA, a mobile phone-based money transfer 

and microfinancing service in China. Alibaba—I was on a visit with 

Alibaba and Tencent a couple months ago in China. They help facilitate $12.8 trillion in mobile payments in China. They have 

leapfrogged us and our technology here. No matter what we think 

of our FinTech, a lot of these innovations were developed here, but 

we are slow adopters somehow in the United States. Are we falling 

behind places like the U.K., Kenya, and China in terms of the 

adoption of this technology and FinTech? 

Ms. OMAROVA. Well, Kenya is very different, has a very different 

financial services market than we do here. They do not have an actual banking system. 

Senator PERDUE. But the U.K. is very similar. 

19 

Ms. OMAROVA. I will get to that in a second. And in Kenya, by 

the way, the success of their mobile banking was built on the central bank and the major telephonic provider banding together. So 

the State was critical to providing the service to everybody else. 

China, yes, China has Alibaba, which is competing with our, you 

know, PayPals and Facebooks and what have you. Again, in China, 

the State apparatus is so strong that China can control whatever 

those companies do, and that is a critical factor. 

The U.K., we always hold up the U.K., especially the industry 

does, as this sort of principles-based, much more market friendly, 

much smarter kind of regulator type environment. But, remember, 

before the crisis, I worked in the Treasury, and we were doing reports about how the Financial Services Authority was so much better than our regulators were in terms of allowing financial innovation to go forward. And then the crisis hit. Where is the Financial 

Services Authority now? I am not so sure that the Open Banking 

Initiative in the U.K. is actually achieving the benefits that it was 

promising. 

So I think what we should look for is not so much how, you 

know, industry-friendly or deregulatory a particular country’s environment is. I think we should look at our market structure and the 

concentrations of power in the tech industry and the financial sector in our country. 

Senator PERDUE. And that is my question. I have to gauge this 

against other standards and other performances, and so are we 

falling behind the adoption of these technologies relative to consumer protection and consumer access to banking services? And I 

would welcome anybody’s response to that. 

Ms. OMAROVA. I do not think we are falling behind. I think we 

are taking a more cautious approach simply because we have probably much more to lose. 

Senator PERDUE. Very good. Anybody else? 

Mr. BOMS. Senator, I would just add we should not discount the 

vibrancy and resilience of the U.S. market, which obviously stands 

way above other markets. 

That said, the lack of consistency and clarity in the regulatory 

and legal framework in the U.S. with regard to data access presents a potential future competitive risk for the U.S. market. 

Senator PERDUE. Thank you very much. 

Thank you, Mr. Chairman. 

Chairman CRAPO. Thank you. 

Senator Warner. 

Senator WARNER. Thank you, Mr. Chairman. 

I want to follow up where Senator Perdue was at, Mr. Boms, 

with what the Europeans are doing, with what the Brits are doing. 

How does this affect, again, our market’s ability to stay competitive 

in what is obviously a global field? 

Mr. BOMS. Sure, Senator. It is very early days. PSD2 and Open 

Banking in Europe and the U.K. just went live on the 13th of January this year. There was a conformance period that will last until 

September of next year. So we are in this transition period. But we 

are seeing adoption of Open Banking APIs by consumers in the 

U.K., for example, increase 50 percent month over month. So, 

clearly, there is interest in adoption of these tools. 

20 

We are seeing significant investment into the FinTech market in 

London. It is not because the cost of living or taxes are low. It is 

because there is a clear regulatory framework and a legal framework for how these tools can be deployed, proscriptive consent and 

disclosure flows that consumers have come to expect and are aware 

of. 

So I do not think it is an imminent threat, but I do think if we 

do not get our house in order in the relative near term, it could become a threat. 

Senator WARNER. One of the things I—and related to this, while 

not the direct topic today, you know, there is a group of us, bipartisan, that have been working for now 31⁄2, 4 years to try to at least 

standardize data breach legislation. The fact that we have got 49 

or 47 different data breach legislative laws—this is different than 

data portability, but I would hope you would think that some level 

of Federal leadership on data breach would be important as well. 

Mr. BOMS. Absolutely, Senator, so long as the floor that it establishes provides sufficient consumer protection. 

Senator WARNER. Right, and that is, I think, what we have done. 

Frankly, it has been some of—I was from the telecom business before. It is my old industry that has been some—everybody is for 

data breach legislation, but then they all want a carveout for their 

specific industry, and that is not going to end up being, I think, the 

way we get there. Unfortunately, those efforts have lagged a little 

bit in the last 8 or 9 months, and I think as we think about this, 

we have got to think holistically. And, Ms. Omarova, that is where 

I want to go to my question with you. I am a big advocate around 

data portability, and I think Senator Brown may have indirectly 

raised this question already. In my efforts on the Intelligence Committee, where we are looking at the social media firms who have 

these platforms, who have enormous, enormous power and growing 

power, if we deal with data portability in the FinTech space alone 

but do not deal with data portability in terms of our individual personal data, if we are not able to move from Facebook to another 

enterprise and make it easy and allow our cat videos to move easily 

as well, we are really not going to be able to have the type of competitive market, I think, in that field. 

I would just like you to comment on the need to not only get this 

right in the FinTech, in the financial arena, but more broadly 

based. 

Ms. OMAROVA. You are absolutely correct. Information is the currency in the digital economy, and, you know, it takes many forms 

and it flows through many, many markets for many, many goods 

and services, not just financial markets but markets for other types 

of data. And it is a structural problem. I understand the concerns 

with competitiveness, and I am completely in favor of allowing consumers to move freely between different apps and utilize various 

information in ways that serve their interests. But the problem 

here is that you have to understand that, structurally speaking, financial institutions are sitting on the type of information that presents, you know, a much heightened danger of misuse, and this is 

where we should be particularly careful with respect to FinTech 

and how the financial information is moving structurally in these 

markets and probably deal with the broader issues of data protecVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00024 Fmt 6633 Sfmt 6633 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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tion outside the financial sector and perhaps antitrust issues as 

well, because those are serious structural issues that exist everywhere in the big tech sector separately. 

Senator WARNER. My concern is that what—and this Committee 

has looked in terms of Russia sanctions, what happened in 2016, 

where Russia intervened, but what I see as the next iteration is 

that someone will come in and break into nonprotected personal financial data, as they did with Equifax, and Senator Warren and 

I have a bill, and it is, I think, a travesty that we are a year later 

and there still has been no penalty paid by that company. But they 

will break in, get personal information, contact any of us as an individual, and then what will pop up with be what is called a ‘‘fake 

video,’’ and it will be somebody that looks like Senator Brown, but 

it is not actually Senator Brown live stream video. And the combination to wreak havoc there not only on the political side but on 

the market side is really huge, so we have to solve this issue not 

just for financial data portability but across the board. 

Ms. OMAROVA. Oh, that is absolutely correct. That is absolutely 

correct. 

Senator WARNER. Thank you. 

Chairman CRAPO. Thank you. 

Senator Cortez Masto. 

Senator CORTEZ MASTO. Thank you, and thank you, Mr. Chair 

and Ranking Member. Obviously, this is an important discussion, 

and thank you all for being here today. It is a great conversation. 

I echo my colleague Senator Warner. I think we have to look at 

this in a holistic approach. I think what I have heard today, we all 

agree we have got to address the data privacy, security, and consumer protection piece of this, but this is emerging technology. It 

is not going away, and we are going to have to figure out at a Federal level how we address this, but also, I believe, incorporating 

State laws in the States as well. They have to be a part of this discussion. 

So let me ask you this, because we received a letter from the National Association of federally Insured Credit Unions, the Committee did. One statement the association makes is that, ‘‘As new 

companies emerge and compete in this area, it is important that 

they compete on a level playing field of regulation, from data security to consumer protection.’’ Would each of you agree with that 

statement? 

Mr. BOMS. Senator, yes. 

Mr. RUBINSTEIN. Yes, absolutely. Whoever holds consumer data 

should be held to the same standards. 

Mr. KNIGHT. Yes. 

Senator CORTEZ MASTO. Thank you. 

Ms. OMAROVA. Well, yes, it is generally a good principle. 

Senator CORTEZ MASTO. And that level playing field of regulation 

does not mean that we roll back regulation, does it? 

Mr. BOMS. Senator, from my perspective, no, it does not. It 

means that we make the regulation consistent across the various 

regulators who have some stake in this. 

Senator CORTEZ MASTO. Thank you. 

Mr. RUBINSTEIN. Yes, I would agree. 

Senator CORTEZ MASTO. Right. And I think you would all agree. 

22 

Mr. KNIGHT. Senator, I would say that when we talk about level 

playing field, we should be thinking about what is the risk that is 

generated that we are trying to regulate against, and so if that risk 

exists, comparable regulation should exist. If a new player comes 

along and offers a comparable service but does not generate a certain risk, then they should not be regulated in the same way visa-vis that risk. For example, a lender that does not fund their loans 

from federally insured deposits should not be regulated as a depository because they are just not generating the risks that go along 

with the deposit holding. They should be regulated vis-a-vis consumer protection in lending, for example. 

Senator CORTEZ MASTO. OK. 

Ms. OMAROVA. Well, sometimes it is very difficult to figure out 

exactly what types of risks a particular lender or a particularly institution really poses. Sometimes we do not see how exactly they 

fund their loans and their services. We have learned that from this 

last crisis. And I think that in that sense, it is important that, if 

we are looking for leveling the playing field, we have to make sure 

that that common level is not the minimum regulatory level of 

oversight but the maximum one. And when we are looking at the 

maximum level of regulatory oversight in the interest of the American public, we should keep in mind the biggest players in those 

markets, not the smallest ones. 

Senator CORTEZ MASTO. Thank you. And can I ask you, each one 

of you, when we are talking about banks and credit unions that 

allow data aggregators access to bank customers’ accounts, if there 

is a violation of those customers’ privacy information and that privacy information for those customers, who should be legally liable? 

Should the banks and credit unions be legally liable if they are 

working with those third-party aggregators and there is a breach? 

Mr. BOMS. Senator, you have identified, I think, perhaps the 

largest, most significant obstacle in this ecosystem, which Mr. Rubinstein referenced in his opening statement. The members that I 

represent would say that he who breaches the data should be responsible for making the consumer whole. 

The catch to that and the issue with that is we have decades of 

regulation and consumer expectations that say that it is the financial institution that either should or must make the consumer 

whole. So on some level, even though our members have taken it 

upon themselves, are adopting this notion of he who breaches must 

make the consumer responsible, at some point we need to holistically take a look at the regulations that we have on the books and 

modernize them for the 21st century economy. 

Senator CORTEZ MASTO. OK. Anyone else? 

Mr. RUBINSTEIN. Senator, as Mr. Boms said, it is a very difficult 

topic, and we firmly believe that whoever causes harm to the consumer should make the consumer whole. 

Unfortunately, this is a chain. Consumer data starts at the financial institution. It moves to a financial data aggregator. Then 

it moves to a FinTech. It may continue to move beyond that. 

The financial institution only has a direct relationship in that 

first step of the chain with the financial aggregator. They need to 

look to that financial aggregator to make the financial institution 

whole if the financial institution has reimbursed the consumer and 

23 

then they can deal with their own customer. Similar to getting into 

a car accident, right? You have auto insurance. You turn to your 

insurance company, and then your insurance company goes and 

subrogates with the others down the chain. It has been very difficult. The industry is not adopting that yet, and we can use a 

nudge in that direction. 

Senator CORTEZ MASTO. Thank you. Please, whoever would like 

to go next. 

Ms. OMAROVA. I think that everybody in that chain should bear 

a responsibility and be exposed to the liability for data breaches of 

bank customer data. And what concerns me about the Treasury report in particular is that it never really addresses that issue directly, and it talks about, yes, we need to kind of have an appropriate liability regime, but it is not clear to me what that regime 

will be like. 

What I know, though, is as a practical matter, in order to 

incentivize banks to share their information, their bank customer 

information, with various technology companies, you are going to 

have to relax the actual liability constraints existing today on 

them, because, otherwise, they simply would not share it. So that 

is what concerns me a lot. 

Senator CORTEZ MASTO. Thank you. And I know I am out of 

time, Mr. Chair. I do not know, Mr. Knight, if you wanted to say 

a few words—I do not want to take up any more time. 

Chairman CRAPO. Briefly. 

Senator CORTEZ MASTO. Thank you. 

Mr. KNIGHT. So in addition to all that has been said, I would say 

that one threshold question we need to talk about is that Treasury 

takes the position in the report that Dodd–Frank Section 1033 

compels the bank to make the information available to the consumer’s chosen aggregator. I do not know if that is the position the 

Bureau will take, and if we are compelling the bank, then the normative argument for holding the bank liable if some accident happens down the chain with an aggregator they did not choose to 

partner with but were compelled to partner with weakens; whereas, if it is a matter of choice all the way down, then the principles 

discussed make more sense. 

Chairman CRAPO. Senator Scott. 

Senator SCOTT. Thank you, Mr. Chairman. Thank you to the 

panel for investing the time to be here this morning. 

Things get complicated when a company is headquartered in 

Tennessee, does business in South Carolina, and is breached in Arkansas. Those States all have different laws on the books governing 

when and how companies must notify the public of a data breach. 

The reality is that a patchwork quilt of 50 different breach notification standards creates a race to the bottom in which breached 

parties will often comply with the lowest possible standard. Consumers are ultimately the ones that pay the price. They are the 

ones that lose out. 

I know that Senator Rounds touched on this question earlier, but 

let me ask you, Mr. Boms, is a State-by-State framework for breach 

notification effective? Who stands to benefit from a more uniformed 

approach? 

24 

Mr. BOMS. Senator, we think that there is certainly room for improvement. A Federal approach that lifts up what the ceiling is 

across the board would benefit consumers, it would benefit the industry. We think it would be a win–win for everybody involved. 

It is not simply an issue of regulatory complexity at the State 

level. Several of the FinTech firms that I work with have Federal 

supervision through third-party vendor risk management, and so 

there is a piece of prudential bank regulatory authority here as 

well on this score. This is another area where consistency among 

regulation, not deregulation, would be immensely helpful. 

Senator SCOTT. Thank you, sir. 

The Gramm–Leach–Bliley Act from 1999, we did business very 

differently then. I think we were all still using paper for most of 

our transactions. We probably had dial-up for our Internet connection, and we certainly did not have cell phones that could do anything other than call, and that was a pretty expensive venture as 

well. 

The bottom line is that the world has changed so significantly 

since GLBA was enforced, became law, but it is still the foundation 

of how we govern data aggregators for financial institutions. I am 

encouraged by the fact that we are moving to APIs from screen 

scraping, but it is happening fairly slowly. 

Mr. Boms, you mentioned Europe, Mexico, and Japan in your testimony. How are U.S. policymakers falling behind in crafting laws 

that foster FinTech innovation and protecting consumer data? 

Mr. BOMS. Senator, I would answer in two parts. I think the first 

thing I would say is APIs in and of themselves are not a panacea. 

They will not solve everything. The API, in addition to being secure, as we have heard, also must be robust. So the API must include data fields like fees, for example, so that a consumer who is 

using a third-party tool that compares fees at one, for example, financial institution can compare what its fees would be for the same 

products or services at another financial institution. So making 

sure that the APIs with the direct feeds are robust is the first step. 

The second is there are no standards in the U.S. market. The 

Treasury report talks about data standardization, which we think 

is a very important area that other markets have addressed. In the 

U.K. open banking environment, the data elements are standardized. The Mexican central bank and securities regulator are currently working on an API that would standardize the data sets. 

This would be, we think, one place to start, but there are quite a 

few that regulators here could begin with. 

Senator SCOTT. Thank you. 

Almost 30 percent of Americans living in economically distressed 

communities are credit-invisible, meaning they have no credit 

score. An additional 15 percent are unscorable due to having an insufficient or old credit history. In South Carolina, that combined 

number is about 23 percent, or one out of every four adults. 

Senator Cortez Masto and I have worked diligently to find ways 

to bring that credit-invisible person to a place where their consistent habits of paying their bills, whether it is their electric bill 

or their cell phone or the rent from a place that they are renting, 

if they are paying those on time, they should get some credit for 

that. 

25 

Mr. Knight, you testified that innovative underwriting can provide consumers with benefits such as lower interest rates. Can you 

speak to the benefits of using rent and utility payments in credit 

scoring and to other developments in underwriting that will benefit 

consumers? 

Mr. KNIGHT. Thank you, Senator. Yes, I think that expanding access to the types of data that bear on the creditworthiness of a borrower, even if they have not traditionally been captured in traditional underwriting like a FICO score, has the potential to be valuable in allowing lenders to make an accurate assessment of the risk 

that they would take on by lending to a borrower. In some cases, 

that will make someone who is credit-invisible visible and, therefore, the lender has enough data they feel like they could make an 

offer. 

In other cases, it will indicate that people who are, in fact, good 

credit risks or better credit risks than they otherwise get credit for, 

because you are looking at data that has not otherwise been picked 

up. So I think that there is potential value there. 

Senator SCOTT. Thank you. I have another question on my legislation, the MOBILE Act, that I will submit for the record. 

Thank you, Mr. Chairman. 

Chairman CRAPO. Thank you. 

Senator Warren. 

Senator WARREN. Thank you, Mr. Chairman. 

So FinTech holds out a lot of promise for consumers and also 

raises a number of concerns. I think it is critical that the Government move methodically on a regulatory approach to FinTech, so 

we encourage productive innovation but we do not expose consumers to a lot of unnecessary risks. 

So the Treasury Department issued a report on FinTech earlier 

this year, and in almost every instance, it advocates for deregulation in an effort to stimulate the FinTech industry. And I am concerned about a lot of those recommendations. 

One set of recommendations is about rolling back the rules that 

govern how banks can share personal financial information with 

third-party data aggregators. So, Professor Omarova, I know you 

addressed this issue in your written testimony, and I just would 

like you, if you could very briefly, to explain what your concerns 

are with the Treasury Department’s recommendations on this 

front. 

Ms. OMAROVA. So my main concern is that the Treasury’s approach will essentially open the floodgate for the banks that are 

currently regulated to open up this treasure trove of sensitive financial data on the customers that they have for much broader 

types of uses by various tech companies. So my concern is about 

Facebook, it is about Google, it is about Amazon. And we do not 

know what they do with the data they touch, so they could use it, 

they could get access to that data in one capacity, let us say as a 

cloud service provider and the code writer, but then misuse it in 

order to sell something to the customer, and that is what I worry 

about. And the customer consent here could be obtained by the 

bank at the point when the customer is actually opening a deposit 

account with the bank, and that is what concerns me. This notion 

of consent and choice could be actually diminished. 

26 

Senator WARREN. All right. That is very important. Thank you. 

You know, given what just happened with the Equifax breach, I 

think a lot of my constituents and constituents for pretty much everybody here would be uncomfortable with the idea of even more 

companies getting access to our financial data without our effective 

consent and without strict rules on how they have to protect that 

data. 

Another set of Treasury recommendations would further weaken 

the wall between banking and commerce. They would allow our 

biggest banks and our huge technology platforms to join their corporate empires—you were just talking about this—and giant technology companies like Facebook and Google to buildup equity 

stakes in multiple smaller banks across the country. 

Again, could you go back to this, Professor, and describe some of 

the potential harms in allowing this kind of consolidation across 

different industries? 

Ms. OMAROVA. Right. So the Treasury basically seeks to weaken 

how control is defined in the Bank Holding Company Act. The 

Bank Holding Company Act currently subjects any company that 

controls a U.S. bank or is affiliated with a U.S. bank to various 

regulations and supervision, and it is essentially an antitrust law 

that seeks to prevent banks from abusing their control of immense 

power over public money and credit. And what the Treasury says 

is essentially we should make it much easier for the banks to acquire equity stakes in tech companies and vice versa. And I worry 

about the fact that it will not create greater competition; it will actually lead to extreme concentrations of power over money and information across the sectors. And it will take the ‘‘too big to fail’’ 

problem to an unprecedented level because in the next crisis we 

may have to save Facebook and Amazon because they would be so 

intertwined in the financial sector. 

Senator WARREN. So, actually, this is powerfully important, and 

I appreciate your comments on this. You know, a lot of discussion 

in FinTech centers on the consumer to corporate part of this, but 

there is also the part about the effect it would have on wholesale 

banking. Can you just say a word more about that? You have 

talked about blowing up ‘‘too big to fail.’’ Just a bit more. 

Ms. OMAROVA. So remember with subprime mortgages, for example, it was also—the rhetoric was all about the right of the consumer to choose to take a very expensive loan, for example, but in 

reality, those mortgages were the fuel for the wholesale market 

speculation. And so I worry that allowing digitization of data and 

all of this sort of new FinTech innovation without proper controls 

will actually increase the potential for wholesale market speculation in the secondary markets that would make the system more 

volatile and more unstable, and we have to be aware of that danger. 

Senator WARREN. Good. Thank you very much. You know, I know 

there is a lot that improving technology can do to reduce costs and 

improve service for customers. But I am concerned that this Treasury report consistently ignores real concerns that could arise both 

for consumers and for the industry and change the—have an impact on protecting data, on reducing consumer choices, on maintaining safety in the financial system. 

27 

So thank you very much, Mr. Chairman, for holding this hearing. 

I hope we will continue to dig into this issue. Thank you. 

Chairman CRAPO. We definitely will. And I think there is lot of 

bipartisan agreement on a lot of these issues. 

I need to wrap up the hearing. However, Senator Brown has 

asked for one more round of 5 minutes. 

Senator BROWN. I have a couple questions. Thanks. 

Chairman CRAPO. Senator Brown, I will grant that to you, and 

I am sorry, then I am going to have to wrap the hearing up. 

Senator BROWN. Mr. Chairman, thank you. We have had sort of 

private discussions about overlap and the common interests we see 

in some of this on privacy, and I am hopeful that we can come together on some things. 

I have a couple questions left. Professor Omarova and Mr. 

Knight, if I could direct the first one to you, starting with you, Professor Omarova. Should a nonfinancial company be allowed access 

to consumers’ detailed financial data such as transactions or account balances? Or should the traditional separation of banking 

and commerce extend to data sharing as well? 

Ms. OMAROVA. I absolutely think that the traditional separation 

of banking and commerce should extend to everything that relates 

to data. I do not think that pure disclosure really cures the problem because the problem is structural. The problem is about the 

market power crossing over different sectors and essentially hurting all of us and the long-term competitiveness of our economy. 

Senator BROWN. Thank you. 

Mr. Knight, any comments on that? 

Mr. KNIGHT. So I am somewhat more optimistic. I think that 

there may be circumstances where allowing that sort of exchange 

can actually be beneficial to the consumer. I do think that meaningful disclosure, meaningful acceptance is critical to this, because 

we are talking about very sensitive information, and if the consumer is allowing that information to be shared, it should be used 

only for the purposes that the consumer has granted access to, and 

that consent should be periodically reacquired. It should not be 

something that you click ‘‘yes’’ on a splash screen when you first 

sign up and then never hear about it again. But I do think that 

there may be scenarios where that exchange actually is worth it. 

Senator BROWN. Thank you. 

And the last question to Mr. Boms, and thank you, Mr. Chairman. What would be the impact of a successful hack of one of your 

members? 

Mr. BOMS. Senator, it would depend on which of the members we 

are talking about. So if I could, I will separate them from the 

aggregator members and the end FinTech clients. 

For the aggregator members, there is a wide variety. They are 

mostly read-only platforms. You cannot execute transactions across 

them. While many do hold credentials as a way to get into the ecosystem, they employ best in class security systems, hardware 

encryption, elements of data security that I am not qualified to get 

into. That is not to say that more cannot be done, but, of course, 

they are not encumbered by—— 

Senator BROWN. And there have been successful hacks in the 

past, of course. 

28 

Mr. BOMS. Well, I would argue, respectfully, that the vast majority of the hacks that we see in the financial ecosystem are at the 

incumbent financial institutions, not the FinTech players, or at 

least the ones that I represent. That is not to say that one will not 

happen the second this hearing ends. 

For the end user—and I should also add, for the aggregators, 

many have adopted policies where they do not collect PII. So they 

are the pipeline; they connect one entity to the data that they acquire for the use case, but do not themselves retain the identifying 

information that the end user provides to their third party. 

But I think underlying the question, Senator, is there need to be 

standards for data security in this ecosystem, and that is why my 

members at least have come out and said, whether it is regulatorily 

prescribed or whether it is private sector driven, we are ready to 

have that conversation. And we have already started to deploy 

some of those standards across the 50 companies that I work with. 

Senator BROWN. Thank you. 

Chairman CRAPO. All right. Thank you, Senator Brown, and I 

again want to thank the witnesses. I have a lot more questions I 

want to ask, and I do not know if I will pummel you with all of 

those, but over time we are going to dig much more deeply into this 

as a Committee. It is an incredibly important issue. And it is complex. It needs to be understood, and we appreciate your helping us 

to get a deeper understanding today. 

That concludes the Committee questioning. For Senators wishing 

to submit questions for the record, those questions will be due in 

1 week, on Tuesday, September 25. Witnesses, we ask you, when 

you receive questions, if you would promptly respond to them. And, 

again, we thank you for your willingness to come and share your 

expertise with us today. 

With that, this hearing is adjourned. 

[Whereupon, at 11:21 a.m., the hearing was adjourned.] 

[Prepared statements, responses to written questions, and additional material supplied for the record follow:] 

29 

PREPARED STATEMENT OF CHAIRMAN MIKE CRAPO 

Today, we will hear four very unique perspectives on a segment of financial technology, or ‘‘FinTech.’’ 

Almost exactly one year ago, the Committee held a hearing to explore the various 

sectors and applications of FinTech. 

In the short time period between that hearing and this one, many developments 

and innovations have occurred, both in the private sector and on the regulatory 

front. 

Digitization and data, in particular, are constantly evolving, challenging the way 

we have traditionally approached and conducted oversight of the financial services 

sector. 

As technology has developed and the ability to readily and cheaply interact with 

and use data has flourished, we have experienced a sort of revolution into the digital era. 

This digital revolution brings with it the promise of increasing consumer choice, 

inclusion and economic prosperity, among other things. 

Less than a decade ago, the concept of mobile banking, a simple transaction, was 

relatively new. 

Now, consumers have countless options by which to interact with and access their 

financial information and conduct transactions. 

As this marketplace rapidly develops, so must we constantly evaluate our regulatory and oversight framework, much of which was designed prior to the digital 

era. 

To the extent that there are improvements that can be made to better foster and 

not stifle innovation, we should examine those. 

Although these technological developments are incredibly positive, the increased 

digitization and ease of collecting, storing and using data presents a new set of challenges and requires our vigilance. 

Many products and services in the FinTech sector revolve around big data analytics, data aggregation and other technologies that make use of consumer data. 

Oftentimes these processes operate in the background, and are not always completely transparent to consumers. 

It is important for consumers to know when their data is being collected and how 

it is being used. 

It is equally important for the companies and the Government alike to act responsibly with this data and ensure it is protected. 

As we have seen in recent years, this can be a challenging task. 

In order to fully embrace the immense benefits that can result from technological 

innovation, we must ensure that proper safeguards are in place and consumers are 

fully informed. 

Today, I hope to hear from our witnesses about: the ways in which FinTech is 

changing the financial sector and the improvements that can be made to ensure the 

regulatory landscape welcomes that innovation; what kind of data is being collected 

and used, and how such data is secured and protected; and what are the opportunities and challenges going forward? 

PREPARED STATEMENT OF SENATOR SHERROD BROWN 

In the run-up to the financial crisis, Wall Street banks bragged about innovations 

that they claimed made the financial system less risky and credit more affordable. 

Some of these innovations were in consumer products—like interest-only subprime 

mortgages. Other innovations were happening behind the scenes, like the growth in 

risky collateralized debt obligations and credit default swaps. 

According to the banks, technological advances like increased computing power 

and information sharing through the internet allowed financial institutions to calculate and mitigate the risks of these complex financial innovations. Here in Washington, banks told lawmakers that regulation would hold back progress and make 

credit more expensive for consumers. Rather than look at financial technology with 

an eye to the risks, Federal banking supervisors repealed safety and soundness protections and used their authority to override consumer protection laws in several 

States. 

Eventually, so-called financial innovations led to the biggest economic disaster in 

almost a century, costing millions of Americans their homes and their jobs. 

Criticizing the bankers and regulators who lost sight of the enormous risks that 

came with these new innovations, former Fed Chair Paul Volcker declared that ‘‘the 

ATM has been the only useful innovation in banking for the past 20 years.’’ 

30 

I am more optimistic about some new technologies benefiting consumers rather 

than just lining Wall Street’s pockets, but I think we should look at this Treasury 

report with the same level of skepticism. 

Rather than learn from past mistakes, the Treasury report embraces the shortsightedness of precrisis regulators. It exalts the benefits of ‘‘financial innovation,’’ 

describes Federal and State regulation as ‘‘cumbersome’’ or as ‘‘barriers to innovation,’’ and recommends gutting important consumer protections, like the CFPB’s 

payday lending rule. It even suggests stripping away what little control we have 

over our personal financial data, just a year after Equifax put 148 million Americans’ identities at risk. 

Just like a dozen years ago, Wall Street banks and big companies are making 

record profits, but working families are struggling just to get by. Student loan debt 

is at record levels, and credit card defaults are rising. Worker pay isn’t keeping up 

with inflation, but we’ve managed to cut taxes for the richest Americans while 

CEOs and shareholders have reaped huge windfalls through over half a trillion dollars in stock buybacks. 

Plenty of financial institutions are adopting new technologies without running 

afoul of the law. Rather than focusing on how we can weaken the rules for a handful 

of companies who prefer to be called ‘‘FinTechs’’ rather than ‘‘payday lenders’’, or 

‘‘data aggregators’’ rather than ‘‘consumer reporting bureaus’’, Treasury should be 

focused on policies that help working families. 

This isn’t a partisan issue for me. I raised concerns about relaxing the rules for 

FinTech firms when Comptroller Curry, appointed by President Obama, suggested 

a special ‘‘FinTech’’ charter almost two years ago. 

The new leaders at the Federal Reserve, the OCC, the FDIC, and the CFPB have 

already made it clear that they’re ready to give Wall Street whatever it asks for. 

And the recommendations in this report call for more handouts for financial firms, 

FinTech or otherwise. 

I am, however, interested to hear from our witnesses about how new financial 

technologies could increase our control over our own information, better protect 

against cyberattacks, or make it easier for lenders to ensure they’re following the 

law. And as traditional banks partner with technology firms, I think it’s important 

for the Committee to consider where gaps in regulation might lead to future systemic risks. 

Thank you to the Chairman for holding this hearing, and to the witnesses for 

their testimony today. 

PREPARED STATEMENT OF STEVEN BOMS 

PRESIDENT, ALLON ADVOCACY, LLC, ON BEHALF OF CONSUMER FINANCIAL DATA

RIGHTS

SEPTEMBER 18, 2018 

Introduction 

Chairman Crapo, Ranking Member Brown, and Members of the Committee, thank 

you for the opportunity to testify today on behalf of the Consumer Financial Data 

Rights, or CFDR, Group. The CFDR Group is a consortium of nearly 50 financial 

technology (FinTech) companies, including financial data aggregation companies and 

end user-facing technology tools, on whose services more than 100 million consumers and small businesses collectively depend for access to vital financial services 

and wellness applications that serve them at every stage of their financial lifecycles. 

CFDR Group member-companies provide, for example, automated savings services, 

no-fee credit cards, investment advisory services, retirement savings advice and critical small business capital. In the complex and often opaque financial services ecosystem, the CFDR Group strives to be the voice of consumers and small businesses 

before policymakers and market stakeholders alike. 

My testimony today also provides the perspective of the Financial Data and Technology Association (FDATA) of North America, a trade association for which I serve 

as Executive Director. FDATA North America is comprised of several financial services providers, some newer entrant FinTech firms and some incumbent, traditional 

providers, united behind the notion that standardization of consumer data access is 

both a fundamental consumer right and a market-driven imperative. FDATA North 

America is a regional chapter of FDATA Global, which was the driving force for 

Open Banking in the United Kingdom and which continues to provide technical expertise to regulators and policymakers in London, to the European Commission, and 

to regulatory bodies internationally contemplating many of the same issues identified in the Department of the Treasury’s (‘‘the Department’’ or ‘‘Treasury’’) report 

31 

1 Comoreanu, A. (2018, June 11). ‘‘Credit Card Debt Study: Trends and Insights’’. Retrieved 

from https://wallethub.com/edu/credit-card-debt-study/24400/. 2Morrissey, M. (2016, March 3). ‘‘The State of American Retirement: How 401(k)s Have 

Failed Most American Workers’’. Retrieved from https://www.epi.org/publication/retirement-inamerica/. 3 ‘‘1 in 3 Americans Have Less Than $5,000 in Retirement Savings’’. (2018, May 8). Retrieved 

from https://news.northwesternmutual.com/2018-05-08-1-In-3-Americans-Have-Less-Than-5- 

000-In-Retirement-Savings. 4 ‘‘Report on the Economic Well-Being of U.S. Households in 2017’’. (2018, May 22). Retrieved 

from https://www.federalreserve.gov/publications/files/2017-report-economic-well-being-ushouseholds-201805.pdf. 5 ‘‘Summer Survey: Trends on Saving for Life’s Planned and Unplanned Events’’. (2018, August 1). Retrieved from https://bankoncit.com/blog/2018-summer-savings-survey/. 6 ‘‘Planning and Progress Study 2016’’. (2016, June 8). Retrieved from https:// 

news.northwesternmutual.com/planning-and-progress-study-2016. 

released on July 31, A Financial System That Creates Opportunities: Nonbank Financials, FinTech, and Innovation. 

The CFDR Group and its members consulted frequently with the Department as 

it considered the current state of the FinTech market, the consumer and small business benefits it provides to Americans today, and how best to harness innovation 

in the FinTech ecosystem moving forward while ensuring that consumers, small 

businesses and the financial system itself are well protected. The CFDR Group’s engagement with Treasury was principally focused on the crucial issue of consumerpermissioned financial data, which ultimately was an area of emphasis in the Department’s report. 

Ultimately, any provider of a technology-based financial tool, whether that provider is a FinTech firm or a longstanding market incumbent, depends on the ability 

to access and utilize, with the consumer’s or small business’ express permission, elements of that customer’s financial data to offer its products or services. Financial 

data, including, for example, balances, fees, transactions, and interest charges, are 

essential to facilitating the technology tools on which millions of Americans depend. 

These data elements are typically held at the financial institution with which that 

customer holds a checking, savings, and/or lending account. Before providing an 

overview of how this data exchange works today in the United States, I would first 

like to underscore the immense need that the technology-based tools offered by 

CFDR Group and FDATA North America member firms are fulfilling. 

The State of U.S. Consumer Finances 

Although the U.S. economy is performing well from a macroeconomic standpoint, 

there are unquestionably significant numbers of Americans who are being left behind and are financially invisible. The level of credit card debt in the United States 

is historically high and, earlier this year, exceeded $1 trillion for the first time ever, 

with the average American household holding approximately $8,200 in credit card 

debt. 1 About half of American consumers have no retirement savings at all, and of those that do, the average retirement account balance is about $60,000. 2 Approximately one-third of American adults have sufficient savings to last comfortably for 

more than a few months during their golden years. 3

The crisis, of course, is not limited only to an accumulation of debt or a lack of 

retirement savings. The Federal Reserve Board of Governors determined earlier this 

year that 40 percent of American consumers could not afford a surprise $400 expense without either selling an asset or taking on additional debt. 4 And, 

unsurprisingly, many of us do encounter these surprise expenses. According to a recent study by CIT Bank, while half of Americans experience a financial emergency, 

such as a major health event or an unforeseen home repair, every year, more than 

one in four do not save for these unexpected events. 5

It is no wonder, then, that 85 percent of Americans report feeling anxious about 

their financial state, with more than two-thirds believing that their financial anxiety is negatively impacting their overall health. 6

Compounding this economic predicament is the growing complexity of most consumers’ and small business’ relationships with the American financial system. The 

vast majority of Americans have multiple different accounts across a variety of products providers. The most basic, fundamental first step towards financial health— 

simply understanding what one has and what one owes—is often intimidating and 

logistically difficult for all but the most financially savvy. The technology-powered 

tools on which millions of Americans have come to depend, provide intuitive, accessible platforms that enable even the least financially savvy among us to manage 

their finances and improve their economic outcomes. In addition to allowing Americans to see the totality of their financial accounts in one place, these applications 

empower consumers and small businesses to find lower loan rates or better loan 

32 

7 ‘‘Financial Inclusion in the United States’’. (2016, June 10). Retrieved from https:// 

obamawhitehouse.archives.gov/blog/2016/06/10/financial-inclusion-united-states. 8 ‘‘EY FinTech Adoption Index 2017’’. (2017, June 28). Retrieved from https://www.ey.com/ 

Publication/vwLUAssets/ey-fintech-adoption-index-2017/$FILE/ey-fintech-adoption-index2017.pdf. 

terms, to avoid predatory products and services, to compare fees across different 

product offerings, to receive personalized investment and wealth management advice, to find and secure capital that otherwise may not be extended, or to take advantage of budgeting and savings tips to secure their financial future. 

This of course presumes that one has access to the system in the first place. 

Twenty percent of adult Americans are underbanked by the traditional financial 

services system and almost nine million American households are entirely 

unbanked. 7 For these consumers, third-party, technology-based tools can provide 

vital, affordable access to a financial system that has left them behind. 

Regardless of the use case a consumer or a small business wishes to leverage, and 

irrespective of whether that technology-powered tool is offered by a FinTech firm or 

a traditional financial services provider, the lifeblood of these tools is userpermissioned data access: the right of the consumer or small business to affirmatively grant access to the third party of their choice to connect to or see the financial 

data required to provide them the product or service for which they have provided 

their consent. 

The State of Consumer-Permissioned Financial Data 

Usage of third-party, FinTech tools in the U.S. is widespread: by 2017, 87 percent 

of consumers preferred to adopt a FinTech application rather than use a product 

or service offered by a traditional financial services provider. 8 To gain access, with 

the consumer’s or small business’ consent, to their customer’s financial data in order 

to provide their products or services, the vast majority of technology-based tools retain contractual relationships with financial data aggregators, such as Envestnet 

Yodlee, Quovo, or Morningstar ByAllAccounts, all of which are members of the 

CFDR Group. These aggregators, which have built data connectivity to thousands 

of U.S. financial institutions over many years, function as technology service providers for the consumer or small business-facing applications. Once the consumer 

or small business has affirmatively provided their consent to the application that 

they wish to utilize, that consent is transmitted to their financial institution and 

they are authenticated. Upon authentication, the aggregator utilizes one or more 

methods of data consumption to capture the financial data permissioned by the end 

user that is required to deliver the use case requested and delivers it to the application provider. The application provider then uses this data to provide its service or 

product to the consumer or small business. 

Because there are no overarching statutory, regulatory or market standards in the 

United States with regard to consumer or small business authentication, or with regard to the data consumption protocol used by aggregators to transmit the end 

user’s data, with their permission, to their application of choice, there are several 

different methods used in the ecosystem today. To authenticate, end users typically 

provide their online banking credentials, either to the third-party application provider delivering them the service or product they have selected, or, through redirection, to their financial institution, which in turn issues an access token to the third 

party and the aggregator with which it partners. Once the consumer or small business is authenticated, the aggregator may use any of several data consumption 

methods to retrieve the financial data required for the use case. Some financial institutions have created direct feeds, such as Application Programming Interfaces 

(APIs), specifically for aggregators and third parties to utilize for the purpose of providing products or services to their customers; however, the vast majority of U.S. 

financial institutions have not. The significant capital investment required to build 

and maintain these feeds typically results in only the largest U.S. financial institutions deploying them. In the case where no direct data feed is available, aggregators 

employ proprietary software to retrieve the data required for the use case from the 

end user’s native online banking environment. This data consumption method is 

colloquially referred to as ‘‘screen scraping.’’ 

I note here a critical issue that underlies the entire FinTech ecosystem’s ability 

to continue to deliver the products and services on which many consumers and 

small businesses now rely: There is no legal requirement in the United States stipulating that a financial institution must make the consumer’s or small business’ financial data it holds available to a third party in the event their customer provides 

affirmative consent for the institution to do so. Accordingly, a consumer’s or small 

business’ ability to take advantage of the benefits offered by third-party, technologyVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00036 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
33 

9 ‘‘A Financial System That Creates Economic Opportunities: Nonbank Financials, FinTech, 

and Innovation’’. (2018, July 31). Retrieved from https://home.treasury.gov/sites/default/files/ 

2018-08/A-Financial-System-that-Creates-Economic-Opportunities---Nonbank-FinancialsFintech-and-Innovationl0.pdf. 10 ‘‘Consumer Protection Principles: Consumer-Authorized Financial Data Sharing and Aggregation’’. (2017, October 18). Retrieved from https://files.consumerfinance.gov/f/documents/ 

cfpblconsumer-protection-principlesldata-aggregation.pdf. 11 Ibid. 12Phillips, C. (2018, September 12). Remarks to the Exchequer Club of Washington. Speech, 

Washington, DC. 13 ‘‘A Financial System That Creates Economic Opportunities: Nonbank Financials, FinTech, 

and Innovation’’. (2018, July 31). Retrieved from https://home.treasury.gov/sites/default/files/ 

2018-08/A-Financial-System-that-Creates-Economic-Opportunities---Nonbank-FinancialsFintech-and-Innovationl0.pdf. 

based tools rests almost entirely with the inclination of their financial institutions 

to allow them to do so. Not all financial institutions are disposed to allow thirdparty tools, some of which compete directly with their own products and services, 

complete access to their customers’ data. The Treasury’s report notes, for example, 

that ‘‘access [to financial data] through APIs was frequently and unilaterally restricted, interrupted, or terminated by financial services companies.’’ 9 In many 

cases, these APIs also may not provide the full suite of data required by technologypowered tools to deliver their products or services. The market is therefore fundamentally dislocated; the ability of U.S. consumers and small businesses to utilize 

third-party technology tools is dependent on the financial services provider(s) with 

which they do business, with disparate outcomes for Americans who bank with different financial institutions. The unevenness of this playing field could materially 

worsen as many large U.S. financial institutions seek to impose on consumers and 

small businesses their view of how the ecosystem should function in the form of bilateral agreements with aggregation firms. 

The Bureau of Consumer Financial Protection (‘‘BCFP’’ or ‘‘the Bureau’’) engaged 

in a year-long process to address this issue, which ultimately culminated in the release in October 2017 of nonbinding principles for consumer-authorized financial 

data sharing and aggregation. 10 Though the BCFP’s engagement was earnest and 

well-intentioned, the principles it ultimately released did not meaningfully shape or 

change market behavior, both because they were not legally binding and because 

the Bureau declined to forcefully stake out a position regarding consumerpermissioned data access. The BCFP asserted, for example, that consumers ‘‘generally’’ should be able to use ‘‘trusted’’ third parties to obtain information from account providers 11 but provided no further detail regarding these qualifiers. As a result of this ambiguity, and despite the BCFP’s much-needed engagement in the 

market, the state of consumer-permissioned financial data access in the United 

States is not meaningfully different today than it was when the Bureau’s nonbinding principles were released almost 1 year ago. 

While policymakers in the United States have not issued any regulation specific 

to consumer-permissioned financial data access, regulators and legislators abroad 

have sought to harness innovation. As these other jurisdictions implement frameworks that harness innovation, the U.S. market is at risk of losing pace internationally with the development and delivery of new, innovative financial tools for consumers. There is, accordingly, ‘‘a huge risk the U.S. will fall behind, and with that 

a risk that jobs will go elsewhere.’’ 12

The United Kingdom’s Open Banking regime, under which consumers can utilize 

authorized third-party tools without restriction, began its implementation phase 

earlier this year, as did Europe’s Second Payments Services Directive, or PSD2. In 

Mexico, following a recently passed new FinTech law, the Bank of Mexico and the 

National Banking and Securities Commission (CNBV) are in the midst of developing 

API standards that national financial institutions will be required to adopt in order 

to facilitate the use of third-party FinTech tools. The Australian Government has 

made public its intention to begin its implementation of an Open Banking regime 

in July 2019, and New Zealand, Canada, and Mexico are not far behind. 

In the preamble to its report, Treasury rightly notes that policymakers’ engagement with the FinTech ecosystem—and the decisions that are made by the financial 

regulatory agencies in response to the Department’s recommendations, particularly 

with regard to consumer-permissioned data access—will have implications for U.S. 

global competitiveness. 13 Developments such as the announcement earlier this 

month of a pact between the Monetary Authority of Singapore and the Dubai Financial Services Authority to work collaboratively on digital payments and blockchain 

projects are becoming increasingly common. While the U.S. market continues to consider the most fundamental policy issues regarding innovation in financial services, 

34 

14 ‘‘The Pulse of FinTech—Q4 2017’’. (2018, February 13). Retrieved from https:// 

home.kpmg.com/xx/en/home/insights/2018/02/pulse-of-fintech-q4-2017.html. 15 ‘‘A Financial System That Creates Economic Opportunities: Nonbank Financials, FinTech, 

and Innovation’’. (2018, July 31). Retrieved from https://home.treasury.gov/sites/default/files/ 

2018-08/A-Financial-System-that-Creates-Economic-Opportunities---Nonbank-FinancialsFintech-and-Innovationl0.pdf. 16 ‘‘Know Before You Share: Be Mindful of Data Aggregation Risks’’. (2018, March 29). Retrieved from http://www.finra.org/investors/alerts/know-you-share-be-mindful-data-aggregation-risks. 

policymakers in other jurisdictions are assertively creating well-regulated, innovative regulatory frameworks designed to attract and encourage large-scale innovation. The stakes are high: Globally, the FinTech market attracted more than $31 

billion in 2017, with the United States attracting more than half the investment in 

the market. 14

Treasury Report Recommendations 

Both the CFDR Group and FDATA North America strongly believe that the Department in its July report identified the key outstanding issues with regard to consumer and small business financial data access. I would respectfully highlight five 

of the Treasury recommendations for the Committee’s consideration, as formalizing 

standards around these areas would significantly bolster the ability of Americans 

to utilize third-party technology tools to improve their financial well-being: 

1. The Bureau should affirm that for purposes of Section 1033 [of the Dodd–Frank 

Wall Street Reform and Consumer Protection Act], third parties properly authorized by consumers . . . fall within the definition of ‘‘consumer’’ under Section 1002(4) of Dodd–Frank for the purpose of obtaining access to financial account and transaction data. 

Treasury’s assertion that the Dodd–Frank Act’s inclusion of language in Section 

1033 mandating that financial institutions provide their customers with electronic 

access to their data should be interpreted to ‘‘cover circumstances in which consumers affirmatively authorize, with adequate disclosure, third parties such as data 

aggregators and consumer FinTech application providers to access their financial account and transaction data from financial services companies’’ 15 marks a significant 

step forward for consumers’ and small businesses’ financial rights. Though it may 

seem self-evident, because Section 1033 of Dodd–Frank provides that the BCFP has 

the authority to promulgate a rule to ensure end users have electronic access to 

their online data, and the Bureau has thus far declined to do so, Treasury’s affirmation that the Dodd–Frank Act provides this right to consumers and small businesses, even in the absence of a Bureau rulemaking, represents a significant victory 

for innovation and for consumer and small business financial empowerment. The 

CFDR and FDATA North America both respectfully echo the Department’s call for 

further action on this score by the BCFP. 

2. All regulators . . . should recognize the benefits of consumer access to financial 

account and transaction data in electronic form and consider what measures, 

if any, may be needed to facilitate such access for entities under their jurisdiction. 

One of the systemic disadvantages facing the FinTech ecosystem in the United 

States as compared with many other countries that have imposed standards with 

regard to consumer-permissioned data access is the immense relative regulatory 

fragmentation that exists in the U.S. financial system. In the United Kingdom, for 

example, two agencies, the Financial Conduct Authority and the Competition and 

Markets Authority, represent the totality of regulatory authorities that were required to implement an entirely new, innovative approach to harnessing FinTech 

under Open Banking. Mexico’s CNBV and the Bank of Mexico are themselves responsible for developing and imposing financial API standards. The Australian 

Treasury and the Competition and Consumer Commission alone will deliver Open 

Banking in 2019. 

There are at least eight Federal regulatory agencies with jurisdiction over at least 

some portion of financial data access in the United States: the BCFP, the Office of 

the Comptroller of the Currency, the Federal Deposit Insurance Corporation, the 

National Credit Union Administration, the Federal Reserve Board of Governors, the 

Securities and Exchange Commission, the Commodity Futures Trading Commission, 

and the Federal Trade Commission. (Other Federal agencies, including the Financial Crimes and Enforcement Network and the Financial Industry Regulatory Authority, have also been involved in the issue of consumer-permissioned data recently 

permissioned data recently. 16) One commonly discussed regulatory constraint to the 

35 

17 ‘‘Third-Party Relationships’’. (2017, June 7). Retrieved from https://www.occ.gov/newsissuances/bulletins/2017/bulletin-2017-21.html. 18 ‘‘BCFPB Collaborates With Regulators Around the World To Create Global Financial Innovation Network’’. (2018, August 7). Retrieved from https://www.consumerfinance.gov/about-us/ 

newsroom/bcfp-collaborates-regulators-around-world-create-global-financial-innovation-network. 19 ‘‘Open Banking Progress Update 13 July–31 August’’. (2018, September 3). Retrieved from 

https://www.openbanking.org.uk/about-us/news/open-banking-progress-update-july-august2018/. 20 12 CFR 205. 

open transmission of permissioned consumer and small business financial data has 

been the prudential bank regulatory agencies’ third-party vendor risk management 

guidance. 17

There are also, of course, regulatory authorities in each State that have jurisdiction over entities that play a role in the FinTech market, financial services providers and FinTech firms alike. While Treasury cannot address the intrinsic, structural disadvantages in the United States’ regulatory regime as compared with other 

countries’, its call for all of the agencies in this space to align behind the Department’s interpretation of Section 1033 of the Dodd–Frank Act is an important step 

towards a level playing field, and one that could be hastened by Congressional engagement. While, interestingly, some U.S. regulatory agencies have begun to collaborate with their peers internationally, 18 greater domestic coordination that provides harmonization, rather than divergence, would spur innovation and improved 

consumer and small business financial outcomes. 

3. The Bureau [should] work with the private sector to develop best practices on 

disclosures and terms and conditions regarding consumers’ use of products and 

services powered by consumer financial account and transaction data provided 

by data aggregators and financial services companies. 

The CFDR Group and FDATA North America strongly believe that consumers and 

small businesses should be empowered to use their financial data for their own financial benefit. To fully realize this empowerment, however, end users must be able 

to clearly and easily understand to what data elements they are granting third parties access to and for what purpose, as well as how they can revoke their consent 

to access and use the data. Though several industry groups have previously sought 

to establish guidelines in this space—and others continue to seek to formulate best 

practices—given the vast scope of the financial services market, very little standardization has taken place. 

Fortunately, to the extent that the private sector, the BCFP and other regulatory 

agencies come together to develop best practices that could be adopted broadly 

across the industry, a market-tested framework already exists. The United Kingdom’s Open Banking architecture includes prescriptive consent flows that ensure 

that a consumer’s or small business’ experience granting or revoking consent to access their data to any third party in the Open Banking environment is uniform. Accordingly, consumers in the Open Banking ecosystem experience the same consentgranting process across every third-party application they use, regardless of the financial institution with which they have their primary banking relationship. 

Offboarding is similarly uniform. The evidence suggests that end users of the Open 

Banking ecosystem are quickly becoming comfortable and familiar with these standards; three million Open Banking API calls were made this July, a month-overmonth increase of 50 percent. 19 Public and private sector participants would do well 

to use these Open Banking consent standards as a starting point for creating best 

practices in the U.S. market. 

4. Any potential solution [to move to more secure and efficient methods of data access should] address resolution of liability for data access. If necessary, Congress and financial regulators should evaluate whether Federal standards are 

appropriate to address these issues. 

The CFDR and FDATA North America believe that the issue of liability is the 

fundamental obstacle preventing the U.S. market from offering a more even, consumer-centric delivery of third-party tools powered by permissioned data 

connectivity. Decades-old regulations, such as Regulation E, create either the regulatory expectation or the consumer perception that financial institutions will largely 

make their customers whole in the event of any financial loss, including as a result 

of a data breach at a third party. 20 Further, prudential bank regulators have told 

the FinTech community that the potential liability exposure to customers that nationally regulated banks face in the event of a data breach for which customers experience a financial loss represents a safety and soundness concern. 

36 

21 ‘‘A Financial System That Creates Economic Opportunities: Nonbank Financials, FinTech, 

and Innovation’’. (2018, July 31). Retrieved from https://home.treasury.gov/sites/default/files/ 

2018-08/A-Financial-System-that-Creates-Economic-Opportunities---Nonbank-FinancialsFintech-and-Innovationl0.pdf. 

Largely as a result, some of the financial institutions seeking bilateral agreements 

with data aggregators are seeking to place the aggregator in the position of holding 

full, unlimited liability for the FinTech ecosystem. These financial institutions hold 

that, because the aggregator is the only party with which they will have a bilateral 

agreement, the aggregator is the only entity from which they can recoup customer 

losses; however, this position is both impractical and untenable. Aggregators typically have no direct relationship with consumers or small businesses. Practically, 

they do not have the scale necessary to be in a position to provide their financial 

institution counterparties with boundless liability protection for the entire FinTech 

market, nor would that fairly apportion responsibility throughout the ecosystem. As 

responsible stewards of consumer data, however, aggregators are prepared to be liable for any direct consumer harm that arises as a result of a breach for which they 

are at fault. 

More broadly, the question of liability must also address the responsibility of the 

third party with which the consumer or small business has a relationship, whether 

it is a FinTech application or a technology tool delivered by a traditional financial 

institution. The CFDR earlier this year released a set of principles, Secure Open 

Data Access (SODA), which called for the implementation of traceability, minimum 

cyberliability insurance standards and other standards designed to ensure that the 

entity responsible for consumer financial loss as a result of a data breach—be it a 

bank, an aggregator, or a FinTech firm—is the entity charged with making the end 

user whole. While CFDR members are starting to implement the SODA principles 

with regard to liability, the financial regulatory agencies and Treasury could augment and assist this work by undertaking efforts to create a more vibrant and affordable cyberliability insurance market, similar to the steps taken by Her Majesty’s 

Treasury in the United Kingdom last year. 

5. Any potential solution [to move to more secure and efficient methods of data access should] also address the standardization of data elements as part of improving consumers’ access to their data. 

Treasury notes in its report that ‘‘a standardized set of data elements and formats 

would help to foster innovation in services and products that use financial account 

and transaction data . . . ’’ 21 While the CFDR Group and FDATA North America 

wholeheartedly agree with the Department’s recommendation, I would respectfully 

submit an addendum to this recommendation. Standardization of data elements will 

only be impactful to American consumers and small businesses if they are able to 

grant access to all of the data required to power the use case they have selected. 

A standardized data set that, for example, does not allow end users to grant access 

to any data fields related to the fees or interest rates a financial institution assesses 

inherently restricts the ability of that customer to utilize fee comparison tools or to 

use a third-party tool to select an alternative, lower-cost provider. 

Therefore, with the appropriate consent, authentication, and liability safeguards 

in place, the standardized data elements made available to the consumer or small 

business to permit access to third parties of their choosing should include all of the 

data elements available to the end user in their native online banking environment. 

This approach would fully enable end users to leverage their own financial data to 

their economic benefit and it would allow for the realization of a competitive, free 

marketplace in which consumers have full transparency into financial products and 

services offered by FinTech providers and financial services firms alike. 

Conclusion 

Though tens of millions of American consumers and small businesses are already 

utilizing third-party tools to improve their financial well-being, more can and should 

be done to harness the power of innovation and to give Americans full control of 

their own financial data and future. The Treasury’s report provides an insightful 

overview of the outstanding issues facing the U.S. market that should be collaboratively addressed in order to better serve consumers and to ensure that the United 

States remains globally competitive as multiple countries implement comprehensive, 

consumer-centric financial data access frameworks. The CFDR Group and FDATA 

North America stand ready to work with the Department, the regulatory agencies, 

market stakeholders, and, of course, Congress, to implement the Treasury’s recommendations. 

37 

1Financial advisors can use eMoney Advisor, a Fidelity-owned business that provides account 

aggregation services along with software that helps them provide financial advice to their clients. 2Fidelity offers its FullView® services to retail customers through Fidelity.com and to retirement plan participants through NetBenefits.com, and developed its first account aggregation 

service over 15 years ago. Fidelity FullView provides a snapshot of customers’ net worth in a 

simple format with an ability to do budgeting and financial planning. 

PREPARED STATEMENT OF STUART RUBINSTEIN 

PRESIDENT, FIDELITY WEALTH TECHNOLOGIES, AND HEAD OF DATA AGGREGATION

SEPTEMBER 18, 2018 

Chairman Crapo, Ranking Member Brown, and Members of the Committee: thank 

you for holding this important hearing. Fidelity is very interested in FinTech and 

data policy and has a unique perspective to share on financial data account access 

and aggregation used by many FinTech firms. 

My name is Stuart Rubinstein and I am President of Fidelity Wealth Technologies 

and Head of Data Aggregation. In this role, I oversee the team focused on helping 

Fidelity and other institutions enable consumers to securely share account data and 

documents with third parties. Fidelity is a leading provider of investment management, retirement planning, portfolio guidance, brokerage, benefits outsourcing, and 

other financial products and services to more than 30 million individuals, institutions, and financial intermediaries with more than $7 trillion in assets under Administration. Our goal is to make financial expertise broadly accessible and effective 

in helping people live the lives they want. 

I will focus my testimony for this hearing on an issue I first worked on over 20 

years ago: financial data aggregation services and ways we can make data sharing 

safer and more secure. 

Fidelity’s Perspective on Data Aggregation 

Fidelity has a unique perspective on financial data aggregation practices and necessary protections for customers. We are on all sides of this issue: we are an 

aggregator of data for third parties, 1 we are a significant source of data for 

aggregators acting on behalf of our mutual customers, and we offer a data aggregation service for our retail customers and retirement plan participants. 2 This perspective gives us a thorough understanding of the benefits of financial data aggregation, but also of the very real cybersecurity and privacy risks that current data aggregation industry practices create. 

Financial data aggregation in this context refers to services that, with customers’ 

consent, collect financial information from their various bank, brokerage, and retirement accounts, along with other sources, to be displayed and processed in an aggregated view. An example of this kind of service might be a budgeting and planning 

smartphone app. Consumers use third party applications that leverage data aggregation because they value tools to help manage financial planning, budgeting, tax 

preparation, and other services. As part of our focus on helping our customers, Fidelity works to make it possible for customers to access the services they want to 

use—including third party aggregation-based services. To that end, customers have 

been able to use their Fidelity data in third party applications for many years. However, the cybersecurity environment has significantly changed over that time and 

we have a responsibility to protect the very sensitive personal financial data and 

assets of our more than 30 million customers from misuse, theft, and fraud. 

Current data aggregation practices make this challenging, because they rely on 

consumers providing their financial institution log-in credentials (i.e., username and 

password) to third parties. Those third parties, typically data aggregators, then almost always employ a practice known as ‘‘screen scraping.’’ At its most basic, screen 

scraping involves the use of computerized ‘‘bots’’ to log-in to financial institution 

websites, mobile apps, or other applications as if they were the consumer. Once the 

bots have access to the site or app, they ‘‘scrape’’ customer data from the various 

screens to be presented on a consolidated basis, along with information scraped and 

collected from other sources. 

There are two consumer data security problems with this practice. First, as a 

matter of basic security consumers should not be asked or required to share their 

private log-in credentials in order to access a third party service. Doing so creates 

cybersecurity, identity theft, and data security risks for the consumer and financial 

institutions. Unfortunately, we know that due to years of this practice, financial institution log-in credentials are now held by a myriad of companies. Some are likely 

very secure, while others may not be secure at all. Given this, allowing third parties 

to log-in using these credentials as if they are the customer creates significant risk 

38 

of cyberfraud. Because consumers go directly to data aggregators or their commercial clients and not their financial institution, the financial institutions never really 

know if the activity has in fact been authorized by the customers or if the customer 

credential has been compromised and a criminal is using the data aggregation service to test the credential’s validity and illicitly gather data. 

Second, screen scraping may result in access to data fields far beyond the scope 

of the service a third party offers the consumer—including personally identifiable 

information (PII) about consumers and in some cases their dependents. This means 

third parties have access to fields of information often used by financial institution 

call centers to identify customers. For example, if a consumer provides his or her 

log-in credentials to a budgeting app, that app potentially has access to sensitive 

personal information like customer dates of birth and dependent names and dates 

of birth, all of which might be data financial institutions use to verify customer 

identities online or over the phone. Collection of information beyond what is needed 

for the service the consumer has elected creates unnecessary risk. And all of this 

adds up to an array of risks financial institutions must navigate to protect the integrity of their systems and the assets of their customers. 

In considering the challenges described above, Fidelity developed the following 

five principles that we believe should guide industry in creating better data sharing 

solutions: 

1. We strongly support consumers’ right to access their own financial data and 

provide that data to third parties. As a provider of aggregation services ourselves, we know that customers value these products, and the demand for aggregation is likely to increase. We also believe that the concept of access is 

broad enough to encompass security, transparency, and cybersecurity protections for consumers. 

2. Data access and sharing must be done in a safe, secure, and transparent manner. We firmly believe credential sharing makes the system less safe for consumers, aggregators, and financial institutions alike. While we strongly support customer access, the security of customer data, customer assets, and financial institution systems must be our primary concern. 

3. Consumers should provide affirmative consent and instruction to financial institutions to share their data with third parties. Rather than trust that third 

parties who use customer log-in credentials to access a financial institution’s 

website are authorized, customers should tell financial institutions which third 

parties have permission to access their financial data. This eliminates the potential that unauthorized access using credentials is mistaken for authorized 

access. 

4. Third parties should access the minimum amount of financial data they need 

to provide the service for which the customer provided access. There should be 

a tight nexus between the service provided and the information collected by 

third party aggregators. For example, if a customer signs up for a tax planning 

service that leverages aggregation, that service should only access the information needed for tax planning. 

5. Consumers should be able to monitor who has access to their data, and access 

should be easily revocable by the consumer. We believe data sharing and 

permissioning should be an iterative process, with customers engaged continuously. Moreover, many customers believe revoking access is as easy as deleting 

an app from their phone—this is not the case. Customers should be able to easily instruct their financial institution to revoke access when they no longer 

want or need the aggregation-based service. 

We believe that embracing these principles will better protect consumers, 

aggregators, and financial institutions, and facilitate more efficient data sharing 

practices. 

How Do We Solve This for Consumers? 

Fortunately, although the risks and challenges of the current system are serious, 

there are steps financial institutions and aggregators can take together to improve 

the data sharing ecosystem. The financial services industry is employing technological solutions for the secure exchange and access of financial information. These 

technologies involve the implementation and use of application programming interfaces (APIs), which are provided by the financial institution to aggregators and 

other third parties. An API works in conjunction with an authentication process 

that is handled by the financial institution. There are authentication processes, for 

example ‘‘open authorization’’ (OAuth), that do not involve sharing of account access 

credentials with third parties. Consumers who want their data aggregated sign into 

39 

3 Available at https://files.consumerfinance.gov/f/documents/cfpb—consumer-protection-principles—data-aggregation.pdf. Fidelity commented on the Request for Information that culminated in these principles (https://www.regulations.gov/document?D=CFPB-2016-0048-0053). 

4See https://www.fsisac.com/article/fs-isac-enables-safer-financial-data-sharing-api. Fidelity 

is a member of FS–ISAC and contributed to the development of the durable data API. 5 Available at http://www.finra.org/investors/alerts/know-you-share-be-mindful-data-aggregation-risks. 6 Available at https://www.sifma.org/resources/general/data-aggregation-principles/. Fidelity is a member of SIFMA and worked closely with other member firms in developing these principles. 

their accounts at the financial institution’s website and provide authorization for 

third party aggregators to access their financial data. The financial institution and 

the data aggregator then manage that connection through secure, encrypted tokens 

that are provisioned for the specific connection. 

There are several compelling consumer and data security benefits for moving to 

APIs. First, it keeps log-in credentials private and secure by eliminating the need 

for consumers to share log-in credentials with third parties. This reduces the cyber, 

identity, and personal data security risks that exist when a consumer shares private 

log-in details with a third-party. Second, it puts the consumer in the driver’s seat 

by giving consumers greater transparency and control of their data by allowing consumers to provide unequivocal consent and instruction to share their data with 

third parties. Third, it allows financial institutions and aggregators to agree on 

what data should be shared and avoid over-scraping. Fourth, it eliminates the need 

to reconfigure aggregators’ systems every time a consumer changes his or her 

username or password or the financial institution updates its webpage. Fifth, it removes the traffic-intensive screen scraping activity from financial institutions’ web 

sites and other digital properties, returning that capacity to the individual consumers for whom those sites were created. Finally, it enables the consumer to monitor the ongoing access and instruct their financial institution to revoke the consent 

if desired. 

Fidelity Access 

In November 2017, Fidelity announced its own API solution for data sharing 

called Fidelity Access. Fidelity Access will allow Fidelity customers to provide third 

parties access to customer data through a secure connection without providing login credentials. Fidelity Access will include a control center, where customers can 

grant, monitor, and revoke account access at any time. We have been working closely with aggregators and other third parties on adoption of this solution. 

Of particular note, eMoney Advisor, Fidelity’s affiliate that offers its own aggregation service, is committed to working with other financial institutions that offer 

APIs. By championing the exclusive use of APIs to facilitate customers providing 

third parties access to their financial data, we hope to show leadership by taking 

action to better secure our customers’ data. 

Industry Standards and Policymaker Guidance 

In addition to our own efforts to address the problems with data aggregation, we 

have been working with a wide array of industry and public sector stakeholders. We 

support many of the data sharing and aggregation principles that have been put 

forth: 

• In October 2017, after a year-long inquiry into the topic, the Bureau of Consumer Financial Protection (BCFP) released nonbinding financial data sharing 

and aggregation principles, which helpfully emphasized the importance of access, security, transparency, and consent. 3

• In February 2018, the Financial Services Information Sharing and Analysis 

Center (FS–ISAC), a cybersecurity information sharing group focused on the financial services industry, published a standard durable data API free of charge 

to help facilitate safer transfer of financial data. 4 The Fidelity Access API is 

based on this standard. 

• In March 2018, the Financial Industry Regulatory Authority (FINRA) published 

an investor alert that explained the risks associated with aggregation-based 

services and noted that many firms are moving toward APIs. 5

• In April 2018, the Securities Industry and Financial Markets Association 

(SIFMA) released data aggregation principles that focused on similar themes. 6

• In July 2018, the U.S. Department of the Treasury released a report on 

Nonbank Financials, FinTech, and Innovation that includes a lengthy discussion of financial data aggregation and helpful recommendations, including simVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00043 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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7 Available at https://home.treasury.gov/sites/default/files/2018-08/A-Financial-Systemthat-Creates-Economic-Opportunities---Nonbank-Financials-Fintech-and-Innovationl0.pdf. 1Steven T. Mnuchin and Craig S. Phillips, U.S. Dep’t of the Treasury, ‘‘A Financial System 

That Creates Economic Opportunities: Nonbank Financials, FinTech, and Innovation’’ (2018) 

[hereinafter Treasury Report]. 

plified disclosures, moving away from screen scraping, and eliminating log-in 

credential sharing. 7

These efforts to provide guidance have brought many of the challenges and risks 

associated with data aggregation to the fore and encouraged healthy debate on how 

to solve them. 

Continuing Challenges 

Despite the general consensus that the status quo is untenable and the industry 

should move to safer data sharing technologies, there are roadblocks that prevent 

wider adoption of APIs and other solutions. Here are what we see as the most challenging: 

• Inertia: One force working against adoption of safer data sharing technologies 

is simple inertia. Existing practices have been the norm for close to two decades. Getting firms to adopt new technologies can be challenging no matter 

what the benefits. However, given the stakes, with headlines replete with examples of cybersecurity events and data breaches, this is not an adequate reason 

to resist better data sharing technology. 

• Cost: Another countervailing force is cost. One of the unfortunate truths about 

screen scraping is that it is cheap and effective. While safer technologies like 

APIs have become less costly as technology advances, building one does incur 

costs. We believe the incremental increase in cost is well worth the substantial 

security and transparency improvements for consumers. Still, financial institutions should be sensitive to this reality, which is why we are providing Fidelity 

Access to third parties free of charge. 

• Liability: Liability is the most stubborn blocker to wider adoption of safer data 

sharing technologies. Third party aggregators want to limit their potential liability in the event that financial data is illicitly obtained. We have seen firms 

try to limit their liability to low dollar amounts. These kinds of limits are untenable for financial firms like Fidelity that have a duty to protect client assets. 

Fidelity believes firms that obtain and handle consumer data should be held responsible to protect that data from unauthorized use, just as we are. Any other 

standard creates moral hazard and does not incentivize aggregators to take 

their data stewardship responsibilities seriously. 

Until all industry participants—aggregators, FinTech firms, and financial institutions—are prepared to overcome these challenges in a responsible manner, we will 

not move as swiftly as we otherwise could to adopt safer data sharing technologies. 

Thank you again for the opportunity to testify and I look forward to answering 

your questions. 

PREPARED STATEMENT OF BRIAN KNIGHT 

DIRECTOR, INNOVATION AND GOVERNANCE PROGRAM, MERCATUS CENTER AT GEORGE

MASON UNIVERSITY

SEPTEMBER 18, 2018 

Good morning, Chairman Crapo, Ranking Member Brown, and Members of the 

Committee. I thank you for inviting me to testify. 

My name is Brian Knight, and I am the director of the Innovation and Governance Program and a senior research fellow at the Mercatus Center at George Mason 

University. My research focuses primarily on the role technological innovation plays 

in financial services. Any statements I make reflect only my opinion and do not necessarily reflect the opinions of the Mercatus Center or my colleagues. 

I would like to begin by thanking Chairman Crapo and Ranking Member Brown 

for their leadership in holding this hearing. The role of financial technology (or 

FinTech) in changing the market for financial services is continuing to grow, with 

innovations permeating all financial markets. The importance of these technological 

changes is reflected by the fact that the Treasury Department chose to devote almost an entire report to the topic in its series of reports on core principles in financial regulation. 1 I also appreciate your collecting speakers from a broad array of exVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00044 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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2See, e.g., U.S. Fed. Trade Comm’n, ‘‘Big Data: A Tool for Inclusion or Exclusion’’ 8-11 (2016) 

(summarizing findings of public workshop on big data regarding potential risks). 3Treasury Report, supra note 1, at 17. 

4Treasury Report, supra note 1, at 39–41. 

periences and viewpoints for what I expect will be a productive discussion. I am 

honored to be part of it. 

Given the limited amount of time, I have focused my testimony on a handful of 

areas centered on the collection, aggregation, and use of data. I am happy, however, 

to answer any other questions you may have to the best of my ability. 

I want to leave you with three main points: 

1. FinTech innovation has significant potential to improve the quality of, and access to, financial services. 

2. While there are potential risks, these risks should be judged against the status 

quo, not an unobtainable perfection. 

3. Existing law can mitigate risk to some degree, and changes to the law should 

be considered only if existing law is proven to be inadequate and the benefits 

of changing the law will outweigh the costs. 

The Potential for a Better Financial Services Market 

Changes in technology have the potential to improve the financial services markets. Specifically, the collection, use, and aggregation of consumer data may allow 

consumers to enjoy more choice, more competition, and higher-quality services. Likewise, the use of artificial intelligence, machine learning, and other advanced algorithmic techniques to process data present the possibility of more accurate, fair, and 

inclusive underwriting and risk management. 

While there are reasons to be excited, there are also potential risks. More granular data collection and broader access might increase the risk and harm of data 

breaches to consumers. There are concerns that the enhanced use of algorithms may 

lead to more discrimination, a lack of transparency, or diminished access to essential services like credit. 2 There are also fears that the existing legal and regulatory 

environment is unable to address the risks introduced by technology. 

While these concerns merit consideration and the risks they describe should be 

monitored, it is premature to panic. First, the early data are promising, in many 

cases finding that financial technology and the competition and innovation it fosters 

are improving financial services. Second, existing law and regulation might mitigate 

some of the major risks already. Although this area is often presented as a lawless 

Wild West, it is incorrect to think that these areas are unregulated. As discussed 

below, existing regulations apply, and in general, we should see how well the existing laws and regulations work with new technology before we impose new restrictions. Indeed, we should consider the possibility that, in fact, we already have too 

much regulation that affects these new technologies. Otherwise we risk forestalling 

innovations that can lead to more competitive, efficient, and inclusive financial markets—to the detriment of the American consumer. 

Data Collection 

As the Treasury Report notes, the ability of financial service providers to collect 

and utilize a broader and more diverse selection of consumer data has the potential 

to improve the provision of financial services, especially to consumers who are poorly served by the status quo. 3 Not only could cost-effective access to more data help 

established firms improve their offerings, it could also encourage competition and 

innovation from new entrants. 

While the ability to access and utilize more data has a significant upside, it also 

presents risks. For example, it is possible that the more granular a dataset a financial institution collects on a consumer, the more harm a security breach could cause. 

Data that might be relatively harmless at one level of detail could become highly 

sensitive at another. What could be labeled ‘‘professional or medical services’’ at one 

level of detail could be labeled ‘‘marriage counseling’’ at another. While obtaining 

more information could allow financial services providers to offer better products, 

we should also be alert to the risks that could develop. 

Additionally, as the Treasury Department notes, there are divergent regulations 

at the State level regarding data security and breach notification. 4 These different 

requirements can increase compliance costs for firms and result in citizens being 

regulated by sets of rules put in place without consultation with them, the conVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00045 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
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5For further discussion of the potential costs of State-by-State regulation on FinTech, including the costs of inefficiency and political inequity among citizens of different States, please see 

Brian Knight, ‘‘Federalism and Federalization on the FinTech Frontier’’, 20 Vand. J. Ent. and 

Tech. L. 129, 185–99 (2017). 6The Clearing House, ‘‘FinTech Apps and Data Privacy: New Insights From Consumer Research’’ 4 (2018). 7Screen scraping generally refers to an aggregator using a customer’s login credentials to log 

into a financial institution’s webpage on behalf of the customer and extracting data from the 

webpage. 8See, e.g., The Clearing House, ‘‘Ensuring Consistent Consumer Protection for Data Security: 

Major Banks vs. Alternative Payment Providers’’ (2015). 9Treasury Report, supra note 1, at 35–36. 

10Treasury Report, supra note 1, at 31. 11See, e.g., ‘‘NACHA, API Standardization—Shaping the Financial Services Industry’’ (2018) 

(discussing efforts by NACHA to develop standards for financial services APIs to allow interoperability). 12Treasury Report, supra note 1, at 34. 

sumers. 5 Given the predominantly interstate nature of cybersecurity, there is little 

question that Congress could constitutionally preempt State law to create consistent 

national standards, and given the costs of the status quo, it may want to consider 

doing so. 

Data Aggregation 

Third-party aggregators, acting on a consumer’s behalf, can now allow consumers 

to see all of their accounts from different financial services providers at a glance. 

This convenient display of information can help consumers more effectively assess 

and manage their finances. Third-party aggregation can also be used by applications, again acting at the request of the consumer, to collect the consumer’s financial 

data in order to allow the consumer to use the application’s service. Such applications are gaining in popularity; a recent survey conducted by the Clearing House 

found that about a third of banking customers use financial technology applications. 6

While there are real potential benefits to data aggregation, the practice is not 

without controversy. Banks and other financial institutions have expressed concern 

that data aggregators, particularly those using ‘‘screen scraping,’’ 7 place consumers’ 

data at risk and potentially expose consumers to fraud and the bank to liability. 8

As the Treasury Department’s FinTech report notes, the banks’ fears are not outlandish, as there is an open question as to the scope of the banks’ liability under 

existing law, even if the customer willingly granted access to a third party that was 

responsible for the data breach. 9

This concern is part of why section 1033 of the Dodd–Frank Act is so controversial. As the Treasury Department report notes, there is a plausible reading of the 

act (one that the Treasury endorses) that requires financial institutions covered by 

Dodd–Frank to, subject to rules promulgated by the Bureau of Consumer Financial 

Protection (Bureau), make account records available in an electronic form not only 

to consumers themselves but also to a consumer’s agent, including a FinTech application. 10 Paired with potential legal liability, this provides banks with few options 

to protect themselves. 

Understandably, this presents some significant issues that the Bureau, and potentially Congress, should consider. Among them are the following: 

• The extent of the burden placed on covered financial institutions. Must a covered 

financial institution make data available to all comers, or may it place limits 

on the basis of safety or data security? 

• The standards for data transmission. As mentioned in the Treasury Report, 

there has been a shift from screen scraping to the use of application programming interfaces (APIs) that may provide a more secure method of communicating data. However, there is not a mandatory standard that would allow 

interoperability. While there are ongoing industry efforts to bring standardization, 11 questions remain as to whether covered financial institutions must accommodate all requests and who will set standards for data transmission methods. 

• The scope of data transmission. One of the major concerns expressed by covered 

financial institutions is that data aggregators can obtain data in excess of what 

is needed to perform the service the consumer has authorized them to do. Conversely, data aggregators express frustration that financial service providers 

prevent them from accessing needed data via financial-service-provider-approved APIs. 12 While the availability of more data may allow applications to 

offer better services, it could also increase consumer harm if there were a 

43 

13The Clearing House, supra note 8, at 7. 14Treasury Report, supra note 1, at 57–8. 15 Anumpam Chander, ‘‘The Racist Algorithm?’’, 115 Mich. L. Rev. 1023, 1028 (2017). 16 Id. at 1030. 17 Id. 

breach. The scope of data that aggregators will be able to obtain from financial 

institutions, and what factors control that scope, will need to be determined. 

• Consumer control of data transmission. The amount of control consumers will 

have over the amount of data that is obtained by aggregators, and how that 

control must be exercised, will need to be determined. According to the same 

survey by the Clearing House, a majority of consumers would like to be required to provide explicit consent to any third party seeking data. 13 However, 

what that might look like in practice (e.g., when that consent must be provided 

or how granular the consent must be), and whether that standard is even practical, remain to be determined. 

• Liability for data breaches. As the Treasury Report discusses, there is a question regarding the scope of liability for a financial institution in the event consumer data is lost owing to a failure on the part of a data aggregator or a downstream application. Financial institutions feel at risk that they will ultimately 

be forced to compensate customers, even if the financial institution was not at 

fault, because the aggregator or application lacks sufficient resources to make 

aggrieved customers whole. This concern is heightened if financial institutions 

are forced to make data available to aggregators, rather than choosing to enter 

into contracts that allow the financial institutions to perform due diligence and 

make demands of the aggregator. 

If the Bureau adopts the Treasury Department’s view regarding section 1033, it 

will need to craft a rule that provides meaningful access while addressing the legitimate concerns of covered financial institutions. However, the Bureau should also 

leave as many of the details as possible to market participants so as to not impede 

innovation or risk enshrining requirements that will become outdated or suboptimal 

far faster than the regulatory process can adapt. Congress should monitor these developments to determine whether any subsequent adjustment is necessary. 

Innovative Underwriting 

As the Treasury Department notes, credit underwriting is one area where data, 

in conjunction with artificial intelligence, are being used to potentially great effect. 

There is optimism that algorithmic underwriting may increase inclusion and improve the quality of underwriting, making it more accurate and efficient. However, 

there are also concerns that it could exacerbate discrimination and exclusion, because the algorithms may exacerbate existing discrimination or be so opaque that 

humans lose the ability to discern what is driving the algorithm’s results, preventing humans from excluding improper variables. 14 These concerns are particularly acute with regard to unintentional discrimination through the use of facially 

neutral variables that nonetheless have a ‘‘disparate impact’’ on protected classes 

of persons. 

While these concerns should be taken seriously, there are also reasons to believe 

they are at least somewhat overstated. First, it must be remembered that the appropriate standard to judge innovative underwriting is not perfection. Rather, we 

should judge whether it is an improvement over the status quo. In this regard, there 

is evidence that innovative underwriting may prove to be less discriminatory than 

current practices. Second, there are reasons to believe that the current legal and 

regulatory environment for financial services may be well situated to mitigate these 

risks. 

As Professor Anupam Chander points out, there are several reasons why algorithms may prove to be less prone to discrimination than human decision making. 

To the extent that discrimination is driven by subconscious or unconscious bias, 

those biases are less likely to survive the process of being written down in an intentional underwriting algorithm compared to a ‘‘gut decision’’ by a lending officer. 15

Additionally, to the extent there is concern that algorithms may present a ‘‘black 

box’’ that cannot be audited, they nonetheless present less of a black box than the 

human mind. 16 Further, to the extent human decision making incorporates inaccurate stereotypes when making decisions, algorithms, with access to more and better data, and without the baggage of inaccurate stereotypes, may be able to do a 

better job. 17

Early evidence of the use of innovative underwriting is promising. For example, 

researchers at the Federal Reserve Banks of Chicago and Philadelphia looked at a 

44 

18See Julapa Jagtiani and Catharine Lemieux, ‘‘FinTech Lending: Financial Inclusion, Risk 

Pricing, and Alternative Information’’ (Fed. Res. Bank of Phila., Working Paper No. 17-17, 

2017); Julapa Jagtiani and Catharine Lemieux, ‘‘The Roles of Alternative Data and Machine 

Learning in FinTech Lending: Evidence From the Lending Club Consumer Platform’’ (Fed. Res. 

Bank of Phila., Working Paper No. 18-15, 2018). 19See Robert P. Bartlett, Adair Morse, Richard Stanton, and Nancy Wallace, ‘‘Consumer 

Lending Discrimination in the FinTech Era’’ (2018). 20Matthew Bruckner, ‘‘The Promise and Perils of Algorithmic Lenders’ Use of Big Data’’, 93 

Chicago-Kent L. R. 1, 38–39, 51 (2018). 21 Id. at 40. 22Tex. Dep’t of Hous. and Cmty. Affairs v. Inclusive Cmtys. Project, Inc., 135 S. Ct. 2507, 

2522 (2015). 

leading marketplace lender’s use of innovative underwriting and found that the 

lender was able to offer many borrowers better rates than they would have received 

from a traditional lender. These loans also seemed to age reasonably well, indicating 

that the underwriting did not present an undue risk of default. 18 Likewise, scholars 

at the University of California, Berkley, found evidence indicating that FinTech 

lenders using innovative underwriting for mortgages were significantly less likely 

to discriminate on the basis of race than traditional lenders. 19 While we are still 

in the early days and more research is necessary, there are good indications that 

innovative underwriting, as applied, may have significant benefits. 

Additionally, certain existing regulatory requirements may encourage firms developing innovative underwriting tools to avoid some of the concerns expressed by pessimists. For example, while there are concerns about the opacity of algorithms, the 

Equal Credit Opportunity Act and Fair Credit Reporting Act require lenders to be 

able to provide prospective borrowers with adverse action notifications explaining 

why the borrower was denied or charged a higher rate and detail the information 

the lender used to make that determination. 20 Complying with this requirement 

will be difficult if the lender’s algorithm is truly opaque, giving lenders an incentive 

to maintain auditability and explainability. 21

Further, while lenders have an economic incentive to ensure that their algorithms 

are accurate and not irrational, there are also existing regulatory reasons to do so. 

To the extent that underwriting algorithms generate lending decisions that create 

the ‘‘artificial, arbitrary, and unnecessary barriers’’ that disparate impact theory is 

meant to address, 22 the lender may, depending on the unique circumstances and 

the relevant applicable statutes, also find itself subject to liability for lending decisions that, while relying on facially neutral criteria, have a disparate impact on protected classes of borrowers, unless those decisions are driven by a legitimate business purpose and cannot be accomplished with less discriminatory means. While 

lenders have a strong profit motive to make certain their underwriting is as accurate as possible, potential liability should also encourage lenders to actively monitor 

and improve their algorithms. 

Conclusion 

The advance of technology has shown significant promise for improving the market for financial services. Specifically, the collection, aggregation, and use of consumer data has significant potential to allow consumers to enjoy the benefits of a 

more competitive and innovative market. Of course, there is no such thing as a free 

lunch, and increased risks may accompany the benefits. However, at present there 

is no reason to panic, and rash regulatory intervention may frustrate proconsumer 

innovation, leaving consumers worse off. 

Congress should carefully monitor and evaluate developments in the FinTech 

arena and intervene only when existing law and regulation—including market regulation—prove inadequate to address a problem and where the costs of intervening 

would not be worse than the problem the intervention seeks to solve. When Congress does intervene, it should do so in a technologically agnostic manner and refrain from imposing specific technical requirements on market participants because 

such solutions are likely to become obsolete in short order. 

A specific area Congress may want to monitor is whether concerns about potential 

liability are chilling innovations in underwriting that might otherwise benefit society. Congress should consider tools such as ‘‘regulatory sandboxes,’’ which can allow 

firms to experiment in a way that encourages innovation while maintaining appropriate consumer protection. While some regulators have announced their intention 

to undertake such activities under their existing authority, given the fragmented 

nature of financial regulation, it may require Congress to provide sufficient authority to allow for meaningful experiments. 

Another area Congress should consider is the question of whether the current allocation of regulatory authority regarding data security and breach notification is 

45 

1See Charles P. Kindleberger and Robert Aliber, ‘‘Manias, Panics, and Crashes: A History of 

Financial Crises’’ (2005). 2 U.S. Department of the Treasury, ‘‘Report to President Trump: A Financial System That 

Creates Economic Opportunities: Nonbank Financials, FinTech, and Innovation’’ (July 2018), 

[hereinafter, Treasury Report] available at https://home.treasury.gov/sites/default/files/2018- 

07/A-Financial-System-that-Creates-Economic-Opportunities---Nonbank-Financi....pdf. 

appropriate. As mentioned earlier, the laws governing data security and data breach 

notification, especially those at the State level, may be unduly burdening market 

participants and forcing consumers to pay for rules they had no say in. Therefore, 

Congress should consider whether establishing consistent, preemptive Federal 

standards would be appropriate. 

Technology presents the opportunity for market actors to more effectively gather, 

aggregate, and use data to provide customers with better, cheaper, and more effective financial services. While there are potential risks that should be monitored, 

there is also the potential for significant benefits. Intelligent regulatory choices, including the possibility of exercising forbearance, can help create an environment 

where consumers are able to enjoy the maximum benefits of innovation and competition while enjoying adequate protection. 

Thank you again for the invitation to testify. I look forward to your questions. 

PREPARED STATEMENT OF SAULE T. OMAROVA 

PROFESSOR OF LAW, AND DIRECTOR, JACK CLARKE PROGRAM ON LAW AND

REGULATIONS OF FINANCIAL INSTITUTIONS AND MARKETS, CORNELL UNIVERSITY

SEPTEMBER 18, 2018 

Dear Chairman Crapo, Ranking Member Brown, Members of the Committee: 

Thank you for inviting me to testify at this hearing. My name is Saule Omarova. 

I am Professor of Law at Cornell University, where I teach subjects related to U.S. 

and international banking law and financial sector regulation. Since entering the 

legal academy in 2007, I have written numerous articles examining various aspects 

of U.S. financial sector regulation, with a special focus on systemic risk containment 

and structural aspects of U.S. bank regulation. Prior to becoming a law professor, 

I practiced law in the Financial Institutions Group of Davis Polk and Wardwell. I 

also served in the George W. Bush administration as a Special Advisor on Regulatory Policy to the U.S. Treasury’s Under Secretary for Domestic Finance. I am 

here today solely in my academic capacity and am not testifying on behalf of any 

entity. I have not received any Federal grants or any compensation in connection 

with my testimony, and the views expressed here are entirely my own. 

FinTech—an umbrella term that refers to a variety of digital technologies applied 

to the provision of financial services—is by far the hottest topic in finance today. 

Recent advances in computing power, data analytics, cryptography, and machine 

learning are visibly changing the way financial transactions are conducted and financial products are used. New financial technologies promise to make transacting 

in financial markets infinitely faster, cheaper, easier to use, and more widely accessible. Reaching across generational and political lines, technology is bringing techsavvy millennials, utopian anarchists, and computer scientists into the mainstream 

debate on the future of finance, infusing it with a new sense of excitement about 

the game-changing potential of the unfolding FinTech ‘‘revolution.’’ As usual, financial markets translate these expectations into massive and rapidly growing flows of 

capital into FinTech-related ventures. 

This is, of course, not the first time in modern history that these market dynamics 

are being played out. 1 As history keeps teaching us, in such periods of rising investor optimism, it is especially critical that policymakers and regulators remain cautious, cool-headed and even-handed in their assessment of FinTech. On the one 

hand, there is no doubt that technological progress creates previously unimaginable 

opportunities for improving the functioning of financial markets and, more broadly, 

the quality of our financial lives. On the other hand, there is no guarantee that any 

of these expected benefits will, in fact, materialize—or whether they will generate 

any real long-term benefits for the Nation’s economy and society as a whole. 

In this context, it is especially commendable that the Committee is taking a closer 

look at the current state of FinTech and the current Administration’s strategic priorities in this area laid out in the U.S. Treasury Department’s recent report to 

President Trump, ‘‘A Financial System That Creates Economic Opportunities: 

Nonbank Financials, FinTech, and Innovation’’ (hereinafter, the ‘‘Treasury Report’’ 

or ‘‘Report’’). 2

46 

3Treasury Report, at 6–8. 

4 Id. at 7. 

5 Id. 

6 Id. at 8. 

At this early stage in the development and adoption of many FinTech applications, it is difficult to come up with an exhaustive list of specific policy concerns associated with each specific technology use. It is also difficult to identify the full spectrum of changes in the existing legal and regulatory regimes needed to accommodate specific uses of new technologies in financial transactions. It is both possible 

and necessary, however, to start taking a broader systemic view of FinTech and 

identifying key public policy issues arising in connection with the continuing growth 

of FinTech. 

A comprehensive analysis of the macrolevel, systemic implications of FinTech is 

provided in my new working paper, ‘‘New Tech v. New Deal: FinTech as a Systemic 

Phenomenon’’, attached separately as an Appendix hereto. In this testimony, I will 

take a broader look at a few overarching themes that arise directly out of the Treasury Report and, in my view, deserve the Committee’s special attention. 

The key point here is that the Treasury Report understates or even ignores a 

number of critically important public policy issues and concerns raised by the unfolding digital ‘‘revolution’’ in finance. My testimony identifies a few such high-level 

public policy concerns that both (1) merit full consideration by the Committee, and 

(2) are not adequately discussed or acknowledged in the Treasury Report. It is not 

intended as a detailed critique of the Treasury’s conclusions and recommendations, 

nor does it claim to analyze the full risks and benefits of any particular FinTech 

application discussed in the Report. The purpose of my testimony is to widen the 

lens beyond the seemingly value-neutral and narrowly technocratic ‘‘solutions’’—and 

to introduce the necessary note of caution with respect to potentially crucial systemic implications of the Treasury’s approach to FinTech innovation. 

The Treasury Report: The FinTech Strategy Outlined 

The Treasury Report addresses a wide range of important trends in today’s 

FinTech sector and discusses a long list of legal and regulatory challenges such 

trends present. The Treasury’s numerous conclusions and recommendations span 

across multiple issues and vary greatly in the level of specificity. The Report’s primary public policy significance, however, is that it outlines the current Administration’s strategic approach to FinTech—and, more generally, financial sector—regulation. Thus, understanding the Report’s programmatic content is the key first step 

in the process of examining FinTech as a public policy challenge. 

Underlying Narrative: FinTech as a Technical Phenomenon 

From the outset, the Treasury clearly states its view of data digitization and the 

corresponding growth in the use of digital technologies in financial and commercial 

transactions as the fundamental drivers of innovation and economic growth in the 

modern economy. 3 The Report asserts that recent advances in core computing and 

data storage capacity dramatically reduced the cost of transmitting, keeping, and 

managing financial information—thus greatly increasing operational efficiencies and 

reducing the overall cost of delivering financial services. 4 It claims further that 

digitization allows financial institutions to satisfy consumers’ and companies’ demand for increased convenience and speed of transacting and to scale up their services to reach a greater number of customers. 5

On the basis of this optimistic narrative, the Treasury concludes that ‘‘[t]he availability of capital, the large scale of the financial services market, and continued advancements in technology make accelerating innovation nearly inevitable.’’ 6 Accordingly, the Report defines the Administration’s overarching strategic policy priority 

in terms of actively facilitating the ‘‘inevitable’’ march of FinTech innovation. 

To the extent this approach conveys a basic recognition of the need to accept and 

facilitate socially beneficial technological change, the Report’s contribution is both 

timely and important. Technological progress and financial innovation, however, are 

not ‘‘natural’’ and value-neutral ‘‘win–win’’ phenomena: they have significant longterm distributional and systemic stability-related—and thus political—consequences. Technology is a tool that can be used in socially harmful ways that advance the interests of the few rather than those of the many. 

This basic fact makes it especially important to keep in mind that the Treasury’s 

conclusions and recommendations directly reflect, and are shaped by, certain fundamentally normative preferences and assumptions. These underlying normative 

choices are often hidden behind the technical idiom and deliberately technocratic 

discussions filling the Report’s 223 pages. An unbiased evaluation of the Treasury’s 

47 

7See, e.g., Id. at 17–19. 

8 Id. at 17. 

proposed FinTech strategy, therefore, requires a clear understanding of what that 

strategy actually calls for—and whose economic and political interests it prioritizes. 

Normative Baseline: Regulatory Accommodation of Private Sector Innovation 

Two principal themes run through the long list of Treasury’s recommendations: 

(1) an explicit and strong commitment to promoting private sector-led financial innovation; and (2) an implicit but equally strong commitment to minimizing regulatory 

interference with private firms’ efforts to scale up FinTech operations. These fundamentally normative choices form the basis of the Treasury’s overall FinTech strategy. 

The Treasury Report envisions financial innovation as both (1) presumptively socially beneficial; and (2) a fundamentally and inherently private sector-led initiative. 

The Report consistently emphasizes private firms’ leading role in digitization of financial data and services. Even where the Report advocates establishing ‘‘public– 

private partnerships’’ (PPP), its envisioned PPP model clearly places control over the 

nature and pace of technological change in private firms’ hands. Throughout the Report, the principal role of the Federal and State lawmakers and regulators is effectively confined to providing the necessary logistical and infrastructural support for 

private firms’ FinTech activities, while otherwise ‘‘staying out’’ of their way. 

Accordingly, the Treasury’s strategic emphasis is on ‘‘modernizing’’ the existing 

legal and regulatory regimes in order to accommodate, rather than control, the process of privately led financial innovation. In that sense, the Treasury’s normative 

stance is fundamentally deregulatory. 

Rhetorical Focus: ‘‘All About Consumers’’ 

As a rhetorical matter, the Report justifies this inherently reactive and accommodating regulatory posture by stressing that new FinTech products are (1) created 

in response to consumer demand for better financial services, and (2) offer important benefits to consumers. 7

These consumer benefits include greater speed and convenience of transacting; 

easier access to financial markets and services; and greater freedom of consumer 

choice with respect to financial products and service providers. 8 By offering these 

benefits, the Treasury’s argument goes, FinTech serves equally the interests of all 

segments of America’s population, from digitally savvy millennials to the underserved poor, from pragmatic bargain-hunters to ideological libertarians. Put simply, 

the Treasury’s argument is that all of us, ordinary consumers of retail financial 

services, are the principal beneficiaries of the proposed regulatory unshackling and 

unfettered FinTech innovation. 

This is, of course, a well-known mode of arguing consistently employed by the proponents of deregulation in the financial sector. The financial industry and its representatives have a long historical record of justifying their demands for regulatory 

easing by reference to consumer benefits. As discussed below, in the years before 

the 2008 crisis, the same rhetoric was widely used to avoid legislative or regulatory 

‘‘interference’’ with predatory subprime lending practices that were at the core of the 

unsustainable speculative asset boom and the resulting economic devastation. It is 

therefore important to contextualize the Treasury’s claims. 

Practical Focus: Relaxing Bank Regulation To Enable Certain Structural Changes 

To operationalize its programmatic goals—promoting private sector-led financial 

innovation and minimizing regulatory ‘‘interference’’ with that process—the Treasury adopts what may be viewed as a structural approach. Many of the Treasury’s 

various recommendations target, directly or indirectly, the organizational and operational ‘‘walls’’ that currently prevent or slow down FinTech companies’ full-scale 

entry into the banking sector. 

Thus, the Treasury Report strongly calls for financial regulators to ‘‘modernize’’— 

or, more precisely, to relax or remove—some of the key rules and regulations governing banking institutions’ relationships with unaffiliated technology companies. 

The unstated goal of the Treasury’s ‘‘modernization’’ strategy is to enable regulated 

banks to form large-scale de facto partnerships with technology companies, without 

subjecting the latter to bank-like oversight. 

Three examples of this deregulatory approach are particularly noteworthy. Thus, 

the Treasury Report lists a variety of specific recommendations that seek to: 

1. enable banking institutions to enter into open-ended, large-scale data-sharing 

and information-management partnerships with technology companies; 

48 

9 Id. at 73–77. 

10 Id. at 26–27. 11 Id. at 73–77. 12 Id. at 52. 13 Id. at 49. 14 Id. at 49–50. 

2. enable mutual equity investments and direct affiliations between banks and 

nonbank technology companies; and 

3. facilitate ‘‘rent-a-charter’’ arrangements allowing online marketplace lenders to 

take advantage of national banks’ exemptions from State usury laws. 

These recommendations raise a number of potentially significant public policy 

concerns that do not receive attention in the Report. In broad terms, these policy 

concerns arise in three interconnected but conceptually separate areas: 

1. consumer financial data privacy and safety; 

2. market structure and potential concentration of economic power; and 

3. systemic financial stability and economic growth 

Below, I will examine each of these high-level public policy issues—or systemic 

concerns—in the context of the three groups of Treasury recommendations outlined 

above. 

Systemic Concern Number One: Consumer Protection 

The Treasury Report advocates for a significant relaxation, if not elimination, of 

the existing rules governing banking institutions’ relationships with third-party vendors, in order to make it easier for regulated banks to form large-scale data-sharing 

and data-management partnerships with data aggregators and cloud service providers. 9

Data aggregators—or data miners—are technology companies that collect and 

‘‘share’’ (i.e., sell to interested businesses) vast amounts of online business and personal user data. So far, banking institutions have been reluctant to share their customers’ financial information—including personal bank account types and balances, 

history of late fees and charges, detailed transaction records, and so forth—with unaffiliated data aggregators. Bound by their legal and regulatory obligations to safeguard customer information handled by third-party vendors, banks typically insist 

on controlling their bilateral relationships with individual data aggregators and 

often impose unilateral restrictions on their access to banks’ customer data. 

The Treasury Report views this situation as an example of undesirable regulatory 

obstacles to financial innovation and, accordingly, calls for a concerted regulatory 

effort to allow data aggregators a greater direct access to banking customers’ financial data. The Report maintains that it is critical to ease legal and regulatory requirements that currently ‘‘hold back’’ financial institutions from entering in unrestricted data-sharing agreements with data aggregators. In particular, the Report 

calls for a universal adoption of Application Programming Interfaces (APIs) that 

would give data aggregators direct access to customer account and transaction data 

in possession of either any particular bank or all participating financial institutions. 10 Relieving banks from legal liability for third-party service providers’ handling of customer data is key to this industrywide shift to APIs that is, in turn, critical to scaling up the flow of financial information from banks to data aggregators. 11

The Treasury Report adopts the same approach to promoting large-scale 

partnering between banks and cloud computing service providers, The Treasury recommends that Federal financial regulators ‘‘modernize their requirements and guidance (e.g., vendor oversight)’’ to reduce regulatory barriers to large-scale migration 

of banks’ data and information management activities to the cloud managed by 

third parties. 12 As the Report emphasizes, facilitating a massive shift to cloud computing would ‘‘increase the speed of innovation’’ in the financial sector. 13 Enabling 

banks and other regulated financial institutions to outsource their integrated data 

management and information technology functions to large cloud service providers, 

without exposing themselves to potentially extensive liability, is critical to this industrywide shift. 14

To justify shielding banks from liability—among other things, by relaxing existing 

bank service provider regulations—the Treasury points to banks’ efficiency gains 

and their customers’ greater convenience and freedom of choice. The basic claim is 

that allowing unaffiliated tech companies to access, host, and manage bank data 

will (1) render financial services faster and cheaper for all consumers; and (2) give 

consumers unfettered control over their own financial data and their own financial 

affairs. 

49 

15See Karen Petrou, ‘‘The Crisis Next Time: The Risk of New-Age FinTech and Last-Crisis 

Financial Regulation’’ (Sept. 6, 2018), available at http://www.fedfin.com/images/stories/client—reports/FedFin%20Policy%20Paper%20on%20The%20Risk%20of%20NewAge%20Fintech%20and%20Last-Crisis%20Financial%20Regulation.pdf. 16Treasury Report, at 26. 17Petrou, supra note 15, at 3. 

There is no doubt that wholesale outsourcing of banks’ customer and enterprise 

data storage and management to specialized technology companies would greatly reduce banks’ operating costs and regulatory compliance headaches—and even enhance banks’ revenues by enabling them to charge data aggregators for direct feeds 

of their customers’ account data. It would also potentially enable individuals to access their bank accounts and other financial records via the same device they use 

for downloading music and rating restaurants. As the Report emphasizes, data-sharing through APIs would create a seamlessly integrated virtual data management 

space for individuals seeking this kind of click-through convenience. 

However, the Treasury Report ignores potentially significant public harms of allowing an industrywide wholesale migration of core bank activities and highly sensitive financial data to the cloud and/or data aggregation platforms run by third parties. What is breezily portrayed as ‘‘financial data freedom’’ for consumers, in practice, may lead to potentially irreversible erosion of consumer rights and meaningful 

freedom of choice in the financial marketplace. 

While it is difficult to present a comprehensive list of potential harms to consumers likely to result from the proposed data-sharing expansion, two basic issues 

deserve the Committee’s consideration. 

Privacy and Safety of Bank Customers’ Financial Data 

One reason for concern is that, despite the attractive rhetoric of ‘‘financial data 

freedom,’’ an easy and direct access to banking institutions’ data creates both the 

opportunity and the incentive for tech platform companies to engage in unauthorized commercial uses of bank customers’ personal data. 

Giving consumers ‘‘unfettered’’ access to their personal financial data, in the way 

advocated in the Treasury Report, would simultaneously give technology platform 

operators an equally unfettered access to the same data. These platform operators, 

however, are not regulated or supervised in the interest of consumer financial privacy as banks currently are. 15 Unlike banks, these companies are not required to 

maintain any particular levels of liquid assets or equity capital to ensure their safety and soundness. They don’t have any explicit legal obligations to make customers 

whole in case of unauthorized withdrawals of money from customers’ accounts. They 

don’t have a corps of dedicated Federal and State agency staff—such as bank examiners—monitoring closely their daily operations for compliance with the applicable 

consumer protection and business conduct standards. In other words, these companies are regular private entities seeking to maximize their own private profits in 

a free capitalist market, governed by the basic principle of ‘‘caveat emptor’’ (‘‘buyer, 

beware’’). In this sense, they are not fundamentally different from used car salesmen. 

Unlike used car salesmen, however, these tech platform companies will now be 

able to get direct access to your bank account and transaction data—and thus invisibly monitor your earnings and your expenses, your daily Starbucks coffee purchases 

and your annual political campaign contributions. That will give these professional 

information merchants an extraordinary advantage over you, the consumer. They 

will be able to ‘‘harvest’’ a valuable asset—your personal financial information— 

without paying you for it. They can then use it to make you buy the products they 

want to sell you. They can also sell your financial information to other salesmen 

who can, in turn, use it to make you buy what they want to sell you. And all of 

this ‘‘free commerce’’ can happen without your knowledge or informed consent. In 

fact, the only action required on the part of an individual to become a captive participant in this spiral of ‘‘free commerce’’ may be as simple as opening a deposit account at a local bank—and perhaps signing a boilerplate ‘‘consent’’ form. 16

If this is a plausible hypothetical, the Treasury’s proposed method of ‘‘embracing 

digitization’’ by relaxing existing regulatory constraints on banks’ data-sharing has 

to be subjected to the strictest scrutiny. Instead of giving consumers meaningful ‘‘financial data freedom,’’ it would give a massive gift of ‘‘free financial data’’ to data 

aggregators, cloud providers, various FinTech companies, and other businesses set 

up to capitalize on it. This is a deeply troubling prospect. As a recent study found, 

‘‘the FinTech ecosystem is predicated on little to no privacy protections for consumer 

data housed outside regulated financial institutions.’’ 17 But it is also intuitively 

50 

18See Emily Glazer et al., ‘‘Facebook to Banks: Give Us Your Data; We’ll Give You Our 

Users’’, Wall St. J. (Aug. 6, 2018). 19Treasury Report, at 38. 20 Id. at 39. 21See Petrou, supra note 15, at 5. 22See https://www.ftc.gov/equifax-data-breach. 23 ‘‘Gartner Says Worldwide IaaS Public Cloud Services Market Grew 29.5 Percent in 2017’’, 

Press Release (Aug. 1, 2008), available at https://www.gartner.com/en/newsroom/press-releases/2018-08-01-gartner-says-worldwide-iaas-public-cloud-services-market-grew-30-percent-in2017. 24 Id. 25Treasury Report, at 23. 26See, e.g., John M. Newman, ‘‘Digital Antitrust’’ (June 22, 2018), available at https://papers.ssrn.com/sol3/papers.cfm?abstractlid=3201004; Lina Khan, ‘‘Amazon’s Antitrust Pareasy to understand the obvious dangers of allowing large tech platform companies 

such an easy access to bank customers’ personal financial data. A strong public reaction to the recent news of Facebook—one of the world’s largest and most notorious 

data aggregators—requesting access to large banks’ customer data shows that consumers care deeply about keeping their financial information private, safe, and secure from all manner of unauthorized use. 18

The Treasury Report does not address the heightened risk of unauthorized commercial uses of consumer data by tech platforms allowed to access it. Instead, it confines the discussion to issues of data security, or unauthorized access to data. 

While acknowledging the importance of data protection in general terms, the Report generally seems content leaving the necessary adjustments to the private sector. Thus, it refers to the fact that the Federal Trade Commission (FTC) imposes 

certain information security requirements on data aggregators that are ‘‘significantly engaged in financial services,’’ and are therefore subject to its so-called Safeguards Rule. 19 In the Treasury’s view, that rule ‘‘appropriately addresses’’ all concerns about the security of customers’ financial information managed by data 

aggregators and other FinTech firms. 20 Accordingly, the conclusion is that no further legislative or regulatory action is needed in order to bolster consumer data protection. It is not clear, however, to what extent the FTC’s Safeguards Rule is sufficiently effective in practice. The Rule may not even apply to giant platform conglomerates whose financial activities do not technically constitute a ‘‘significant’’ portion 

of their overall operations. 21 Moreover, a recent massive data security breach at 

Equifax, which affected over 143 million people, is a vivid example of what can happen even on the FTC’s watch. 22

Of course, any meaningful discussion of data security has to address the critical 

issue of apportioning liability for security breaches. While the Treasury acknowledges the importance of this issue, it does not provide a clear answer to the fundamental question: Who will be liable to the consumer whose bank account is hacked? 

It seems clear that, as a practical matter, the only way banks would be willing to 

share their customer data with tech platforms is if they are not held liable for the 

platform operators’ failures to protect the data. But, if banks are not liable, then 

who is going to make the account holder whole? Unless this question has a clear— 

and satisfactory—answer, the notion of ‘‘facilitating innovation’’ through unrestricted data-sharing is inimical to the objective of protecting consumers’ interests. 

Predatory and Discriminatory Pricing of Financial Services 

The Report’s rhetoric of consumer choice and financial data freedom implies the 

existence of a perfectly competitive and transparent market in which individual consumers have the power to choose the best FinTech service provider. Reality, however, is far more complicated and a lot less benign. 

In particular, the market for cloud computing and data analytics is both highly 

concentrated and inherently opaque. Only four megatech companies currently dominate the worldwide market for cloud services: Amazon, Microsoft, Alibaba, and 

Google. 23 These four ‘‘hyperscale’’ service providers hold approximately 73 percent 

of the global cloud infrastructure services. 24 Apple, Amazon, Google, Microsoft, and 

Facebook—five of the largest publicly traded U.S. companies by market capitalizations—are the pioneers of megascale data aggregation and ‘‘integral drivers of the 

digital economy’’ as a whole. 25 Even though the Treasury Report refers to data 

aggregators and cloud service providers in generic terms, it is these megacompanies 

that define the dynamics in the tech sector. 

It is no coincidence that today’s giant technology conglomerates are aggressively 

growing, diversifying, and continuously expanding their market shares. As recent 

studies show, this constant quest for size and market power is the built-in economic 

imperative in this business so intimately dependent on network effects. 26 These 

51 

adox’’, 126 Yale L. J. 710 (2017); Frank Pasquale, ‘‘Paradoxes of Digital Antitrust’’ (2013), available at https://jolt.law.harvard.edu/assets/misc/Pasquale.pdf. 27 Alberto Cavallo, ‘‘More Amazon Effects: Online Competition and Pricing Behaviors’’, Harvard Business School and NBER (Aug. 10, 2018), available at https://kansascityfed.org// 

media/files/publicat/sympos/2018/papersandhandouts/825180810cavallopaper.pdf?la=en. 28David Dayen, ‘‘Does Amazon Have More Power Than the Federal Reserve?’’ New Republic 

(Aug. 28, 2018), available at https://newrepublic.com/article/150938/amazon-power-federal-reserve. 29 Id.; Rana Foroohar, ‘‘Amazon’s Pricing Tactic Is a Trap for Buyers and Sellers Alike’’, 

FT.Com (Sept. 2, 2018). 30See Petrou, supra note 15, at 4. 31See Foroohar, supra note 29. 32See Renae Merle, ‘‘Trump Administration Strips Consumer Watchdog Office of Enforcement 

Powers in Lending Discrimination Cases’’, Wash. Post (Feb. 1, 2018), available at https:// 

www.washingtonpost.com/news/business/wp/2018/02/01/trump-administration-strips-consumer-watchdog-office-of-enforcement-powers-against-financial-firms-in-lending-discriminationcases/?utmlterm=.4c83cde19b28. 

companies’ critical reliance on complex proprietary analytical tools renders their 

business models, and the markets in which they operate, fundamentally nontransparent. Put simply, nobody really knows what exactly these companies can see or 

what they can do with the data they touch. 

In this context, the Treasury’s proposed strategy of enabling megatech companies 

to ‘‘get inside’’ banks’ customer data raises a number of significant consumer protection concerns. If that happens, the dominant players in the financial data and services market will be perfectly positioned to abuse their enormous market power, 

among other things, by engaging in predatory or unfair pricing of financial products 

and consumer discrimination. 

The basic blueprint for such abuses is already there. For example, Amazon’s unprecedented market power in online commerce and command of digitized consumer 

data enable it to adjust its prices almost instantaneously, in response to fluctuations 

in current demand for specific goods. 27 For example, if more people are buying a 

particular brand of baby food in the morning, Amazon can raise its price by noon. 28

This type of ‘‘dynamic pricing’’ is difficult for any outsider to detect, as only Amazon 

has control of its algorithms and data. This algorithmic opacity makes consumers 

extremely vulnerable to predatory or unfair pricing, and not only by Amazon but 

also by other companies widely emulating its practices. 29

In the context of financial services, this technical capacity for nontransparent ‘‘dynamic pricing’’ can easily translate into the highly questionable practice of ‘‘microtargeting’’ consumers. Amazon, Google, and other FinTech companies will be able 

to use the vast amounts of data gained from monitoring consumers’ behavioral patterns and commercial transactions—and now the detailed real-time bank account 

data—to ‘‘up-price’’ financial products and services offered to individual consumers. 30 In essence, they will be able to charge individual borrowers not the fair 

market price but the maximum price each of them is able to pay. 

This microtargeting may be presented to the public under the benign guise of 

‘‘product customization.’’ In practice, however, it will effectively destroy consumers’ 

ability to make informed decisions and to gauge whether they are being overcharged, underserved, or even entirely excluded from certain product markets. The 

opacity of the pricing process, the service provider’s control of the customer’s data, 

and the practical difficulty of switching providers will fundamentally skew the balance of power in favor of the service provider. 31

Importantly, the same factors will also make it difficult, if not impossible, for any 

regulatory agencies to detect and punish abusive behavior in financial markets. The 

growing deficit of regulatory capacity is likely to leave consumers to fend for themselves—precisely at a time when they acutely need Government protection. This is 

particularly poignant, given the current efforts to weaken the Bureau of Consumer 

Financial Protection and to limit its enforcement capabilities. 32

In sum, simply relaxing existing bank regulations in order to allow wholesale migration of the highly sensitive and valuable financial information currently controlled by banks to data aggregators, cloud providers, and other FinTech companies 

would expose consumers to potentially massive data privacy and safety risks. Rather than gaining meaningful control over their personal financial data, American consumers will be an easy target for unscrupulous salesmen of the digital era. A prudent public policy approach to safe and secure financial data-sharing in the digital 

age requires a deeper and more balanced analysis of these risks, as well as the 

means of preempting them. 

52 

33See Bernard Shull, ‘‘Banking and Commerce in the United States’’, 18 J. Banking and Fin. 

255 (1994); Bernard Shull, ‘‘The Separation of Banking and Commerce in the United States: an 

Examination of the Principal Issues’’, 8 Fin. Markets, Inst. and Instr. 1 (Aug. 1999). 34 12 U.S.C. §24 (Seventh). 35 12 U.S.C. §§1841-43. 36See Saule T. Omarova, ‘‘The Quiet Metamorphosis: How Derivatives Changed the ‘Business 

of Banking’ ’’ 63 U. Miami L. Rev. 1041 (2009); Saule T. Omarova, ‘‘The Merchants of Wall 

Street: Banking, Commerce, and Commodities’’, 98 Minn. L. Rev. 265 (2013). 37 Id. 38 12 U.S.C. §1843(k). 39Treasury Report, at 80. 40 Id. 41 12 U.S.C. §1841(a). 42Treasury Report, at 80. 

Systemic Concern Number Two: Structural Shifts in the Economy 

Under the headings of ‘‘aligning’’ and ‘‘modernizing’’ the regulatory framework, 

the Treasury Report makes a number of specific recommendations intended to remove or relax the existing restrictions on permissible business activities and organizational affiliations of banking organizations. While framed as a narrowly technical 

issue, this effort goes directly to the long-standing U.S. policy of separation of banking from commerce. It also raises a broader spectrum of concerns related to potentially far-reaching structural shifts in the U.S. economy. 

The principle of separation of banking and commerce is one of the core principles 

underlying and shaping the elaborate regulatory regime applicable to all U.S. banking organizations. 33 Under the National Bank Act of 1863, U.S. commercial banks 

generally are not permitted to conduct any activities that fall outside the statutory 

concept of ‘‘the business of banking.’’ 34 Moreover, under the Bank Holding Company 

Act of 1956 (the BHC Act), bank holding companies (BHCs)—companies that own 

or ‘‘control’’ U.S. banks—are generally restricted in their ability to engage in any 

business activities other than banking, managing banks, or certain activities ‘‘closely 

related’’ to banking. 35

Since the 1980s, the scope of banks’ and BHCs’ permissible activities has been 

steadily and gradually expanding. 36 The Office of the Comptroller of the Currency 

(OCC) has been especially aggressive in its interpretations of the statutory term 

‘‘business of banking’’ to allow banks to engage, among other things, in data storage 

and certain software-related activities. 37 In 1999, Congress passed the Gramm– 

Leach–Bliley Act (the GLB Act), which partially repealed the Glass–Steagall Act 

and authorized certain qualifying BHCs to become ‘‘financial holding companies’’ 

(FHCs) and to conduct a wide range of financial and even some commercial activities. 38

These developments notwithstanding, however, U.S. banks’ and BHCs’ activities, 

investments, and organizational affiliations remain subject to significant limitations. 

Citing with approval the OCC’s aggressively expansive approach, the Treasury Report recommends that all banking regulators interpret banking organizations’ scope 

of activities ‘‘in a harmonized manner as permitted by law wherever possible and 

in a manner that recognizes the positive impact that changes in technology and data 

can have in the delivery of financial services.’’ 39

The Treasury also recommends that the Federal Reserve ‘‘consider how to reassess’’ the definition of ‘‘control’’ in the BHC Act, in order to make it easier for banking institutions and FinTech companies invest in each other’s equity. 40 The BHC 

Act defines ‘‘control’’ in deliberately broad terms: in addition to specifying a quantitative threshold (direct or indirect ownership of 25 percent or more of any class 

of voting securities), it grants the Federal Reserve discretion to make the requisite 

findings of ‘‘controlling influence’’ in a wide range of circumstances. 41 The Treasury 

Report criticizes the Federal Reserve’s accumulated interpretations of ‘‘control’’ as 

‘‘not sufficiently transparent’’ and thus discouraging—instead of facilitating—the 

formation of extensive business partnerships and close organizational relationships 

between BHCs and FinTech companies. The practical worry here is that unregulated technology companies may be deemed either to ‘‘control’’ a U.S. bank or to be 

‘‘controlled’’ by a BHC—and thus subject to the BHC Act’s activity restrictions and 

supervisory oversight. 42

Although the Treasury does not explicitly direct the Federal Reserve to adopt any 

specific definition of ‘‘control,’’ the main thrust of its recommendation is clear: a 

properly ‘‘modernized’’ definition should be significantly narrowed and uniformly applied. In contrast to the Treasury’s usual calls for ‘‘tailored’’ FinTech regulation, the 

Federal Reserve’s tailoring of ‘‘control’’ determinations to the circumstances of each 

individual case is deemed undesirable as hindering bank partnerships with and acquisitions of (and by) nonbank technology companies. 

53 

43See Omarova, ‘‘The Merchants of Wall Street’’, supra note 36, at 274–278. 44See E. Gerald Corrigan, ‘‘Are Banks Special?’’ 1982 Fed. Res. Bank of Minn. Ann. Rep., 

available at http://www.minneapolisfed.org/pubs/ar/ar1982a.cfm. For a systematic exposition 

of banks’ special function as sovereign public’s ‘‘franchisees,’’ see Robert C. Hockett and Saule 

T. Omarova, ‘‘The Finance Franchise’’, 102 Cornell L. Rev. 1143 (2017). 45See Arthur E. Wilmarth, Jr., ‘‘The Transformation of the U.S. Financial Services Industry, 

1975–2000: Competition, Consolidation, and Increased Risks’’, 2002 U. Ill. L. Rev. 215 (2002). 

Separation of Banking and Commerce 

Adopting a systematic policy of aggressively pushing the legal and statutory 

boundaries of bank-permissible business activities and affiliations, as advocated by 

the Treasury, will significantly undercut—if not completely incapacitate—the operation of the foundational U.S. principle of separation of banking and commerce. In 

this sense, it will weaken the overall integrity and efficacy of the U.S. bank regulation and supervision. 

It is important to remember why the entire system of U.S. bank and BHC regulation is designed to keep institutions engaged in deposit-taking and commercial lending activities from conducting, directly or through some business combination, any 

significant nonfinancial activities, or from holding significant interests in any general commercial enterprise. There are three main public policy reasons for maintaining this legal wall between the ‘‘business of banking’’ and purely commercial businesses: (1) preserving the safety and soundness of federally insured depository institutions; (2) eliminating potential conflicts of interest and ensuring a fair and efficient flow of credit to productive economic enterprise; and (3) preventing excessive 

concentration of financial and economic power in the financial sector. 43

Of course, each of these traditional concerns may be more or less pronounced in 

the context of a particular commercial activity. It is also clear that banks’ involvement in certain nonfinancial activities may—and often does—produce financial benefits to their clients and, indirectly, to society as a whole. Yet, after decades of unquestioning acceptance of private firms’ self-interested depiction of such benefits, it 

is critical that policymakers fully address and appreciate potential social costs of 

mixing banking and commerce—especially, digital commerce. 

The key point here is simple: allowing banks and BHCs to form wide-ranging 

business partnerships with technology firms—either through global contractual arrangements or through outright combinations—would critically undermine all of the 

public policy goals at the heart of the U.S. bank regulation. 

For example, it would expose banking institutions to a wide variety of nontypical 

and potentially excessive economic, operational, and legal risks associated with tech 

companies’ rapidly evolving commercial activities. Banks are ‘‘special’’ business actors in that they perform critical public functions, enjoy direct public support, and 

are inherently vulnerable to runs that can trigger systemic financial crises. For 

these reasons, banks’ safety and soundness remains the cornerstone of bank regulation and supervision. 44 Expanding banking entities’ economic activities to encompass global e-commerce, ‘‘big data’’ management, and AI development will diversify 

and magnify not only their potential revenues but also their potential losses and 

vulnerabilities. It will also render banking organizations’ internal governance and 

regulatory oversight far more challenging, if not outright impossible, propositions. 

Furthermore, it would give rise to new patterns of conflicts of interest, potentially 

systematic misallocation of credit, and other cross-sectoral abuses of market power. 

Some of these abuses of market power are discussed above, in the context of consumer protection. However, this type of bank-tech conglomeration would also pose 

an immediate and tangible threat to all other businesses, especially those competing 

with banks’ technology affiliates or partners. These types of structurally determined 

distortion in the economywide credit flows would critically impede economic growth 

and cause a host of socio-economic and political problems. 

Market Structure, Antitrust, and ‘‘Too Big To Fail’’ Concerns 

Perhaps the most far-reaching potential consequence of opening the door for direct 

cross-sectoral acquisitions and affiliations between banking institutions and tech 

firms is the dangerous increase in the overall concentration of the economic and political power likely to result from it. 

The U.S. financial services industry is already heavily concentrated. The passage 

of the GLB Act, which officially removed the long-standing prohibition on affiliations 

between commercial and investment banks, has elevated the pace of industry consolidation to a qualitatively new level. 45 The level of industry concentration increased further in the wake of the global financial crisis of 2008, so that the top 

54 

46https://fred.stlouisfed.org/series/DDOI06USA156NWDB 47See NAFCU, ‘‘Modernizing Financial Services: The Glass–Steagall Act Revisited’’ (2018), at 

14, available at http://stilltoobigtofail.org/wp-content/uploads/2018/09/Glass-Steagall-ActWhite-PaperlR4.pdf. 48See Matt Phillips, ‘‘Apple’s $1 Trillion Milestone Reflects Rise of Powerful Megacompanies’’, 

N.Y. Times (Aug. 2, 2018). 49See id.; Lina M. Khan, ‘‘Sources of Tech Platform Power’’, 2 Geo. L. Tech. Rev. 325, 326 

(2018). 50See David Streitfeld, ‘‘Amazon Hits $1,000,000,000,000 in Value, Following Apple’’, N.Y. 

Times (Sept.4, 2018). 51See Phillips, supra note 48. 52See Omarova, ‘‘The Merchants of Wall Street’’, supra note 36, at 276–277. 53See Matt Egan, ‘‘Too-Big-To-Fail Banks Keep Getting Better’’, CNN Money (Nov. 21, 2017), 

available at https://money.cnn.com/2017/11/21/investing/banks-too-big-to-fail-jpmorgan-bankof-america/index.html. 

five banks in the U.S. now control approximately half of all assets in the sector. 46

Large BHCs control over 80 percent of all banking assets. 47

The same trend is strongly evident in the tech sector. Despite the great number 

and diversity of what we call ‘‘technology’’ companies, a few giants at the core of 

the tech industry undoubtedly dominate it. Thus, only two companies, Apple and 

Google, currently provide the software for 99 percent of all smartphones, the indispensable devices for mobile payments. 48 Facebook and Google capture between 59 

and 73 cents of every dollar spent on online advertising in the U.S. 49 Amazon takes 

49 cents of every e-commerce dollar in the U.S. 50 This dominance is clearly reflected in the stock markets. Earlier this year, both Apple and Amazon exceeded $1 trillion 

in market capitalization. And the largest tech companies—including Apple, Amazon, 

Facebook, and Google—lead the longest stock market rally in decades. 51

It is against this background that the Treasury Report’s seemingly low-key, technocratic recommendation to ‘‘correct’’ or ‘‘clarify’’ a specific regulatory interpretation 

of the statutory definition of ‘‘control’’ in the BHC Act should be evaluated. 

The existing body of the Federal Reserve’s interpretations of what constitutes 

‘‘control’’ for purposes of the BHC Act is fundamentally fact-driven and thus inevitably complex. While that may complicate private firms’ efforts to structure their 

investments so as to avoid being subject to the BHC Act, it preserves the necessary 

flexibility enabling the Federal Reserve to safeguard the principles underlying the 

Act. This is especially critical in light of the fact that the BHC Act was originally 

designed to operate as an antitrust, antimonopoly law. 52

By contrast, what the Treasury calls ‘‘a simpler and more transparent standard 

to facilitate innovation-related investments’’ would effectively enable large U.S. financial holding companies to take significant equity stakes in various FinTech ventures, alongside large tech companies. It would also enable the tech giants to acquire significant equity stakes in U.S. banks and BHCs of varying sizes, without 

becoming subject to BHC regulation. The Treasury Report carefully frames its recommendations to create an impression that such a regulatory pullback would make 

financial markets more efficient and competitive by enabling a myriad of small investments by a myriad of banks in a myriad of competing tech companies—and vice 

versa. What remains unsaid, however, is that the dominant players in both markets—including JPMorgan Chase, Citigroup, Bank of America, Goldman Sachs, 

Morgan Stanley, Wells Fargo, Facebook, Amazon, Google, Apple, Microsoft, and 

IBM—will also be able to take advantage of such explicitly permissive regulatory 

standards. Given the importance of scale and network effects for both tech platforms 

and financial institutions, they will be remiss not to. 

Thus, in practice, ‘‘simplifying’’ the Federal Reserve’s interpretation of the BHC 

Act’s ‘‘control’’ requirements for purposes of ‘‘facilitating FinTech innovation’’ is likely to trigger a wave of unprecedented cross-sectoral consolidation. Because of the 25 

percent threshold built into the BHC Act’s definition of ‘‘control,’’ this new-generation consolidation wave will likely take new transactional forms, potentially resulting in a Byzantine system of corporate ownership and de facto management interlocks. In this web of formal and informal corporate control linkages, detecting and 

punishing collusive behavior and other abuses of market power will be even more 

difficult than it is today. 

One additional point bears emphasis here. In both sectors, companies’ size and 

market share are key to profitability and success. In the financial sector, the quest 

for scale and scope is also driven by the presence of the bank public subsidy. The 

well-known phenomenon of ‘‘too big to fail’’—a de facto suspension of market discipline with respect to systemically important entities—presents one of the greatest 

public policy challenges in the financial sector. 53 Drastically curtailing the regime 

of separation of banking from commerce would facilitate a potentially massive transVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00058 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
55 

54See Omarova, ‘‘The Merchants of Wall Street’’, supra note 36, at 349–351; Julie Cohen, 

‘‘Technology, Political Economy, and The Role(s) of Law’’ (June 8, 2018), available at https:// 

lpeblog.org/2018/06/08/technology-political-economy-and-the-roles-of-law/. 55See generally Rana Foroohar, ‘‘A Light Shines on the Concentration of Power in Silicon Valley’’, FT.Com (July 22, 2018); Buttonwood, ‘‘Political Power Follows Economic Power’’, Economist.com (Feb. 3, 2016), available at https://www.economist.com/buttonwoods-notebook/2016/ 

02/03/political-power-follows-economic-power. 56Madden v. Midland Funding, LLC, 786 F. 3d 246 (2d Cir. 2015). 57See Michael S. Barr, et al., ‘‘Financial Regulation: Law and Policy’’ 185 (2nd ed., 2018). 58For a discussion of why bank charters are special and different from regular corporate charters, see Robert C. Hockett and Saule T. Omarova, ‘‘ ‘Special’, Vestigial, or Visionary? What 

Bank Regulation Tells Us About the Corporation—and Vice Versa’’, 39 Seattle U. L. Rev. 453 

(2016). 59See Consumer Federation of America and U.S. Public Interest Research Group, ‘‘Rent-ABank Payday Lending: How Banks Help Payday Lenders Evade State Consumer Protections’’ 

(Nov. 2001), available at https://consumerfed.org/pdfs/paydayreport.pdf. 60See, e.g., Kathleen C. Engel and Patricia A. Mccoy, ‘‘The Subprime Virus: Reckless Credit, 

Regulatory Failure and Next Steps’’ (2011). 

fer of banks’ public subsidy to the tech sector. In that sense, it is virtually guaranteed to take the ‘‘too big to fail’’ problem to an entirely different—perhaps even unimaginable—level. In the next crisis, the sheer scale of the Government bailouts required to keep the hypersized FinTech conglomerates from failing might make the 

taxpayer cost of saving Wall Street in the last one look like small change. 

Of course, money is not the only thing that matters to the American public in this 

scenario. The increasing concentration of economic power in a small club of corporate giants is a direct threat to American democracy. 54 It perpetuates and exacerbates deep socio-economic inequality, which inevitably undermines political order 

premised on ideals of equal participation and voice. Big corporations’ ability to ‘‘buy’’ 

political influence fundamentally corrupts political process and corrodes public confidence in the democratic system as a whole. 55 This is an unacceptably high societal 

price for the personal convenience of accessing one’s bank accounts and digital wallets via a single iPhone click. 

In sum, it is critical to keep in mind that, without proactive and appropriately 

applied public oversight, data digitization, cloud computing, and other seemingly 

value-neutral and science-driven FinTech innovations may operate as hidden channels for the formation of economywide FinTech platform conglomerates. 

Systemic Concern Number Three: Financial Stability and Economic 

Growth 

The Treasury Report uses a direct reference to the ‘‘bank partnership model’’ in 

its discussion of marketplace lending. Among other things, the Treasury makes a 

very specific recommendation for Federal legislation overruling the Second Circuit’s 

decision in Madden v. Midland Landing LLC, which held that the National Bank 

Act did not preempt State usury rules with respect to the interest charged by a 

third-party nonbank purchaser of loans from a national bank. 56

The Madden decision directly affects marketplace lenders operating under the socalled rent-a-charter model, in which the online lender markets the loans and runs 

its proprietary algorithms but the actual loan is initially extended and funded by 

a chartered bank. The bank typically holds the loan for a few days and then sells 

it back to the online lender. 57 In effect, the online lender buys the originating 

bank’s ability to ‘‘export’’ its home-State’s favorable (or nonexistent) usury rate nationwide. In this sense, the bank is ‘‘renting out’’ its bank charter—or, more accurately, selling a special legal privilege the Government grants exclusively to chartered banks—to an entity that does not qualify for a bank charter and is not entitled to any privileges that come with it. 58

The ‘‘rent-a-charter’’ model is not a recent invention; it was widely used by predatory payday lenders and subprime mortgage companies in the run-up to 2008. 59 At 

the time, Federal bank regulators did not interfere with this unseemly charter-arbitrage practice in the name of promoting ‘‘financial innovation,’’ ‘‘freedom of consumer choice,’’ and ‘‘access to credit’’ for high-risk/low-income borrowers. The OCC’s 

aggressive Federal preemption strategy, the Federal Reserve’s laxity, and the absence of a dedicated Federal financial consumer protection agency contributed to the 

rampant growth of subprime debt that ultimately triggered a major financial crisis. 60

In this context, the Treasury’s insistence that Congress legislatively overrule 

Madden brings into bold relief the broader concerns about systemic financial stability and the threat of recurring financial crises. All too often, the familiar rhetoric 

of ‘‘facilitating consumer access to cheap credit’’ obscures the underlying systemwide 

dynamics that drive the emergence and growth of specific ‘‘innovations.’’ The TreasVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00059 Fmt 6621 Sfmt 6621 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
56 

61For an in-depth analysis of the systemic significance of FinTech, see Saule T. Omarova, 

‘‘New Tech v. New Deal: FinTech As a Systemic Phenomenon’’, 36 Yale J. Reg. (forthcoming 

2019), available at https://papers.ssrn.com/sol3/papers.cfm?abstractlid=3224393. 62See generally Engel and McCoy, supra note 60; ‘‘Fin. Crisis Inquiry Comm’n, The Financial 

Crisis Inquiry Report: Final Report of the National Commission on the Causes of Financial and 

Economic Crisis in the United States’’ (2011), https://www.gpo.gov/fdsys/pkg/GPO-FCIC.pdf; 

S. Permanent Subcomm. on Investigations, 112th Cong., ‘‘Wall Street and the Financial Crisis: 

Anatomy of a Financial Collapse’’ (2011), http://hsgac.senate.gov/public/lfiles/FinanciallCrisis/FinancialCrisisReport.pdf. 63 Id. 64Testimony of Sandy Samuels, Senior Managing Director and Chief Legal Officer of Countrywide Financial Corporation and the Housing Policy Council of the Financial Services Roundtable 

before the Subcommittees on Financial Institutions and Housing, U.S. House of Representatives 

ury Report’s normatively inflected rhetoric also diverts attention from the significant potential impact of proposed deregulatory measures on the financial markets 

as a whole. To avoid repeating the costly mistakes of the pre-2008 period, therefore, 

policymakers must look behind the Report’s technocratic gloss and examine FinTech 

developments from a systemic, public interest-driven perspective. 

Financial Asset Speculation in the Digitized Marketplace 

Contrary to the Treasury Report’s baseline narrative, FinTech is not simply a 

matter of applying computer and information science to financial transactions and 

finding ‘‘win–win’’ technical solutions to various market ‘‘frictions.’’ It is trivially 

true that new technological tools are designed to make financial transactions faster, 

cheaper, and easier to use and adjust to transacting parties’ individual needs and 

preferences. But that is only part of the story. The rise of FinTech is an integral 

part, and a logical stage in the development, of the broader financial system. Therefore, FinTech’s overall normative significance cannot be simply postulated on the 

basis of its intended microtransactional efficiencies. It has to be assessed in the context of the financial system’s stability and ability to perform its core social function: 

effectively and reliably channeling capital flows to their most productive uses in the 

real, i.e., nonfinancial, economy. 61

From this systemic perspective, the rapid digitization of data and financial services presents a far more complex public policy challenge than the Treasury Report 

is willing to acknowledge. FinTech innovations are driven not only—and perhaps 

not even mainly—by the financial institutions’ and tech companies’ desire to improve retail financial services. Despite the consumer-centric rhetoric surrounding 

FinTech, digital technologies are likely to have their greatest systemic impact in the 

highly volatile and speculative secondary financial markets dominated by professional traders, dealers, and institutional investors. Fixing the focus of policy discussions on the expected benefits of FinTech to retail consumers, however, diverts attention from potentially crucial developments in wholesale financial markets. It accordingly creates a dangerous blind spot for policymakers and regulators. 

The pre-2008 subprime mortgage and securitization boom provides a vivid illustration of just how dangerous it can be. It is well-known that the rapid growth of 

risky subprime mortgage lending in the early 2000s—a predominantly retail market 

phenomenon—was fundamentally driven by the insatiable demand on the part of 

yield-hungry institutional investors for tradable asset-backed securities. Subprime 

mortgage loans served as the perfect raw material for the creation of high-yielding 

yet highly (and wrongly) rated mortgage-backed securities (MBS), collateralized debt 

obligations (CDOs), and other complex structured products. 62 As speculative demand for these products grew, mortgage lenders used increasingly deceptive and 

discriminatory tactics to generate greater volumes of such raw material, among 

other things, by targeting the most vulnerable borrower populations. 63

Ironically, in the public arena, these predatory subprime loans were often touted 

as a great benefit for low-income borrowers. This is how a senior executive of now 

infamous Countrywide Financial described his company’s subprime lending activities to Congress in early 2004, a year in which some of the worst subprime mortgages were originated: 

‘‘[ . . . ] Countrywide entered the nonprime lending market in 1996 as part 

of our effort to make homeownership possible for the largest number of 

American families and individuals. We believed then, as we believe now, 

that nonprime lending is a natural extension of our commitment to bring 

Americans who have traditionally been outside mainstream mortgage markets into their first homes. Our nonprime lending programs also have 

helped these families and individuals build equity and use this equity to 

send their children to colleges, start their own businesses, and gain control 

over their financial destiny.’’ 64

57 

(March 30, 2004), available at https://www.gpo.gov/fdsys/pkg/CHRG-108hhrg94689/pdf/ 

CHRG-108hhrg94689.pdf. 65 Id. 66See Robert C. Hockett, ‘‘Accidental Suicide Pacts and Creditor Collective Action Problems’’, 

98 Cornell L. Rev. 55 (2013). 67For a detailed discussion, see Omarova, supra note 61. 68 Id. 

‘‘Nonprime products give borrowers more choices and make credit more 

readily available, because we and other lenders can price according to the 

level of risk.’’ 65

Millions of Americans who either lost their homes in the crisis or are forced to 

carry the heavy burden of underwater mortgage debt would strongly disagree. 66

In reality, of course, Countrywide flooded the market with risky loans not because 

it cared for its poor borrowers’ economic rights, but because it was reaping huge 

profits in the wholesale securitization markets. Its executive’s remarkably self-serving statements illustrate how the financial industry used—indeed abused—consumers not only as the unwitting captive source of fuel for its high-stakes speculation game, but also as the ‘‘sympathetic beneficiary’’ legitimizing and shielding that 

game from public scrutiny. 

Today, similar consumer-centric rhetoric is being deployed to justify various deregulatory moves, among other things, in the context of FinTech innovation. It is, 

of course, too early to draw definitive conclusions as to what exactly this rhetoric 

may be obscuring from policymakers’ and the broader public’s view. The recent history tells us, however, that whenever a powerful private industry demands deregulation in the name of consumers’ ‘‘freedom of choice’’ or ‘‘access to credit,’’ something 

a lot bigger and much less altruistic is driving these demands. It is, therefore, both 

timely and necessary to start identifying some of the ways in which FinTech is likely to impact the ‘‘big-picture’’ issues related to systemic financial stability. 

The basic point here is simple: In the current environment of global investment 

capital glut, the rapid digitization of financial data and transactions is bound to amplify the underlying structural incentives for excessive speculation in secondary 

markets for financial instruments. By making financial transactions infinitely faster, cheaper, and easier to use and to customize, FinTech innovations potentially empower wholesale market participants to engage in financial asset speculation on an 

unprecedented level. Armed with new digital tools, financial and FinTech firms will 

be able to synthesize potentially endless chains of virtual assets, tradable in potentially infinitely scalable virtual markets. This FinTech-driven qualitative growth in 

the volume and velocity of speculative trading, in turn, potentially amplifies the financial system’s vulnerability to sudden shocks and cascading loss effects. In short, 

a fully digitized and frictionless financial marketplace is bound to grow not only 

much bigger and faster but also more complex, opaque, and volatile. 67

It is worth emphasizing that advances in technology are increasingly enabling private market participants to create tradable cryptoassets effectively out of thin air. 

These cryptoassets—digital tokens or bits of data representing some value—can 

have such an attenuated connection to productive activity in the real economy as 

to be practically untethered from it. By potentially rendering the financial system 

entirely self-referential, this type of unchecked private sector ‘‘innovation’’ can fundamentally undermine—rather than promote—the long-term growth on the part of 

the American economy. On a macrolevel, therefore, the key risk posed by FinTech 

lies in its—still not fully known—potential to exacerbate the financial system’s dysfunctional tendency toward unsustainably self-referential growth. 68 (For a detailed 

discussion of these and related issues, see Appendix to this testimony.) 

Regulatory and Supervisory Capacity 

Understanding some of the potentially destabilizing systemic effects of unchecked 

FinTech innovation brings into a sharp relief the crucial importance of strengthening the capacity of the relevant regulatory agencies to effectively oversee this 

process. 

FinTech’s ability to bring about massive increases in the volume and velocity of 

speculative trading in financial assets inevitably magnifies the systemic role of—and 

amplifies the pressure on—central banks and other public instrumentalities charged 

with ensuring financial and macroconomic stability. Hyperfast, hyperexpansive financial markets require a hyperfast and hypercapacious public actor of ‘‘last resort’’—one of the central bank’s core functions. Similarly, substantial new risks to 

consumers, posed by the digitization of personal financial data and the rise of the 

digital platform economy, dramatically elevate the role of Government agencies in 

protecting consumers’ data privacy and safety. And, of course, the growing concern 

58 

69Treasury Report, at 168. 70See, e.g., Hilary Allen, ‘‘A U.S. Regulatory Sandbox?’’ (Feb. 2018), available at file:///C:/ 

Users/sto24/Downloads/SSRN-id3056993.pdf. 

with potentially excessive concentrations of economic and political power in the 

hands of hypersized FinTech conglomerates underscores the need for a far more 

proactive approach to Government enforcement of antitrust principles. 

This, however, runs contrary to the Treasury Report’s overall deregulatory strategy and the emphasis on an inherently passive and accommodative regulatory posture. As a general matter, the Report supports, and even insists on, proactive—or 

‘‘agile’’—regulatory action only where such action is necessary to ‘‘expedite regulatory relief’’ under existing laws in order to facilitate private experimentation with 

new digital technology. 

The Treasury’s recommendation to form a State and Federal ‘‘regulatory sandbox’’ 

should be read in this normative context. 69 Several foreign jurisdictions, including 

Singapore and the United Kingdom, have already established such regulatory 

sandboxes, which essentially refer to the practice of allowing certain FinTech companies to operate for a period of time without having to comply with various otherwise applicable laws and regulations. The purpose of this arrangement is to conduct 

a controlled test of FinTech products, which should then help the regulators decide 

how beneficial and safe these products are for the rest of the market. 

The idea of a regulatory sandbox as a way to generate usable empirical data for 

better regulatory decision making is not necessarily a bad one. In each particular 

case, however, the efficacy of this effort depends fundamentally on the specific design features of the ‘‘sandbox.’’ Thus, if the specific assessment criteria for FinTech 

products in the ‘‘sandbox’’ are insufficiently capturing potentially problematic effects 

of these products on consumer interests or systemic financial stability, the resulting 

data will not be a reliable indicator of how that product will fare outside the ‘‘sandbox.’’ Furthermore, some of the most significant systemic implications of a particular 

product may be inherently impossible or difficult to test in a controlled ‘‘sandbox’’ 

environment. 70

In any event, a ‘‘regulatory sandbox’’ is not a substitute for a well-coordinated and 

well-resourced regulatory apparatus, capable of devising and dynamically implementing a comprehensive and balanced approach to overseeing FinTech activities. 

In this moment of great change in financial markets, the American public needs 

such an apparatus: it needs capable regulators and supervisors who show their true 

‘‘agility’’ by staying in front of, rather than behind or away from, the market. 

For all of the foregoing reasons, I urge the Committee to apply the healthy dose 

of skepticism to the Treasury Report’s and the interested industry actors’ consumercentric rhetoric and deregulatory demands. The systemic significance of FinTech innovations must be assessed in the broader public policy context, with a special focus 

on the need to protect American consumers from abusive market practices on the 

part of megasized corporate conglomerates, to safeguard the structural integrity of 

the U.S. financial market, and to ensure long-term systemic stability and sustainable growth of the Nation’s economy. Technology is not an end in and of itself, it 

is merely a tool: it can be used to improve our collective future or to destroy it. The 

Committee’s task is to ensure that the latter does not happen, while everybody is 

looking the other way. 

59 


91818001.eps

NEW TECH v. NEW DEAL: 

FLNTECH AS A SYSTEMIC PHENOMENON 

Saule T Omarova' 

Introduction ................................................................................................... 2 

I. Fintech as a Challenge 10 the New Deal Senlemen!. ................................ 8 

A. Fintech: A Preliminary Overview. ..................... .. .......... 8 

B. The New Deal Seulement in the Financial Sector ............................. I I 

C. Pre-Fintech Erosion of the New Deal Seulement A Brief Recap .... I 6 

D. Fintech and the New Deal Seulement Reframing the Inquiry ......... 20 

II. The Logic of Financial Innovation and Erosion of the New Deal ......... 22 

A. Focusing the Inquiry: Secondary Markets in Financial Instruments. 22 

B. The Mechanisms of Secondary Market Proliferation: A Preliminary 

Taxonomy .......................................................................................... 26 

I. The Market's Modus Opertmdi: Synthesizing and Scaling Up ..... 27 

2. The Four Mechanisms of Synthesizing Assets and Scaling Up 

Trading Activity ..................................................................................... 29 

C. Systemic Implications of Secondary Market Proliferation........ .. 34 

Ill. Decoding Fintech: Technological Revolution, Markel Evolution, or 

Power Devolution? ............................................................................... . 37 

A. "Eliminating Frictions:" Cryptocurrencies and Distributed Ledgers 38 

I. Bitcoin: Symhesizing Assets ......................................................... 39 

2. Distributed Ledger Technology: Scaling Up Trading ................... 42 

B. "Democratizing Finance:" Digital Crowdfunding and Robo-Advising 

........................................................................................................... 47 

I. Mal1ketplace Lending and ICOs: Synthesizing Assets ................. . 47 

2. Robo-Advising: Scaling Up Trading ............................................ 52 

C. Fintech as a Systemic Phenomenon: Unsenling the Public-Private 

Balance...................... ....................... .. ........ 55 

Conclusion .................................................................................................. 57 

'Saule T. Omarova is ?rofessorofl..:lwat Cornell University. For thoughtful comments, 

I thank £>an A \\TO)', Erik Gerding, Amias Gerety. i\dam Feibelman, Adam Levitin, and 

conference participants a1 Vanderbilt Law School and Oxford University. Special thanks to 

my colleague ond eO<Onspirntor, Bob Hockett. All errors are mine. 
60 


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2 FINTECH - drajl 18-Sep-18 

~'TRODUCT ON 

''Fintech," a popular tenn referring to the wide universe of innovative 

technology-enabled financial services, is by far the hottest topic in today's 

finance.' Fi ntcrh is visibly changing the way we conduct financial 

trnnsactions and use financial services: volatile cryptocurrencies are 

becoming a mainstream trnding asset, companies are rnising capital by 

issuing digital tokens instead of securities, and robots are advising people on 

some of the most important financial decisions of their lives.

2 less visibly, 

however, fimech is also beginning to change the way we think about finance. 

Increasingly ubiquitous, the fintcch phenomenon is grndually refiaming our 

understanding of the financial S)'Stem in seemingly objective, science-driven 

terms, as yet another sphere of targeted application of infomtation 

technologies and computer analytics. 

This emerging narrative of finance is seductive in its simplifying 

elegance. It focuses on concrete transactional aspects of finance, rnther than 

its inherently complex systemic dynamics. Targeting solutions for identified 

and isolated rrictions in financial market transactions, fintech embodies an 

inherently micro- rnther than macro-level view of the financial system. It 

deals with clearly functionally defined, progrnmmable (and thus controllable) 

business processes and tools, rnther than diflicult nonnative judgments and 

policy trndeofTs. Yet, the fintech narrative also has distinct undertones of a 

social re1•olution in its broader aspirations to re!ruild financial markets on 

principles of mutuality, cooperation, and inclusiveness. In that sense, its 

implicit promise is to redefine not only how we trnnsact with one another, but 

also who we are as a community: new technology will succeed where old 

politics !ailed. 

What should we make of this emerging narrative? Does fintech signify a 

genuine revolutionary shift in the fundamental dynamics of finance? And, if 

1 $&,e.g., Garrett Bald\\ in, The Top 10 Tre1N/s in finttch, fi!IURES MAGAZINE (April 

IS, 20t6). o"liloMe at hnp1/ll\\w.furummag.roml2016,-'l).!ll>hret0-tr<nds·fint<cll ("No 

tem1 is more ubiquitous in tod3y's financial media than lintech ... ); Bob J,isani. Here's Where 

Finrecl• Is /leading Nm. CNBC (June 6. 20t6). m'flilobte at 

hnps1/\m"'.mbc.com1201Ml61061heres-llnere-fintech-is-hoading-neX1.html ("The 

inleraction bet\\'een finance and technology, or "fonte<h." remains a hot topic."); Daniel 

Newman. Top S Digilfll Trtmsformmion Trends in fill(mCilll Sen·ices, FORBES (May 9, 

201 7), ami/able 01 hnos:l/lnnvJotbe:scom/siteydanicillellman/201 JI051091t00:5-digital· 

transfonnation-trends-in-finarn:ial-servicesl#75g!2cle20-k. ("If it feels li~e this change is 

fast and furious. you're right."). 

' For example. in 2017, an influential industry repon identified serenteen distinct 

"lintech S<TI'ices" oll<r<-d by a " ide array of pro\'idcrs in such"'"" as"money transfer and 

payments, finamial planning. s.wings and in\'estmcnl. borro"ing. and insurance." Ernst & 

Young. £Y fi11Tech Mop1i011 l11dtx 20/i: n,e R11pifl £m;;rgenceofF•i•Teth. at 6. arailable 

at hnp1tl•ww.ev.C<IllliPublicatioru\·"LUAssetsley-fintech-adootion-ind<x-20171SFILfieyfinteyh·adoolioo.index-2017.odf 
61 


91818003.eps

8-Sep-18) FINTECH - drajl 

so, what are the nature and potential implications of this fintech re1•olution? 

Is it capable of delivering the ultimate, nomtatively neutral and politically 

uncontestable, cure for the financial system's underlying dysfunctions? 

The purpose of this Anicle is not to provide definitive answers to these 

questions. Rather, it is to propose a general conceptual framework within 

which they s~ou d be addressed. Much has already been, and continues to be, 

\\Titten abcut the rise of fintech and its growing impact on financial markers 

and regulation.' Legal scholars, in panicular, are increasingly interested in 

various lega I and regulatory challenges posed by the new technological 

advances in iinance.4 Some of the most valuable insights to date have come 

from the literature examining specific legal, economic, or operational aspects 

of individual fintech applications.

1 Alongside these targeted legal analyses, 

1 For a small sample. see U.S. Dept. of the Treasul)'. Report to Presiden1 Tmmp: A 

Financial System thaf Creates Econ'()mic Opportunifies: NottiHmk Fiuoncials. Finterh, and 

lmtOI"Oiioll (July 2018).ai'Oilab/e at hnps~lhon!e.tw"rv·SOI sitey<kfauh fole¥2018 01 · 

Financiai-System·that-Creates·Economiy()p00!11mities .. ·Noobank-Financi .... !!df 

]hereinafter, Treasury Report); John Schindler, FutTecil ond fiiiiWio/ hmoi'Otioll: Drirm 

am/ Dep1h, Finanto & Economics Discussion Series Pap<r 2011-081, lloard of Governors 

of the Fooeml Reserve System (2017), m"Oilable or 

https~A""' . f<dera1rtsmt.gov/eeon!t§lf¢slfolosl20 t 708 I oap.od f; Financial Stability 

Boord. Finm~eiol Stabilio· lmplictl/ions front Fiureclt (21 June 2017). orailable ar 

httoJiwllw,f\\>.org/wp-contentluoloadsiR21Q611.!!df; Financial Stability Boord, Fimech 

Credit: Market Stru<ture. Bt~incss Models and Finan<ial Stability Implications (22 May 

20 11). arailabf~ 111 hnp~lwww. !Sb.om/11 p-contentluploads/CGFS-FSB-Reoon-M-Fin T«h· 

Credit.!!df: IVORW ECONO~IIC fORUM, REAliZING Tile OTE~'TIAL Of Bt.OCKCIIAtN: A 

MULTISTAKr:tiOLOCR APPROACII TO TilE S1£W,IROSUIP OF lltOCKCIIAIN ANU 

CRYri'OCURRE:;CtES (June 2011), m·ailable at 

http Amw3 ll¢forum.O!l!!docs!WEF Reali?jng Potential Bloskehain pdf. 

' For a sampl< of the rapidly gJ~Jwing legal sebolarship oo these iSSU<S. see Iris II· Y 

Chiu, fjnredt roW Disruptire Busi11& Models in Finmrciaf Produrls. lmermetliarion and 

Marker-Policy lmplitalion.sfor Fi111mtiol Regulators. 21 J. 'T£01. L& POL'\' 55 (2016); 

Douglas W. Amer, Janos Batberis, & Ross P. Buckley, n1e Erolmion ofFiwclt: A Nell' 

Post·Crisis Paradigm' 41 G£0.J. L\'T'LL 1211 (2016); Douglas IV. Amer, Janos llarberis. 

& Ross P. BU<kley. Fin Tech, RegTecl1, 1md Rewtcepnrali:ation ofFinanciol Regulation 31 

N\1'. J. INT'L L. & ~'S. )71 (2017): Oir!; i\. Zetzsehe. Ross P.llucklcy. Douglas IV. Amcr. 

Janos N. Barberi~ From Fin Tech 10 Techfin: Tile Regula/01)' Clwllenges of Dara-Drirtll 

Finm11:e, EBI Working Paper Series No.6 (2011); Chris Brummer & Yesha Yadal', nre 

Fimec/1 Tri/emm/1. Vanderbilt Law Res. Pap<r oo. 11-~6 (2011~ arailable ar 

hnrn:l/rooers.;~.OO!Molllpaoers cfin?absuact id= JOlmO; Tom llak~r & Btnooict G. c. 

Dellacn. Regulaling Robo A1hice Across the Fi110111:ial Serrices hulusrry, I 03 IOWA l. REV. 

(fortbcomi11g 2018); William J. Magnuson, Regulating Finlech, VA~D. L REV. 

(forthcoming 2011); Rory Van Loo, Maiing l11mJ\'111ion More Compoliti•~: The C1~e of 

Fimech. 65 UCLA L. REV (2011). 

' Set, e.g .. John Am1our & Luca Enriques, The Promise ami Perils of Cro.-dfiwlillg: 

Bem-em Corp11rme Fintmte tUid Co11sumer Camracrs. ECGI Working l'ap<r No. 36612011 

(2011~ ami/able at hnps lpaners ssm oomlsoll papmcfm?a&.trnct id=3035241· Jeanne 

Schroeder. 8iu:oil111nd the Uniform Contmercial Code. 2~ U. MtA\11 Bus. L. REv. I (2016); 
62 


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4 FINTECH - drajl 18-Sep-18 

there is a rapidly expanding body of scholarship that attempts to take a 

broader inventory of issues fint~h raises for lawmakers and financial 

regulators.

6 This literature helpfully identifies certain key considerations the 

regulators should "keep in mind" as they address such issues in practice and 

discusses innovative ways for regulators to ''stay on top" of technological 

change. Yet, it stops short of offering a coherent conceptual account of 

fint~h as a systemic phenomenon. As the list of identified regulatory 

concerns and considerations grows longer and more detailed, however, the 

need for an overarching conceptual framework within which to analyze the 

role of technology in finance ~omes increasingly pressing.7 

Aiming to fill this gap in the existing literature, this Article takes a deeper 

and more encompassing systemic view of fintech, both as a financial market 

phenomenon and as a regulatory challenge. It takes a position that, in order 

to make real sense of technological changes "disrupting" today's financial 

markets and regulations, it is necessary to broaden the anal)1ical and 

nonnative lens beyond the immediate economic and legal effects of specific 

fintcch applications. At bottom, an inquiry into the nature and dynamics of 

the "fintcch revolution" is, and should be, an integral part of the broader 

inquiry into lhe nature and dynamics of,(luauce itself The latter, in tum, is, 

and should be, a fundamentally nom1ative inquiry into the social function -

and, by extension, dysfunction- of modem finance. Therefore, the Article 

posits, the role of technology in finance cannot be properly assessed. or even 

understood, without explicitly addressing the underlying questions about the 

role oftoday's finance in the broader socio-economic system. 

The emerging fintech narrative in its present fonn, however, tends to 

mask this underlying continuity. The newly empowered and fashionable 

notion of"financeas techt1ology" is threatening to eclipse that of'·finance as 

Angc131Vakh, T!Je Bitcoin81ockdlhin as Financial Morkrt bifrastmcmre: A U>rosideration 

of Op<rmiorwl Risk, IS J. . & Pus. POL'\' 837 (2015); Ad.un J. Levitin, Pnru/Qro's 

Digital Box: TUe Promise and Perils of Oigita/ll'allets, 166 U. I'ExN.l. R£1'. (20 17). 6 See sources cited suwa nMe 4. for analyses focusing on financial k"gUiators · attempls 

to entourage technologkal inn(Wation and to develop 1heir o"n tt>thnological capabilities:. 

S<e Hilary J. Allen. A US Regulatol')' Sandbox?. IIWiilable at 

hups://paoer>.ssm.comlso!J!papers.cfin?abstract id 3056993: Roty Van Loo. Rise of The 

Oigillll Regularor, 66 DUKE l. J. 1267 (2017). A sontewhat distinct thread in this liter:uure 

focuses more namJwly on fintC(h·relmcd changes in tltt fumiliar pauerns oftrnnsactiorutl .. intenncdialion" in \'arious come.xts. See lknjamin GC\"tt, Disilllmneditlfing l:."leclrcnic 

Parments: Digital Caslwnd Virtual Currencies. 31 J.I 'T L BANKt~G l. & REG. 661 (20 11): 

Kathryn Judge. The Fuwre of Direct Fi111mce: The Oiowging Paths ofPeer-to-Prer Lemli1'8 

01rd Kickstarter. 50 IV AKE FOREST l. REV. 603 (2015 ). 

' Fora rcc<11t re1iew of th< emerging economic res<areh on finte<h. S<e l'eter Gomber. 

Jascha-AI~<an<kr Koch, Michael Siering, Digital Finon<e am/ Fimech: Currem Research 

ami Frllrm! Re.searcll DirtclifJIJS, J. BUS. EC0}..1• (2017). As this re\iew shows, there are 

presently significant gaps in the economic literature on the subject. 
63 


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8-Sep-18) FINTECH - draft 

public policy." This Anicle seeks to reintegrate these two concepts, both as a 

mauer of des.criptive accuracy and as a nonnative mauer. Technology enables 

and drives financial transactions, but so does public policy embodied in 

financial laws and regulations. On a micro-level, finance often appears 

primarily, if not purely, transactional: a mauer of individualized private 

exchange among market actors. On a macro-le1·el, howe1•er, modem finance 

is a mauer not only of great public imponance but also of great public 

invoh•emenL 8 The rise of fintech throws into sharp relief this essential 

hybridity of modem finance and exposes some of the deepest normative 

tensions underlying it. 

The Ankle argues that, from this systemic perspective, the fintech 

phenomenon has a broader significance than a "disruption" in the prevailing 

modes of, or institutional channels for, deli1·ery of specific financial services. 

Its arrival marks a potentially decisive shift in the fundamental political 

arrangement underlying the operation of the modem financial system, as it 

currently exists in most advanced markets. Not surprisingly, that arrangement 

is most easily discemable in the U.S. that, fort he most pan of the last hundred 

years or so, has been the world's leader in del'eloping not only large-scale 

capital markets but also the sophisticated legal and regulatory apparatus for 

a sustained and systematic oversight of financial markets and institutions. 

The U.S. system of financial sector regulation took shape during the New 

Deal era, as pan of a concened govemmenl response to the economic and 

political fallout from the stock market crash of 1929 and the Great Depression 

that followed it.9 Today's elaborate scheme of U.S. financial regulation and 

supervision, directly or indirectly replicated around the world, continues to 

rest on the fundamental norms and policy principles at the core of the New 

Deal refom1s.10 These deep underlying norms and principles form what this 

Anicle calls the New Deal seulement in the sphere of finance. 

As discussed below, the New Deal senlement reflects cenain politically 

deril'ed judgments about the optimal balance of private Freedom and public 

control in the financial market.11 Under this paradigm, private market actors 

1 For an in-d<pih theorelical mount of lhc fundamemal hyl>ridily of modem finance as 

a public-prirale enlerprise, see Roben C. Hockell & Saule T. Omarova, The Fi11once 

Frotldtist, 102 CoR.~ELL L. REI'. 1143 (2017) (hereinafter, "fi11once Fro11chi~"J. 

'See Saulc T, ()maro\'3; 011e Sltp f{)(lmrd. T•·o Sleps Bock' Tile IIISiilrtliotwl StmCiure 

of U.S. Fiti/Jnttitl Smicts eg~tlotiOII Afttr the Crisis of 1003, in ROlliN HUI U"'~G & DIRK 

SaiO N)~IKER (EOS.), INSTITUTION>IL STRUCTURE OF fiNANCIAL REGULATION: THEORIES 

ANO INTER.'<.ITIONAL EXPERIENCES 137 (2014) ) (hereinafter, "fllstituliOIIOI Structure"} 

(derailing lhc institutional legacy of the New Deal in 1he finanoial S«lor). 10 See Saule T. Omarova, The Dodd-Frank Act: A NtM' Deo/for A NtM'Age? 15 N.C. 

BANKING IXST- 83 (2011) [hereinafter. "A New O.olfor A NtM· Age"] (analyzing the key 

clements ofohe regulaoory pllilosophy in ohc finanoial sector). 11 For a discussion oflhe New Deal senlcment's core fca1ures, see infra P•n I.B. 
64 


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FINTECH - draft [8-Sep-18 

retain contr<>l om substmnive decisions on how to allocate financial capital 

to various productive uses - and thus the power to detennine the overall 

volume and structure of financial claims in the system. The public, on the 

other hand, bears the primary responsibility for maintaining the overall 

stability of the financial system and enabling markets to function smoothly 

and e0icien1ly. Government regulation is the indispensable mechanism 

through which the public manages the moral hazard built into this 

arrangement: in essence, regulation constrains market participants' ability to 

generate excessive system-wide risks in pursuit of private profits.

12 

An inherently unstable and contestable nature of this balance is the source 

of the fundamental tension at the core of the New Deal selllement. In an 

important sense, the entire history of U.S. financial markets and regulation 

since the New Deal era has been the history of continuous renegotiation and 

readjustment of this public-private boundary, driven by private market actors' 

continuous efforts to expand their freedom to create and trade financial 

claims. 

To elucidate these deep-seated systemic dynamics, the Article 

deliberately shifts the anal)1ical focus from primary markets, in which firms 

raise capital by issuing financial claims, to secondary markets in which such 

claims are traded. Despite legislators' and regulators' continuing 

preoccupation with "capital fonnation" in primary markets, the financial 

system's center of gravity has long shifted to secondary markets." Secondary 

markets in financial assets currently dwarf primary markets in temts of size, 

complexity, :and systemic significance." Secondary markets also operate as 

the principal sites of relentless financial "innovation" and chronic overgeneration of systemic risk.

15 The key to understanding what drives today's 

complex financial system, therefore, is to understand what dri,·es the 

continuous gro\\1h and proliferation of secondary markets. 

Operationalizing this insight, the Article identifies the core mechanisms 

and techniques that enable private actors to create and grow- continuously 

and ''irtuall)' unconstrained- secondary mari<ets for financial risk trading. It 

argues that the gr0\\1h of financial markets is best understood by reference to 

two interrelated system-wide transactional practices: {I) continuous 

synthesizing of new tradable financial assets, and (2) scaling up the volume 

and velocity oftradingacti,•ity in financial mari<ets. The Article breaks down 

these phenomena further by sho\\~ng how private market actors pursue these 

overarching objectives via four principal mechanisms: pooling and layering 

11 See id. 

" See i1rjra Part I I.A. 

" See infra notes 92-93 and accompanying 1cx1. 

" ld. 
65 


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8-Sep-18) FINTECH - draft 

of claims, alld acceleration and compression of trades.t6 

System-wide deployment of these transaction meta-technologies -

pooling, lay-ering, acceleration, and compression - enables the constant 

gro111h and complexification of the financial market. By the same token, it 

magnifies the extent and urgency of the public's obligation to accommodate 

privately created claims and to manage macro-financial risks. Critically, 

however, the public side is not always able to keep up with these increased 

demands by expanding its regulatory oversight capabilities. In fact, private 

actors' very success in synthesizing financial assets and scaling up trading 

activities often depends on the lack or inefficacy of regulatory controls - a 

familiar story aptly illustrated by financial market developments since the 

early 1980s and the global financial crisis these developments brought 

about.ll 

This Article examines the rise of fintech in the context of this decadeslong process of gradual erosion of the New Deal settlement. It posits that 

deciphering the meaning of "fintech revolution" as a macro-financial, 

systemic phenomenon requires a deeper understanding of how specific 

fintech applications impact the public's capacity to maintain the stability of 

the macro-environment. Fintech may present a unique opportunity to correct 

the increasingly problematic imbalance between private misallocation of 

credit and the public's ability to modulate credit aggregates-or it may further 

intensifY that imbalance_ts 

Reframing the inquiry along these dimensions, the Article argues that the 

more established fintech applications to date are already exhibiting signs of 

ske11~ng the balance further in favor of private actors' unrestrained freedom 

to generate - and over-generate- financial risk. While it may be too early to 

draw definitive conclusions, the recent advances in computing power, 

cryptography, data ana1)1ics, and machine learning appear poised to amplify 

the long-lasting systemically destabilizing trends in the financial market. As 

shown below, new technological tools enable private market participants to 

engage in the continuous synthesizing of crypto-assets that are (a) untelhered 

from, and thus unconstrained by, any producti1•e activity in the real economy, 

and (b) tradable in potentially infinitely scalable virtual markets. What is 

commonly seen as the key micro-level ad1•antage of fintech - its ability to 

eliminate transactional "frictions" and to circumvent traditional market 

boundaries- also operates to amplify the system's capacity to fuel financial 

"See ilifra Part tLB.2. 11 See i1>/ra Part I.C. 

" See FiMI/Ct! Fro11cilise. supra note 8. For a detailed theoretical and historicallyglllllllded posH:risis account of the importance of structural, as opposed to individual or 

fim1-tevet, incentives for financial risl:-taking, set Robert C. Hockeu, A Fixer-Upper for 

FiMJICI!, 871VASII. U. L. REV. 12t3 (2CI0) (hereinafter. Fil·tr-Upper(. 
66 


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FINTECH - draft [8-Sep-18 

speculation on an unprecedented scale.19 On a macro-level, therefore, the key 

risk posed by fintech lies in its- still not fully known - potential to exacerbate 

the financial system's dysfunctional tendency toward unsustainably selfreferential gro111h.

20 

From this perspective, the onset of the fintech era marks a crucial political 

moment. ln,•isibly, the new technology is "disrupting" the New Deal 

settlement in finance. The nearly century-old arrangement that rigidly 

separated credit generation and allocation (an exclusively primte right) from 

credit modulation and accommodation (an explicitly public responsibility) 

appears increasingly ill-suited for ensuring systemic stability in the emergent 

world of frictionless crypto-speculation.

21 Accordingly, in trying to make 

sense of specific technological advances, we must not lose sight of the 

ultimate systemic challenge rising in their background: the growing need to 

rethink the current public-private boundary in finanee.22 

The Article is organized as follows. Part I provides a brief overview of 

recent fin tech developments and places them in the context of what I call the 

New Deal settlement in finance. It outlines the defining features of this 

political settlement and traces the process of its gradual erosion in recent 

decades. Delving deeper into this process, Part II advances a novel conceptual 

framework for understanding the fundamental dynamics of secondary 

markets in financial instruments. It oll'ers a preliminary taxonomy of 

principal mechanisms- or system-level transaction meta-technologies - thai 

enable private market actors to engage in continuous synthesizing of tradable 

assets and scaling up of trading activities. Finally, Part Ill examines specific 

fintech applications - Bitcoin, distributed ledger technology, marketplace 

lending, initial coin offerings (ICOs), and robo-advising - from the 

perspective of their potential to amplify the operation ofthese core financial 

market mechanisms. It concludes by drawing out some of the key systemic 

implications of these new technologies and, accordingly, redefining fintech 

as a public policy challenge of the highest order. 

I. FtNTECH AS A CHALLENGE TO THE NEw DEAL Sm LEMENT 

A. Fintec/1.· A Preliminary Overview 

"Fintech" is an umbrella tenn that refers to a variety of digital 

technologies applied to the provision of financial services and, more 

generally, developments in the financial socror. Perhaps the most 

immediately recOgJlizable symbol of the finteeh era is the rise of private 

" For a dmiled disoussion. see i•yro Pan Ill. 

"'See i•ifra Pan lll.C. 21 /d_ 12 for a oomprchcnsi\'C thooretical and nomtali\'C acrount of the core public-private 

dynamics in finance. see Fintmce Fra11Chi.st. S11prt1 note 8. 
67 


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8-Sep-18) FINTECH - drajl 

cryptocurren.cies, defined generally as "any form of currency that only exists 

digitally, that usually has no central issuing or regulating authority but instead 

uses a decentralized system to record transactions and manage the issuance 

of new units, and that relies on cryptography to pre1•ent counterfeiting and 

fraudulent transactions.''1l Bitcoin is the first cryptocurrency to date that went 

mainstream, albeit as an investment asset rather than a viable substitute for 

fiat money. ~ The Bitcoin network is built on blockchain technology, which 

uses a complex algorithm to allow decentralized verification and recording 

of each transaction in a publicly viewable distributed ledger.11 

Importantly, the blockchain - or, more broadly, distributed ledger -

technology potentially allows for a wider range of uses outside simply 

supporting specific cryptocurrencies. Thus, Ethereum, a blockchain platfonn 

designed to host an unlimited number of project-specific third-party 

applications, enables what is now kn01111 as "smart contracts" to automate the 

execution of a wide varie.ty of transactions, including the ongoing 

perfonnance of transacting parties' obligations.26 Among other things, "smart 

contract" algorithms can automatically disburse payments or transfer title to 

assets, upon the verified occurrence of specified triggering events.21 

Corporate dividends, interest payments, insurance payouts, and derivatives 

collateral m3nagement are some of the areas in which smart contracts 

potentially ofier the most easily discemable optimization benefits. 

Smart contracts also enable so-called "initial coin offerings," or !COs, in 

which various fim1s raise capital online by issuing digital tokens, or "coins," 

that carry various rights with respect to some future digital product or service 

the issuing fim1s intend to finance and develop.1& An ICO is essentially a new 

fom1 of crowdfunding that, ideally, enables tech startups to raise funds 

directly from their user communities.29 Another fonn of digital crowd funding 

n ~lcrri3m-Webstcr, CryprDCitffeucy, arailt~ble "' hnos:/1\ww.menia.m· 

webster.comldiction:u'\'/mptocurrene\'. II is notoriously diOicult to draw pt~Xisc 

definilional boundaries among different cat•'&Orics of crypt<Kurrcncics, crypto-ass<JS. 

tokens, coins, etc. See Hinge. infra note 15S(highlighting definitional diniculties). 

"For a dmiled discussion ofOitcoin. see infra Part II I.A. I. 

" See iii. 

"'The term "sman contract'' has no clear and uniformly ae<epted definition. Depending 

on context, it n1ay r~fcr either to a computer code stored, \'ctilied, and e~ec tcd oo a 

blockch.lin, or to a specific application of that code as an eflitthe sub~itute for a legal 

rontract. See Josh StaJt, Jfaklirg Sense ofBiodchaiJI Smart Contracts. COI~I.l£SK.CO\I (June 

4. 2016~ araifable at httpsJhnm.ooindesf<.comlmaking-sclll<·Sinart:£9!!traCtsl. 

" ld. 

,. Many ICOs are functiooatty equil·alent to sccuritics ofterings without II>< mandatory 

disclosure and other inl'cstor-protection features required under U.S. securities '""· 

Accordingly. the applicability of federal se<urilies Ia"~ and regulations to !COs has been 

on< of the hol1os1 legat issues in the finteoh spoce since 20t6. Set infra Prut lti.B.I. 

" Set i1rjra note 208 and accompanying le<l. 
68 


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10 FINTECH - drajl 18-Sep-18 

is peer-to-pe-er, or marketplace, lending.30 The original idea behind today's 

marketplace lending platforms - LendingCiub, Sofi, and others - was to 

bring together individual and small-business borrowers and lenders, in order 

to create a truly decentralized and direct credit market31 Not surprisingly, 

marketplace lending is often portrayed as a tool of"democratiling" finance 

by eliminating the need for banks and other financial intermediaries and by 

expanding access to credit. 

The same "democratiling" impulse is commonly ascribed to the 

increasingly popular practice of robo-advising.32 Robo-advising denotes 

providing online financial advice with minimal or no human participation, 

using algorithmic asset alloc~tion and trading models.'

3 Financial 

institutions' ability to replace expensive human advisors 11~ h cost-effective 

computer codes is seen as the tool of broadening access to previously 

exclusive ll'eahh management sef\•ices: everyone can invest in capital 

markets with robo-advisors' help.;J 

As this brief 01•erview shows, all of the currenlly existing fintech 

applications - cryptocurrencies, blockchain technologies, smart contracts, 

digital crowdfunding, and robo-advising - explicitly promise to 

"revolutionize" provision of financial services. New digital technology 

unlocks nell' possibilities for a fully frictionless transacting in a completely 

virtualized world, 11ithout the costs and delays associated with the use of 

professional financial intennediaries operating under multiple jurisdictions' 

rule.s. By making financial transactions infinitely faster, easier, and cheaper, 

fintech also offers new opportunities for financial inclusion and expanded 

access to financial services. In this sense, new technology seems poised to 

"revolutionize'' finance not only as a matter of transactional efficiency but 

also as a matter of political economy. 

Yet, buill into this narrative is a crucial presumption- sometimes explicit 

but often implicit-thatthe unfolding fintech ''revolution" is a politically and 

nonnatively neutral phenomenon, a ''win-win" situation not involving hard 

public policy choices and trade-offs. The prevailing altitude is to treat most 

of the problems commonly discussed in connection with fintech -

cybersecurity concerns, network governance lapses, legal unccnainty, or 

" Marketplace lending is defined broadly as "any prac1icc of pairing borro"~" and 

ltndcrs through theuseofan online plmfom1 withoul a uaditional oonk imenncdiary ... FDIC, 

Mtukflp/ace Lemling. SUP. ISSIGtn~ (Winler 2015), rrrailable at 

hnos://www fd~c.go,'frtgulatiqns/txaminatiansfgmroisorvlinsightslsiwin!5!si winler2015 

-anicleQ2.!ldf. 

" For 100re on the C\·olution of marketplace lending. see infra notes 193-205 and 

accompanying text. 

ll For a discussion of robo-ad,;sing. see infra Pan 111.8.2. 

»See i•!fra no1e 221 and accompanying t~<~. 

" For a cri 1ioal examination of lh~ claim. S<.'< infm Pan 111.8.2. 
69 


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8-Sep-18) FINTECH - draft II 

regulaiOI)' gaps- much like naiUral "growing pains" accompanying society's 

triumphant march to a beuer future, benign temporary glitches uhimately 

resolvable dbrough belter coding or faster rule-IITiling. 

Finance, however, is not politically or normatively neutral: money and 

power arc t11·o sides of the same coin. Finance is, and always will be, a mauer 

of utmost and direct public policy significance. Financial arrangemenls are 

fundamental~y shaped by, and in tum shape, broader economic and political 

structures and choices. ''Virtualizing'' financial transactions does not change 

this basic fact, only obscures it from view. Understanding the full 

significance-of the fin tech phenomenon, therefore, requires widening the lens 

beyond the immediate micro-transactional enects of new technology to 

encompass l~e essential dynamics of the financial system as a whole. 

To this end, it is critical 10 start by reminding ourselves of the core 

political arrangemen11ha1 detennines the principal structure and operation of 

loday's financial system. In the U.S. context, it may be referred 10 as the New 

Deal seulement in finance. 

B. The New Deal Seulemem in the Financial Sector 

The New Deal era was the pivotal moment in the emergence and 

development of the entire system of modem U.S. financial sector regulation 

and supervision.'5 ll was during this fateful period thai Congress created a 

comprehensive system of disclosure-based federal securities regulation and a 

federal deposit insurance scheme, institutionalized the separation between 

banks and securities firms, and established numerous other legal and 

regulatory principles that continue to shape the operation of the U.S. financial 

system today . .l6 The purpose of this Article, however, is not 10 n.>count the 

specific financial sector reforms of thai turbulent era but 10 distill the 

overarching principles that infonned, guided, and found expression in the 

multitude of such refonns. This is what I call the New Deal seulemenl in 

financen 

The New Deal senlement reflects certain politically deril'ed judgments 

ab-out the optimal balance of private freedom and public control in the 

financial market Several key features of the New Deal political seulement 

defined the substantive contours of the U.S. regulatory philosophy in the 

financial sector. AI the highe.st level of generali1,1tion, the New Deal reforms 

inslilulionali zed the broad concept of public interest - including public 

"See MtCtiAEl S. a,,RR, lto•tLL E. lACKSO~ ~LIRGARET E. TAitY.IR, FlN INC~Il 

EGIJI.ATIO~: L,IWANOPOt.ICY 47-52 (2016). 

"See id.: hrsrirurio11al Srrucrure. supra note 9. 

:n It is worth reiterating here that the Ani de concerns itself with the New Deal scnltment 

only in the C<lntext of financial marl.:e1s and regulation and 001 as a broader phenomenon in 

American political histOI)' and constitutional de~ opment. 
70 


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12 FINTECH- draft [8-Sep-18 

representation and public enforcement - as a legitimate factor in the daily 

operation of financial markets. The new regulatory philosophy explicitly 

acknowledged the overarching need (i) to protect the public from abusive 

market practices (as opposed to letting all market participants fend for 

themselves), (ii) to ensure that private financial markets should strive to serve 

the public's needs (as opposed to private market participants' needs alone), 

and (iii) to take the lead role in maintaining the integrity and healthy 

functioning of financial markets (as opposed to letting markets self-regulate). 

In pushing tihe public-private line in finance in this thereto unprecedented 

way, the New Deal settlement was a political "disruption" of enormous 

significance. 

Yet, it didn't push the line too far into the "public control" territory. The 

New Deal regulatory refonns left private actors ftnnly in control over 

substantive allocative decisions in financial markets, limiting the area of 

direct public control mainly to procedural and infrastructural support of the 

financial market's operations. With limited exceptions, the government's 

principal role was defined primarily as that of an outside regulator, the source 

and enforcer of the basic rules of fair play in financial markets.» It was 

envisioned as a largely exogenous force with a limited mandate to influence 

private market actors' decisions on channeling credit and investment flows 

to specific uses.

39 This principal delineation of public and private roles was 

reflected in and operationalized through such important regulatory choices 

as, for example, a deliberate rejection of merit-based financial product 

approval and a systematic preference for disclosure-based schemes . .o To put 

it simply, as long as the risks associated with a particular financial product 

were adequately disclosed, the government had little power to prevent the 

risky produc1 from entering the market.'1 

"Of course, the New 0..1 m gave rise to many fonns of din.'<! gorcmmcnt action 

inside, rnthcr tl.an merely outside, the ostensibly pri1~tc finantial markets. Perhaps the best 

example in tl•is n.'Spc<t was the Re<onstiUCtion Finante Corporntion (Rt'C), the onee· 

powerful but now n«llly-forgotten fcd<t.ll in~rumcn ity that play<d a critical role in 

maimaining lhe functioning of the nalion·s financial mari.;ets during the Grtal Depression. 

The extraordinary nature of this e.<e<ption, however. only underscores the general rule. For 

an in-depth aMiysis of the RFCs role and institutional legacy, see Roben C. Hockett & 

Saute T. Omarn1·a, Prirme IYMfth tmd Public Goods: A C"" for a Nmionllf fnrestm"'t 

Aut/l()liQ•, 43 J, CORP. L. m (2018) [hereinafter, Notionollnr~tment Authorio·J, 

" See Robcn C. Hockett & Saule T. Omarora, Public Actors in Prirate Markets: 

Ton·ord a Den-lopmemal finance Stme, 93 WASil. U. L. REv. 103. t 13 (20t5) [hereinafter, 

Public Actors]; Robert C. Hockett & Saute T. Omaro1·a. ""Prirate" .\leans to "'Pltblic" ends: 

Gorernments as Market Actors, IS TttEORETJC,IL INQUJR.tESIN L. 53. 54-55 {2014) 

"' For in-depth discussion of finan<ial prodU<t approral as a form of macroprudential 

regulation, see Sau\e T. Omarol'a, License to Deal: Mmuimory Approral of Complex 

fintmcilll Prtxlucts, 90 WASH. U. L. REV. 63 (2012). 

" See A New fkal for A New Age? supra note I 0, at 95-97. 
71 


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8-Sep-18) FINTECH - draft 13 

As a result of this fundamental line-drawing between the public and 

private roles in finance, the New Deal regulatory paradigm had an inherently 

micro-. rather than macro-, focus. Because private market panicipants, with 

their infonnational advantages and individualized economic incentives, were 

presumed to be superior decision-makers "on the ground," their judgments 

on risks and returns of particular financial transactions and products were not 

to be substituted by those of the regulators. To the extent regulators' 

judgments are, and expected to be, driven by the generalized public interest 

considerations rather than by any specific transactional "efficiencies," 

however, this policy choice set the context for a systematic prioritizing of 

micro-transactional factors over macro-systemic ones, and of individual 

action o1•er collective agency. It is implicitly assumed that, if the former is 

taken care of, the Iauer will necessarily follow.'1 

Accordingly, the New Deal paradigm focused expressly on regulating 

individual financial finns, licensed and supen•ised under clearly identified 

regimes, based on the types of products they onered and acti1•ities they 

engaged in.u The regulatory boundaries among financial instimtions (banks, 

securities broker-dealers, insurers, etc.) and financial products (securities, 

banking products, insurance, commodity futures, etc.) were dra1111 in clear 

categoricaltenns.

44 The silo-based regulatory architecture, in which separate 

administrative agencies oversee formally separate financial sub-sectors under 

different statutory schemes, was an institutional embodiment of this 

approach. 

45 

At the same time, the New Deal refonns have also institutionalized the 

public's role as an explicit market backstop "oflast resort." Perhaps the most 

readily recognizable example of this public safety net is the comprehensive 

federal deposit insurance scheme administered by the Federal Deposit 

" This is a basic logical error kno•" as the fallac)' of composilion. For a pos1-<risis 

hoor..~ical and historicall)'-growodcd attounl of 1he in1ponanee of macro-, as opposed 10 

micro-. dynamics in financial marl<el~ see genero/6• Robert Hocken, A firer-Upper f()j' 

Finance, 81 WASil. U. l. REv. 1213 (2010); Robert Hoeke11, Bretton Woods 1.0: A 

Constntctil~ Retrie\'111 for Sustoilmble Fin111rce. 16 N.Y.U. J. LEG. & Pus. POt'Y 40 I (2013). 

'

1 See lnstitutio11ol Stntclure. supra nole 9. The canonical <>ample of lhis regulalory 

philosoph)' was 1he Gla.;-Sieagall Acl, 11hich eslablished a s)'Sicm of ~rict separalion 

bchl'e<n commtreial banking and inl'e-slmcnl banking. Banking Acl of 1933, Pub. L No. 13-

66; 48 StaL 162 (1933), 

"Set h•stitutionnl Structure. supra note 9: OVER.~MENT 1\CC()U).'TAI!Illn' OFFICE, 

GA0-05-61. fiNANCIAL REGULATION: INOliSTRV CHA~GES PRO)IPTNEEO TO ECO~~!OER 

U.S. REGUuiTORV STRUCTURE (OCT. 2004): GROUP OF nuRTY. n1E STRl:CTURE OF 

FI:<.\NCIAL SuPERVISION: APPRO.ICI!ES AND Cti.ILLENGES IN A GLOB,IL MARKElPLACE 

(2008); OVERNM~\'T ACCOUXfABILITY OFfiCE, Gi\0-13-180, FINANCIAL REGUUTORV 

REFORM: fiNANCI.IL CRISIS LosSES AND POTENIML IMPACTS OF THE DoDo-FRANK ACT 

(JAN. 2013). 

"Seeid. 
72 


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14 FINTECH - drajl 18-Sep-18 

Insurance Corporation (FDIC).-111 Another important example of the public's 

market-preserving role is the central bank's expanded emergency authority 

to prop up not only banks but also broader financial markets.47These political 

choices functionally transfonned the government from a (presumably) 

exogenous rule-maker and enforcer into a direct financial market 

participant.~8 Furthennore, these choices explicitly putt he government- the 

quintessential political actor, the ultimate collective agency- in charge of 

preserving the stable functioning of financial markcts.'9 

In consequence, there was - and still is- deep tension at the heart of the 

New Deal regulatory paradigm: it vests substantive control over the 

allocation of risks and returns in financial markets in private actors operating 

on a micr()-level,and assigns the responsibility for ensuring financial stability 

to public actors operating on a macro-level. Government regulation was 

designed to counteract and control the obvious moral hazard built into this 

system. In this sense, effective public oversight of financial markets and 

institutions was- and still is -critical to maintaining the New Deal political 

settlement. II is through close regulation and supervision of financial markets 

and institutions by specialized govemment agencies that the SOI'ereign public 

was expected to keep profit-seeking private market participants from abusing 

their micro-level freedom to generate macro-level risks. 

The fundamental problem with this approach is that, in practice, 

allocation and modulation of credit and money in the financial system are 

intimately connected; systemically destabilizing asset price booms are the 

direct e!Tect of socially suboptimal allocative decisions by individual market 

participants.30 The superficially neat functional separation of public and 

private, therefore, is inherently unstable.lleneath an intuitively clear division 

of functions, there are complex dynamics, conflicting interests, and 

ambiguous boundaries. 

In etl'ect, it may be said that the entire history of U.S. financial markets 

and regulation since the New Deal era has been the history of continuous 

" S.e.ge11erall); 12 U.S.C.§ 181 I et "''·The U.S. """ the forst jurisdiction to establish 

such a comprehensive dcposi1 insurance regimt. 

" See. e.g .. Federal Res<l\c Act, Sec. 13(3). 12 U.S.C. § 344 (authorizing the Federal 

Reserve to provide emergency liquidity suppon to financial n>:rl<t~ subject to specified 

conditions). See o/so. Fed. Res. !lank ofM nncapol ~ Lemler of More fhtm1a!f Resor( ... 

REGtoxs (2002~ ami/able m hnos11wllw,minneaoolisfed.mg!publication.llthc· 

region!lendtr=Of-more·th>n·last·n.'son. 

"See Public Actors. 511pra note 39 (discussing the ta.xonomy of roles gowmmcnts 

p<rf()JTJ] in their capocities as market actors). 

" This essential hybridity is especially Yisible in the stn~tture and op<,..tion of the 

mod<m banking system, which is best understood as a public-priYate pannership - or a 

fr:tnchisc arrangement. Foran in-<lepth analysis of how this arrangement works in praetice, 

see Fitwnce Frtmchise. supra note 8. 

"'Sfeid. 
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8-Sep-18) FINTECH - drajl 15 

renegotiation and readjustment of this delicate balance. Financial institutions 

and their clients, searching for higher profits and competitive edge, keep 

pushing the I ine toward greater pril•ate freedom to transact, to "complete" the 

perennially "incomplete" markets by creating and trading in new financial 

instruments.31 They often do so by exploiting gaps and ambiguities in the 

existing laws and regulations and by deliberately structuring transactions to 

escape the application of unfriendly legal rules, a technique widely known 

under the label of"reg11latory arbitrage.''51 

This constant injection of privately created risks into the financial system 

creates quantitatively and qualitatively new challenges from the viewpoint of 

systemic stability, predominantly the public's responsibility.lnexorably, the 

public is in a reactive posture: once capital allocation decisions are made by 

private actors operating on a micro-le1•el, the macro-level modulation comes 

into play as a principally ex post response.5

' This fundamental logic both 

implicitly sllapes, and is reflected in, the widely-shared assumptions about 

the basic dynamics of finance: we take for granted that markets "evolve" and 

"innovate" (lhe primary, active, positive value-creation side of the publicprivate equation), while regulators "respond" and "react" {the secondary, 

passive, negative ham1-limitation side). These assumptions define both the 

policy and the discursive agenda: how financial regulators do, or should, 

respond to privately-driven financial innovation -and adjust regulatory tools 

and objecti1•es to the new context - is one of the perennial questions that 

" A ··comple<e S)'Stem of mark ciS" is one in which 1h<re is a matke~ for ""''Y good. See 

Mark 0. Flood, A11 lmroductiou to Complete Morktts, Fro. Rts. BANK OF St. UJIJIS ReVIEW 

32. J.l (March·April 1991), IIL'Oilable at 

hups;!ltitesJiloui<fed.orglfileslhtcfocsip!lblis;uion¥&ie"l9t/03&tar!;<!l Mar Aocl991.p 

!!f. For lhe original th<or<tical acoount, see Kenneth J. Mow & Gerad D<breu. Exi>te11ce of 

an £quilibrim11 foro Competilil~ &ouomy, ECONO)IETRICA 265 ( 1954). 

" The literaiUte on the naiUTc and role of regulalol)' arbitrage in 1he fmancial SCI'\ iees 

«<lor is 100 I'Oluminous to cite here. The rise ofloday's deril'alii'CS and "'PO markets, and 

1he grow1h of money markcltnutuat funds, for cxamplt. 11tre dir«l produciS of regulmory 

arbitrage and financial fim1s· desire to circumwnl spetilic n.~ulalory constraints on their 

ac1i1•ities. for a re<<nl book-lenglh a«ounl of lbes< dynamics. see ERIK GEROING.l.AW, 

8UB~Lf,S,AND FINANCI.IL REGUI ITIO~' (2013). 

ll This is, of course. a generalization. The poim 11m is not to say 1ha1 e~ ery specific 

sys1emic stability enhancing measure is ane.rpost response to a specific lransa<tion. Agency 

rules are prospectil'c in their application, Banking n~ulation and supcn ision, in panicular, 

in,•ol\'e regulatory agencies in prh-atc banks' balance-sheet managtmem with the \iew 

1oward prti'CIIIingthem from failure. Ne~·.nhttess. CI'Cn in lhatconiCXI, lheprinoipal posture 

of 1he public oversighl is oot 10 subsli1u1e its Olln. public-interest based, subs1amive 

judgment forlhal of the bank's managemcol in eve!)' instance 11hen lhe bank ~ extending a 

loan or emcrin,g imo a dcril•alil'e contra<!. These ~'P" of direct credil-money allocation 

d«isions are left 10 pril'ale bank managcts. RegulaiOI)' lirnitations on indil·idual banks' 

iel'crage, risk conccnormion, or liquidily posi1ion arc ck>signed 10 shape 1hesc choices only 

indin.'<lly and, in 1b~ sense. are fundamentally rcac1il•e. 
74 


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16 FINTECH - draft [8-Sep-18 

preoccupy scholars of financial markets and insti tutions.~ What goes 

unnoticed, however, is that this seemingly objective description of"how the 

world works" is itself, to a great e,xtent, a product of a nonnative choice as to 

the relati1•e competencies of private and public actors in financial markets.

11 

C. Pre-Fintech Erosion of the New Deal Seulement: A Brief Recap 

Technology plays a critical role in this process of continuous 

renegotiation and resetting of the public-private balance in finance. 

It is well known, for example, that advances in computing and 

communications technology since the 1980s enabled the rapid gro111h of 

increasingly diverse and complex derivative$ markets. Derivatives are 

bilateral contracts whose value is "derived" from that of some other 

underlying, or reference, asset.l6 Though the commonly encountered 

derivatives are linked to commodities, securities, interest or exchange rates, 

pretty much ~ny quantifiable- and, importantly, fluctualing - value can serve 

as a reference asset.11 Derivati1•es enable financial market participants both 

to hedge their existing or anticipated risks and to make essentially speculative 

bets. While simple derivatives appear to have been in use even in ancient 

times, it was only in the 1980s that financial finns were able to use their 

newly acquired technological capabilities to scale up derivatives trades and 

tum them into one of the fastest growing segments of global financial 

markets.ss 

A similar story unfolded in the market for securitized products. 

"For a 1\\Wll book-length treatment of this subject, see CRISTIE FORO, NNOVATIO~ 

ANO THE STATE: f'INAXCE. REGULA TIO~. AND JUSTICE (2017). 

" For an in-depth discussion and critique of this trnditional delineation of roles in 

finance. see P~blic Actors, supra note 39; Fitwnce Franchise. supra note 8: Nati01wl 

/nresrmem Am1roril)', Stlpra note 38: Saule T. Omaro\'a, &mk GorernoJlce mtd Sys1emic 

Swbili~·: The "Goldm Slmre .. Approadt, 68 AU.. L. REV. 1029 (20 I 1) [hereinafter, Gold~t 

SJmreJ. 

,. See generally )OilN C. HULL, OPTIONS, fUTURt:S. AKD 0TIIER DERIVATIVt:S {9th ed. 

2014): R. STAFFORD JO!l~SDN, INTROOUCTION TO DERIVATIVES: OPTIONS, fUTURES, AND 

SWAPS 1- 10 (2009). 

" As a genernl rule. the more \'Oiatile the underlying asset's '~lue. the more lucrnti,·e 

the related deri\•atives contrnct. Actordingly, derivatives contra<IS may be linked to things 

like inflation rntes, naturnl catastrophes, or even financi•l market ''olatility itself. 

" There is a volurnitlOUS body of scholarly and popular litcmture de1ailing the hi ~o , 

economic functions, legal status.. institutional struc1urc, and financial stability implic-ations 

of deri,·ati,·es markets instruments. For a small samplt. see Mark J. Roe. The Derimti•-es 

Market 's PO)~rent Prwrities as Financial Crisis Aec<lerator. 63 STAN.l. REv. 539 (201 I); 

lynn A. Stout, Deril'ati•-es and tlte Legal Origin oftlte 21XJ8 Credit Crisis. I liAR I'. BUS. l. 

REV. I (2011); Dan Awrey, The Mech011isnts ofDeril'llti•-es Market Efficimcy, 91 N.Y.U.l. 

REV. I 104 (201 6); Saule T. Omaro,·a, The Quiet Metmnorphosis: /low Deril'llli•-es Cltangro 

tlte "Business of Banling ... 63 MIAMI L. REV. I().! I (2009) [hereinafter, Tlte Quiet 

.11211111/0Tphosis). 
75 


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8-Sep-18) FINTECH - drajl 17 

"Securitization" generally refers to the practice of pooling revenuegenerating assets, such as mortgage or credit card loans, and using the pooled 

assets as collateral backing the issuance of debt securities to investors. 5

9 

While not a recent invention, securitization became a major market-driving 

phenomenon in the 1980s, in large part because the advances in technology 

enabled originators and securitizers of loans to create much larger and more 

complex pools of securitizable assets and to manage the risk-return stmcture 

of debt securities backed by such assets. By the early 2000s, the market for 

these ''structured" asset-backed products- including highly complex multi· 

layered schemes such as "collateralized debt obligations" (COOs)-grew to 

unprecedented levels.$) Moreo1•er, the gro111h of securitization was 

intimately connected to the gro111h of derivatives markets, mainly through 

the use of credit derivatives to stntcture asset-backed claims.61 

Both derivatives and stmctured asset-backed products are heavily 

dependent on the capacity of their creators to mn increasingly complicated 

computer models.

61 The principal economic function of these and many other 

complex financial products is to allow for isolating, pricing, and trading 

specific risk factors embedded in, or constituting, the same otherwise 

indivisible asset. This process of synthetically constructing tradable financial 

claims out of deconstructed traditional assets- shares of stock, loans, or 

commodities- requires sophisticated anal)1ical tools and computing power. 

As a result, today's highly structured financial products- marketed and used 

as both risk-management and risk-taking tools- are also, to a great extent, 

rech products.6; 

Importantly, however, the technology that enabled derivatil•es and other 

structured finance transactions was proprietary in character, developed and 

owned by financial institutions dealing and trading in these markets. That 

rendered the tech component of complex financial products less visible and 

more subsumed in their overall economic ftmctions and eOt<:ts. The latter, 

,. See Kenneth C. Ke11cring, Seeuritization mrd Its DisCf)lllellls: Tire DJ~mnics of 

Fimmrial Product De~~loprnenr, 29 CAROOZO L. REv. 1553. 1556 (2009); Jonatl>an C. 

LipSOfl.lle: Deji11ittg Securiti!lltio11, 85 S. CAl.. L. REV. 1229. 1257 (2012). 

'" See generalo·, fiN. CRISIS INQUIRY C0M)i"N. TilE FINAX0,\1. CRISIS L'Q\IIRY 

REPORT: fJN1\l REPORTOFTIIE NAJIO~ \L COW~oliSS[ON ON TilE CAUSF.S Of FINAXCI1\LANO 

ECOXO)!IC CltiSIS IN TilE UNIIED ST,\l<S (201 1), hiips:f/~""'·t>!!0·80\' fdsysfpkg/GPQ­

~ S. ER.IIAXE~'TSUBCOMll, ON INVESTIGATIONS, 112111 CONG., II' ALl.S'TRfET ANO 

THE FINANCIAL CRISIS: AN>ITO)IY Of A FINANCIAl COllAPSE (2011), 

hno:llhsgac.sg•ate.gov/ooblili fii!$1Financial Crisis!FinanciaiCrisisRqxl!l.pdf. 

"See id. 

"See Erik F. Gerding. Code. Crash. and Opett Source: Tire Outsourcing of FirNmcwl 

Regula/ion 10 Risk Models aml1he Global FiiUI!tcibl Crisis, 84 \V ASII. L. RF.V. 127 (2009). 63 Such familiar terns as ll.!'nns as '·financi31 engineering:• ··quams." ··rocket scientists,'' 

and "legal technology" may, on some lml. reRectan intuili1·c recognition oflhis underlying 

conllt--clion. 
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18 FINTECH - drajl 18-Sep-18 

of course, were often insepamble from the legal or regulatory functions and 

effects. Complex financial products are economically attracti1•e not only 

because they allow for a more fine-tuned, bespoke tailoring of risks and 

returns of financial investments but also because they often lower the costs 

of such investments by circumventing specific laws and regulations. 

Accordingly, regulatory arbitrage is a strong driver of ''innovation" in 

financial markets.

61 Much of such innovation is, in fact, little more than a new 

way of avoiding regulatory limitations and compliance costs.6j Deregulatory 

policy choices, both fonnal and infonnal, further magnify and support these 

strategic shifts of financial activities from the traditionally '\veil-lit" 

regulated areas to unregulated "shadows" of the same economic markets.66 

This is in essence the familiar story of the emergence and gro111h of the 

control ersial "shadow banking'' sector.61 The tem1 "shadow banking" does 

not have a finnly defined meaning and refers generally to a variety of 

financial markets and activities that mimic the economic substance of bank· 

like credit-money creation without being subject to the same kind of 

regulatory oversight.68 Both derivatives and securitillltion markets are 

routinely cited as key examples of shadow banking in action: in both of these 

~See s11pra no1c )2 and arcompanying 1e~1. 

"See FORD, supra not~ 54: Dan 1\wrey. Complexity.lmro•vtioll 011d the Regulation of 

Modem Finlltzcial Markets. 2 H,IRI'. BUS. L. REV. 235 (2012) (discussing SliJl~ly de 

incenth·es for financial institutions to engage in socially suboptimal .. innO\'ation" as a means 

of generating sllOII·temt monopoly-like rents). 

'" For in-de~llt analysos of lhc hidden dcr,-gula~ion dynamics, set nze Quiet 

Metamorphosis, Sll{lfl/ note 58 (detailing how the na!ional bank regulalor, omcc of the 

Comptrolltrofthe Currcrn:y. used informal deeision·making tools to expand deposit-taking 

ins.litulions· powers 10 trade and dl!'ill in deri\·ati\'es instruments}; Saule T. OmarO\'a. From 

Grmnnz-Leaclz-8/iley to Dodti-Fra11k: tlze U11jit/jilled Promise ofSection 2JA of the Federal 

R>!SetW Act, 89 N. C. L. Rcv.l683 (201 t) (detailing how llte Federal Reserve used its 

infonnal administrati\'c powr;:ors to loosen imponant stanuory restrictions on banks' 

trans.1etions wi !h aniliated entities). Fora broader account of lltc dc"-gulatory dynamics in 

financial bubble-bust cycles, set GEROtXG, s11pro note 52. 61 There is a huge literature on shadow banking. especially in the post-2008 era when 

the term b«ame synonymous with excessi,-e systemic risk creation. For a small S3mple of 

this literature. see, e.g., Tobias Adrian & 11)1111 S<>ng Shin, n:e Shado••• 8anki11g S)>t<m: 

lmplicatiolls for Fi11anciol Reg11lolibn. Fed. Res.. Bank of NY Stafl' Repon No. 382 (July 

2009); Tobias 1\drian. Adam B. Ashcraft, Nicola Cctor<lli, Shadow Bank Monitoring, Fed. 

Res. B3nk of NY Smn· Repon No, 638 (StJll. 10 13); Gary Gonon & Andrew Metrick, 

Reg11lati11g the Shadow lkmki11g S)>tenz, Brookings PaP"r on Econ. Acthity (201 I): 

GERDIXG, s11pra note 52. a1 395-470; Morgan Ricks. Mo11ey a11d (Siuzdo•) Brmkuzg: A 

nzo11glzt £.<perinzem.31 RtV.OF8,1NKtXG& FIX. L. J3t (2011-!2). 

" The tcnn was coined by Paul McCulley. Paul McCulley. Tet011 Rejlmiolls. Global 

Central S.nk Focus (PIMCO) (Sept. I, 2007) at 2. omilllble or 

hnp11eawsile.coounonweallh.com!EasvSitesiF.a">Site Z3263YI uploa4s11"!10n%20Refl«: 

tioll•.odf. See nlso. Bryan NO<th & Rajde<p Scngup1a, Is Shodoll' lkmluzg Really Bmzkitrg? 

1)tE GIO~l Eco~OMtST. Fed. Res. Bank of St. Louis (October 20t 1), at S-!3. 
77 


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8-Sep-18) FINTECH - drajl 19 

markets, various regulated and unregulated financial institutions 

continuous!)• generated ultimately unsustainable levels of leverage and risk.69 

This excessi,•e risk-creation was at the root of the global financial crisis that 

began in 2008, when the elaborate system of complex structured products and 

derivatil•es sitting on top of risky subprime mortgages collapsed with a 

frightening speed. 

This story is, of course, well known and widely 11Ti1ten about. The 

intei]Jiay of''financial innovation" (i.e., technologically-enabled large-scale 

trading in derivatives and other structured financial products) with pervasive 

regulatory arbitrage (i.e., using transactional techniques to defy structural 

boundaries) and gradual deregulation (i.e., fonnally eliminating or infonnally 

loosening risk-limiting rules and conditions) C\'entually led to the world's 

worst systemic financial crisis in eighty years, followed by a prolonged global 

economic recession.10 

A brief recap of this narrative, however, helps to highlight the more 

fundamental dynamics manifested in the rise of shadow banking: the gradual 

erosion of the New Deal settlement, as the contested public-private balance 

shilled toward an increasingly greater private freedom to make allocative 

decisions dctennining the types and levels of risk in the financial system, 

without the proportionately necessary increase in the public's ability to 

manage credit-money aggregates. Moreover, while the sphere of public 

control over financial risk-generation diminished, the scope and scale of 

public accommodation of privately created liabilities in financial markets -

both old and new, well-lit and pitch-dark - dramatically increased over the 

same period. 71 The events of2008-2009 shai]Jiy exposed the practical effects 

of this fundamenlal imbalance: privately crealed allocative distortions in 

financial markets led to unsustainable accumulations of risk and leverage in 

the system, and the public had to "clean up" the resulting mess. In this sense, 

the popular reference to "privatization of gains and socialization of losses"12 

aptly captures the dynamics of erosion of the New Deal settlement in the 

financial secw. 

The Dodd-Frank Act, the most far-reaching legislative refonn in the U.S. 

financial sector since the New 0..'31, was an effort to curb some of the most 

111 See rourccs cit<'li supra 11\lte 67. For a more 1.11ge1td disrussion of 11M: spctific 

me<hanisms 1hrough which shadow banking amplified <rc'<ii1-money aggn,-ga1es. su Filla nee 

Fnmclrise. supra n01e Salil75-t 192. 70 See sources tiled Sllpro note 67. Gf..Rl)ING. supro nole 52, pro\ ides a comprthensh·e 

analysis of lhese I rends in lhc pre-cris~ de<ades. 

" For a detailed analysis of lhis ine1 iiable e'pansion of public acoommodalion, su 

Firumce Fralltirise. sllpra n01c 8all l75-1 192. 

"See. •.g .. Joseph Stiglirz. U.S. Docs No1 Hm~ Copilalism Nou·. CNBC.cO~I (19 Jan. 

20 10). ami/able 01 hnos:l/\"""·cnbqomlidf34921639. 
78 


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20 FINTECH - draft [8-Sep-18 

visible manifestations of this imbalance.

13 The Act explicitly sought to 

reinsert public agency and public interest in finance, among other things, by 

articulating 1he overarching policy goal of protecting systemic financial 

stability and by institutionalizing system-wide oversight of the financial 

sector.'' Yet, despite these important measures, the Dodd-Frank Act did not 

alter the substamive basis of the New Deal seulement, discussed above.

1

; 

Thus, the old silo-based structure of the financial sector oversight remains 

almost entirely intact.76 The new macroprudential regulatory regime 

essemially utilizes scaled up microprudential toolsn And, to the extent 

Dodd-Frank seeks to restrain potential risks posed by derivatives and other 

structured products, it does so only indirectly, through demanding greater 

disclosure, encouraging standardization and centralized clearing, and 

incentivizing more prudent risk undermiting by private parties.11 

More generally, under the Dodd-Frank Act, the public still does not have 

any direct involvement in or control over allocation of financial capital, a 

traditional sphere of private dominance. In fact, by reconfinning this pre· 

crisis under.;landing of the relative competencies of private and public actors 

in financial markets, the Act further exacerbated the deep-seated tension 

within the New Deal paradigm. 

D. Fimeclt wu/11te New Deal Sen/eme/11: Refi'aming 11te Inquiry 

It is in this context that the fintech "revolution" began to change, or 

"disrupt," the way financial transactions are conducted and financial services 

are delivered.

19 Its game-changing potential, however, extends beyond the 

pure transactional aspects of finance. This Article argues that fintech is 

emerging as a powerful new tool for reselling the current public-private 

balance in finance. Does it ofler a unique opportunity to correct the 

structurally destabilizing imbalance between private generation of financial 

risk, on the one hand, and public accommodation of such privately-generated 

risk, on the other? Or will it operate to intensify this imbalance? If it is the 

!alter, does that mean that fintech is going to be the prol'erbial last nail in the 

" Dodd-Fnlnk Wall Stn.~t Reform and ConsumerProte<:tion Act of20t0. Pub.l. Il l· 

203, H.R. 4t73 (si£1l"l into law July21. 2010). 

,, See Dl\ VIO SKEEL, THE NEW FINA~CIAL DEAL: U~OERSTANDING TilE Dooo-fRANK 

ACT ,\NO ITS (UNt~'!F.NDED) CONSEQUENCES (20t 0); A New Dta/for A Ne>l' Age? suprtl note 

10, 

" See supra Pan I.B. 

f6 See lustNuriouol Stntcmre. supra note 9. 

n See Roben Hockett. The Mllfroprudenlwl Tum: From ln.s1ituli01wl Safe~· and 

Soundness' 10 S)'stemotic 'Financial Siabili~·· in Fimmciltl Supen·ision. 9 VA. L. & Bus. REV. 201 (2015). 

" See. A A'<nl' Dta/f()r A ,v.,,. Age? supra note tO, at 96-97; AIITC)', M«lwnisrns. supro 

note58. 

" See supra Pan I.A. 
79 


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8-Sep-18) FINTECH - draft 21 

coffin of the New Deal settlement in finance? 

These questions help to reframe the key inquiry into the nature and 

systemic impact of fintech. Ultimately, understanding fintech as a systemic 

phenomenon - as opposed to a mere collection of discrete finance-related 

applications of digital technology - n.>quires analyzing whether, and how, 

specific fintech applications affect the public's capacity to maintain the 

stability of the macro-environment. This reframing allows to overcome the 

current fragmentation of the fin tech debate by redirecting it away from the 

familiar but ultimately unproductive themes.80 It also enables us to situate 

fintech in the broader analytic3l and nonnative context as an integral part of, 

or the latest phase in, the decades-long process of gradual renegotiation of 

the New Deal settlement in finance. 

The fundamental continuity in this process is hard to miss. Despite its 

"disruptive" appearance, today's digital technology largely facilitates and 

amplifies certain long-standing trends in modem finance. In this sense, it is a 

continuation of the core pre-fintech dynamics in financial markets, whose 

cumulative effect to date has been the gradual "unsettling" of the New Deal 

settleme.nt. At the same time, however, qualitatively new technological tools 

can elevate these built-in tensions to a qualitatil•cly new level, potentially 

demanding a qualitatively new political settlement. 

This means that fintech is properly conceptualized not so much as 

"re1•olutionizing" finance as providing new channels for the operation of the 

fundamental financial market dynamics predating it. From this perspective, 

it is important to resist the obvious temptation to focus on superficially no1•el, 

micro-transactional aspects of fintech. In the final analysis, the systemic 

significance of the unfolding fitech revolution is in its- not yet fully known 

- potential to redefine the basic patterns of interaction between the private 

and the public sides of modem finance. 

Accordingly, the first step toward understanding fintech as a systemic 

rather than transactional, or micro-/ere/. phenomenon is to re-examine from 

the new vantage point the underlying drivers of the changing public-private 

balance in finance. llle project of decoding the fintech revolution, thus, 

begins with reassessing what we already know about the functioning, and 

mal-functioning, of financial markets and institutions, in light of what we are 

teaming about new finance-related technologies. 

So, what exactly do we know about the functional dynamics of finance? 

10 One e.x.mple of such a familiar theme is so-called '"disintenncdiation" of incumbent 

financial institutions by fintech entrnnts. While these typeS of shift in the structure of specific 

martel segments undeniably rnise impo~ant regulatory iSSU<S, concqnualiting the broader 

fintech d)namics in tenns of"disint<mlediation" is ne.."<ilessly n.'<loctionist and unhelpful. 
80 


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22 FINTECH - draft [8-Sep-18 

II. TilE LOGIC OF FINANCIAL INNOVATION AND EROSION OF TfiE NEW DEAL 

A. Focusing the lnquhy: Secondmy Markets in Financial instruments 

As discussed above, one of the key features of the New Deal settlement 

in finance was that it left the critical task of credit· or capital-allocation to 

private market actors.

8t This is true despite the fact that federal laws and 

regulations impose limits on the ability of the least sophisticated, and 

therefore most vulnerable, financial market participants to invest in certain 

high-risk financial instruments.82 These investor-protection measures operate 

primarily to draw the intra-sectoral lines separnting more strictly regulated 

retail markets from institutional, or whole.sale, markets subject to much 

lighter oversight. But they do not - nor were they ever intended to- put the 

regulators in charge of making specific investment choices on behalf of retail 

market participants. In retail as well as wholesale financial markets, private 

investors have the ultimate power to decide which financial instruments to 

buy - or which risks to take on- and at what price. 

Private actors also decide which financial instnunents to o!Ter for sale to 

both retail and institutional itwestors. Companies issue securities and takeout 

loans, banks offer deposit accounts, insurers sell insurance policies, asset 

managers set up funds, investment banks create structured products, and 

derivatives dealers stand ready to take the other side of swaps. The 

government does not control these decisions, as long as the relevant private 

parties make required disclosures and otherwise conduct their businesses in 

accordance with the applicable rules. The U.S. has no system of substantive 

risk assessment and regulatory pre-approval of individual financial 

products.SJ 

Instead, under the temts of the New Deal political bargain, the 

government's principal role is to provide macro-stability, not only by 

regulating but also by directly backing private financial markets.

14 This 

public backup should not be confused with, or reduced to, what is simply its 

most visible and concrete manifestation: a government bailout of private 

finns. As argued elsewhere, public accommodation of privately created risks 

and liabilities is the defining dynamic in a modem financial system, one that 

11 See supra notl'.S 3&-11 and accompa!t)~ng t~~l, 

~ For example, under the U.S. regime of Sc.--curities regulation, retail in\'estors are 

disallowed 10 im·est in privately plactd securities pursuant to tile SEC Rule t44A, which 

limits pennissible purchaser.; to institutional im·estors. See 11 CFR §240.144!\. Similarly, 

retail investors cannot im·est directly in hedge funds. priloiC equity funds, or Ollter funds 

exempt from registration and regulation as "im·esunent corn(Xlnies'' under the Investment 

Company ACIOf 1940.See 15 USC§803-3(cX7). 

SJ See s11pra nolc 40 and accompanying 1cx1. 

" See supra notes 4&-49 and accompan)ing text. 
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8-Sep-18) FINTECH- draft ?' .J 

can be traced throughout all of that system's interconnected layers.85 Public 

accommodation is what ultimately enables financial flows on the systemic 

level and unden1~ites the gro111h of putatively private capital markets.16 

Inevitably, however, public accommodation also creates powerful 

structural incentives for om-generation of financial risks by rent-seeking 

private parties.

81 h incentivizes the creation and proliferation of financial 

products - and the related gro111h of secondary markets in which such 

financial pro<lucts are traded. This built-in incentive for constant reproduction 

and gro111h of secondary markets is a fundamental, and fundamentally 

underappreciated, driver of what is routinely understood as financial 

innovation. 

Standard accounts of finance use primary markets as the archetypal 

sening in which "financial intennediation" takes place: the savers of money 

extend loans or invest in the equity of the users of funds, with the mediating 

help of a professional financial intennediary.

88 llte intermediary- a bank, a 

securities dealer, or an investment fund- is said to "transfomt" all or some 

of the key i~k anributesembcdded in the transaction. This is what is typically 

described as maturity, liquidity, or credit risk transfonnation: a set of 

functions typically perfomJCd by banks, the quintessential "intermediaries,'' 

and replicated in part by non-bank financial institutions.

119 

This nanrative, which remains the dominant intellectual framework for 

analyzing the financial system dynamics, is fundamentally misleading.90 

Among other things, it masks the independent significance, and indeed de 

" It is this public 3CCOntmodation - often unseen or taken for granted - that 

fuooamentally <nables and undm1ntes the financial flo11~ in the S)~ttm: from the banking 

sector. throug)! capital martets. to the outer roges of the eon~ant y evoh•ing '~hadow 

banking.)' See Finance Fr(l}zc/zise. supra note 8. 

"See it/. 

"See it/. 

"' This is, lite-rally, a tc~tbook understanding of ··r.nancial intcmuxliaticm." Set Zvt 

ilooiE& ROBERT C. MERTON. FINANCE 22-23 (1000); ~\'T ATHE\\~ & JOt!NTHo~t?SON. 

n1E ECOXO~ItCS OF B.INKtNG 33 (2005); 0ARil>IR,I CASU Ef AL., 'TROOUCT ON TO 

8,\NKtNG 18 (1006): STUART I. GREEN8M.I~I & ANIAN V. ThAKOR. CO~'TEMPORARI' 

ftNANCtAL I~"TER.IIEOtATtON 55-58 (1007): STEPIIEN G. CECttEITt & KER.IIIT 

SC!tOENilOLT7- MONEl', BANKING, AND ftNoiNC~IL MARKtm 39 (3~ ed .. , 1008); RICHARD 

ScolT CAR~ 'ELL ET AL., nm LAW OF flNAXCI.IL INSnlUTtONS 37 (5° 00., 1013). 

" Rcferenres tO cn:dit, ma!Urity, and liquidity tmnsforntation as the core functional 

features of banking and, by extension, "shad oil' banking" are 100 ubiquitous to cite. What is 

interesting for the purposes of the pTI-'Sent discussion is that this eonecptual apparatus 

presupposes a specific purpose behind the intenn<diatro transaction: mol'ing capital from 

the in,·estor-sa,er's hands into the hands of a prodocti1·e user-entn:pren<ur. While not stated 

explicitly, an implicit presumption here isthat the "user" is seeking funds for some legitimate 

economic usc ilnd not for a speculative financial reim·estment. 

w For a dcmiled c~plana ion of why the "financial imenncdiation"' onhodox~· is 

fundamentally misleading, see Fi1111nce Fr011chiu. supra noteS. 
82 


91818024.eps

24 FINTECH - drajl 18-Sep-18 

facto primacy, of secondary-market dynamics in the modem financial 

system. In primary market transactions, the emrepreneurial'\osers" of capital 

issue securities and incur loans primarily for the purpose of funding nonfinancial economic enterprise, thereby taking capital out of the financial 

system and putting it to productil•e use in the real, i.e., non-financial, 

C(;Onomy.

91 This feature of primary markets operates as the key "safety 

valve" that keeps the financial system from outgrowing the economy's 

capacity to absorb capital at any given moment. In other words, primary 

markets' ability to generate financial claims, and thus financial risks, is 

inherently subject to certain extemally-detem1ined limits. 

In the vast majority of real-life financial transactions, however, market 

players borrow and issue various financial claims in order to invest in other 

financial claims. Unlike one-ofT primary-market issuances used to fund 

companie~' investments in operating assets, secondary-market transactions 

fund investments in jin(mcial assets. This seemingly trivial diflerence has 

critical consequences. Thus, largely as a resull of the legal and financing 

technologies developed specifically for this purpose, there is no "natural" 

(i.e., independent from the operation of the financial market itselQ limit on 

the volumes of financial claims - i.e., financial labilities - traded in 

SC(;ondary markets. In principle, an unlimited number of market participants 

can enter itllo an unlimited number of secondary-market transactions 

involving an unlimited varietyoffinancialclaims and liabilities. To the extent 

these privately created claims/liabilities are publicly accommodated, eiiher 

directly or indirectly, they amplify- potentially indefinitely - both private 

markel panicipants' rents and the public's aggregate risk exposure. 

This basic relationship explains why today's secondary markets in 

financial instruments are the principal sites of both relentless transactional 

"innovation" and chronic over-generaiion of systemic risk. It also explains 

why secondary markets in financial assets currently dwarf primary markets 

in tcm1s of size, complexity, and systemic significance.?! Tllis is both a 

srnoctural and a functional imbalance. In theory, secondary markets' main 

function is to support and facilitate primary capital markets by providing 

liquidity, price discovery, and risk-shifting (including exit) opportunities for 

primary market participants. In practice, secondary market trading often 

" Of cour'St'; as business emitics. financial n~itutions also raise ~pi al by issuing socuri1ies in primary markcos Of borrowing money. The poin1 here is ohao, in ohe ~andaJd 

picture of how c.opi!al marl;eos operate, companies issue cquioy and raise debo in O<dcr to 

suppor1 or expand their ··real-economy" business operations that genera1e jobs and w(tlllh. 

This is the implicit nom1ativc justifirntion for financial imenncdiation as a socially ,·atuablc 

acoh•ity. II isdi1lkuh too1·cr<Stimate the signilkantt of this implicit norma~ivc assumption. " See. e.g .. WO<Id Fcd<raoion of E."hanges, 1017 Full YetiF Markel lligh/ig!rts. 

file:l/iC:/Useo; sto24 DmmtoadsiWFE%20FY 20201 Jt/,20~ arl:co%201l lishLS JXIf 

(providing S!allsoirnl brcakd0\\11 of annualorading volumes on global exchanges). 
83 


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8-Sep-18) FINTECH - draft 25 

determines the tenns and volumes of primary issuances of financial claims. 

The rapid rise of tmsustainably risky subprime mortgage lending in the early 

2000s, in response mainly to the rising demand for such loans as the raw 

material for MBSs and COOs, provides a vivid example of these inverted 

dynamics.') 

Inexplicably, however, the significance of this shift in the financial 

system's center of gravity- from capital-raising in primary markets to risktrading in secondary markets- has not been fully appreciated and examined 

in the academic and policy discussions. Even in the post-crisis era, the 

"financial intennediation" discourse effortlessly glides over the fundamental 

differences between primary and secondary market d)qtamics, blending them 

together under the superficially descriptive labels of various balance-sheet 

"transformation" functions." Within these discursive parameters, the 

principal focus of the mainstream policy debate is on potential means of 

fortifying financial intennediaries' balance sheets, whose inherent fragility is 

presumed to be a necessary feature of a thriving financial system. This 

nonnative and conceptual stance, in tum, heavily favors self-consciously 

technocratic approaches to both analyzing developments in financial markets 

and framing regulatory responses. Little, if any, attention is being paid to such 

"big" nonnative questions as the underlying causes of the persistent- and 

steadily increasing- tension between the public and private interests, roles, 

and respecth·e compe.tencies in the financial sphere. As a result, there is 

cunently a conspicuous gap in our collective understanding of the eflicacy 

and social desirability of combining primte freedom to create tradable 

financial risk products 1~ h public responsibility to backstop secondary 

markets in which such products trade. 

To fill that gap, and to de1·elop a fuller and deeper understanding of the 

systemically destabilizing logic of "financial innovation," it is necessary to 

refocus the inquiry on the core dynamics in secondary markets for financial 

products. In doing so, it is important to move beyond the familiar descriptions 

of maturity or liquidity "transformations" appearing on, or otT, various 

balance sheets. Instead of dissecting how various finns ''intermediate" in 

various transactional contexts, we should shill our efforts toward identifying 

and examining the principal mechanisms and techniques that enable private 

actors to create and grow - continuously and 1•irtually unconstrained -

" Sit SCUf<'(S cited supra nOic 00; GARY GORTON, SLAPPED BY THE INVISIBLE HAND: 

TilE .~~IC OF 2007 (200$). 

" ''Maturity'' or ''liquidity" tr.msfonnation is the same balance-sheet phenomenon in 

any tr.lnsactiooal setting. In the canonic primary-market context of banking, this structural 

balance-sheet frngility is beliel'ed to serl'c a socially beneficial purpose, thus justitying an 

explicit public ba<kup for ronks. The same logic is then easily extended to the same types of 

botanre-shcet frngitity n.'Sulting from se<ondal)'-mari<<t actil'ities. 
84 


91818026.eps

26 FINTECH - draft [8-Sep-18 

secondary markets for financial risk trading. 

B. The Mechanisms ofSecondmy Market Proliferation: A Preliminmy 

Taxonomy 

As argued above, the fundamental division of roles built into dte New 

Deal seulement creates strociUral incemives for the disproportionate growth 

of secondary markets in tradable financial assets.

95 The bulk oflltese tradable 

assets are "produced" for reasons 1ha1 have linle 10 do with "capital 

formation"- or canonical capilal allocation- in primary markets. To put it 

simply, financial producls are bundles of financial risks and returns 

manufactured by financial institutions for sale to other market participants, 

mainly portfolio investors or managers. 

From a micro-level transactional perspective, this is typically viewed as 

a valuable financial service. We are all familiar with the s1andard vocabulary 

that conveys this nomtative assessment in ternts of "providing liquidity," 

"completing markets," "discovering prices," "enabling diversification and 

risk management," or "creating portfolio-enhancement opportunities." From 

a macro-le1·el systemic perspective, the principal consequence of 1his 

continuous manufaciUring of financial products is the continuous injection of 

privately-cre-ated financial risks into the system. Yet, we do not currently 

have a sufficiently extensive and well-established vocabulary to articulate 

this systemic perspective as a valid counterpoint to the dominant transactional 

view of financial markets' operation.96 

Developing such a vocabulary is no easy task. It requires taking a fresh 

look at the familiar phenomena in an effort to identify important overarching 

trends and dynamics that were either unnoticed or unappl'l.>tiated in previous 

accounts.

97 lt requires a new nanrntive that helps to explain how, through 

which mechanisms, secondary markets in financial instruments are able to 

grow and proliferate. 

There is, of course, a well-kno1111 (though not entirely uncontested) 

nanrntive of now various market "innovations" in recent decades-including 

money market mutual funds, wholesale derivatives and repo markets, and 

complex securitized products - emerged in response to, and were enabled by, 

specific legal and regulatory developments.9S This Article neither replicates 

nor challenges that story. Instead, it seeks to take the analysis to a higher level 

of abstractioll by drawing out the broader- more fundamental and unifying 

" See supra noles 85-93 and accompan)ing I<>:L 

" In our pre1·ious work, Roben Hockett and I hare begun !his projecl of de~ eloping a new roncepmat vocabulary of modem finance as a systemic ph<nomenon. See. e.g .. Fi11ance 

FrMthise. s11pra note 8; P11blic Actors. supra note 39; Fixer-Upper. supra note IS. 

" See ill. 

" See supra ?an I. C. 
85 


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8-Sep-18) FINTECH - draft 27 

- dynamics behind these and many other developments. The purpose of this 

exercise is to develop a preliminary taxonomy of core dynamics operating in 

se~:ondary markets for financial instruments.99 

Inevitably, any attempt to construct such a taxonomy nms into 

definitional and boundary-drawing difliculties. The constant gro111h and 

complexification of financial markets is a multi-lel'el process, with a 

seemingly infinite variety of "elements" and "factors" interacting in a 

seemingly infinite variety of ways. It is nearly impossible to isolate any 

specific such element or factor with surgical precision. It is nevenheless 

possible, and potentially more informative, to focus on the fundamental logic 

behind these factors. 

I. The Market's Modus Operandi: Synthesizing and Scaling Up 

At the most abstract level, the gr0111h of financial markets is best 

understood by reference to two interrelated practices: (!) synthesizing 

financial assets, and (2) SCllling up transactional activity. To put it simply, 

both the scope and the scale of financial markets increase when (I) more 

products can be purchased and sold, and (2) more trades can be made in these 

markets. 

The practice of synthesizing financial assets typically involves creating 

new types of financial claims out of the existing ones. Some of the most basic 

and familiar examples include creating tradable stock indices, '"iting options 

on gold or shares of common stock, securitizing loans, and e1•en setting up 

mutual funds. In all of these cases, a relatively small range of traditional 

financial assets - common stock, corporate bonds, loans, or commodities -

serve as the base on which a potentially unlimited number of new types of 

financial cla.ims are created. lmponantly, the standard economic logic of 

supply and demand does not constrain this process. An increasing supply of 

tradable assets- or items on the menu of choices available to financial market 

panicipants - generates an increasing demand for them, which in tum 

incentivizes more asset-synthesizing.

100 And le1•erage plays the critical role 

in enabling this iterative supply-demand pattem.101 

The resulting proliferation of tradable financial claims is itself an 

imponant measure, and a detemtinant, of the quantitativegro111h of financial 

markets. The concept and practice of "scaling up" - i.e., increasing the 

" For case of reference. and unless o1hcnrise specified, I will refer 10 S<'<Ondaty marl: CIS 

in financial instrumcniS as simply "financial markciS." 100 See SOOJCCS ciled supra no1e 65. 

'" In lhal sense, today"s high finance may be said 10 follo11· 1he S1arbucks business 

model, in which the con~anl inrcnlion and markeling of new, imenlionally and carefully 

dificren1ia1ed, JjltOOUCIS mates ils 011n demand. JuSIIikc I he S~arbucks dcsigner-be1·erages, 

mosl complex financial producrs are made using the same basic ingredicn1s. Le,·erage, of 

course, fune1ions much like cafteine 1ha1 keeps e\'cryonc con•ing bac.k for more. 
86 


91818028.eps

28 FINTECH - drajl 18-Sep-18 

volume and veloeity of transacting - is another fundamental detenninant of 

such gro111h. Scaling up is achieved through a wide variety of means. Market 

inti-astructure and transactional technologies are of special imponance in this 

respect. for example, centralized trading platfonns (fonnally registered 

exchanges, alternative trading networks, or dealcr-nm pri1<ate pools), 

clearinghouses, and payments systems all enable far greater 1•olumes of 

trading to take place at greater speeds than would otherwise be achievable. 

Similarly, greater standardization of financial instruments helps to increase 

the volume of trading, at time.s dramatically, as in the case of the International 

Swaps and Derivatives Assoeiation (ISDA) doeumentation for derivati,·es 

contracts. IO! The ISDA example also shows how targeted changes in the 

applicable legal regimes can effectively unloek the gro111h of entire markets 

for financial products.103 Finally, algorithmic trading is perhaps the most 

readily available example of rapid rise in the veloeity (and, by extension, 

volume) of transactions as a result of the sheer expansion in technological 

capacity.11)! 

Synthesizing financial assets and scaling up financial transactions are two 

fundamentally S)~lemic practices, universal modes of operation at the very 

core of financial markets' logical design. Not surprisingly, they both have 

profound stmctural implications. Thus, the introduction of new financial 

products often leads to the emergence of new specialized markets in which 

they arc traded. New actors may enter the.se newly created markets, both on 

the sell and the buy sides, while the established financial institutions may 

assume ne11• roles in them. New patterns of market concentration and 

systemic interdependencies take shape. Via the multitude of specific 

transactional channels through which the twin imperatives of synthesizing 

and scaling up operate, the financial market grows not only bigger and faster 

but also more structurally complex. 

It is, of course, impossible and ultimately unnecessary to enumerate all of 

these specific channels. It is nevertheless helpful, for anal)1ical purposes, to 

identify the key mechanisms market panicipants use to synthesize financial 

assets and to scale up financial transactions. 

"':;., hnos1An•w.isda.QN!bookloompl<ie-isda-docum<n13tion-oockml. 

'" This r~fc~S specifically to ISI)A's successful campaign to serure JWfcremial 

lrtalmenl of d<ril~iil es under ilw U.S. BankrupiC)' Code, as well as under many o1hcr 

jurisdic1ions' insolwncy Ia" · S<e Ste\'en L. Schwarcz & Ori Sharon. Tire 8/nrla-uprcy-/.alr 

Safe Harbor for DeriiYttio-.s: A Poth·Depell(lence Ana6•i~ 71 WAS! I. & LEE L. REI'. 1715 

(2014). 

"' For a g<ll<f31 o•·miew of algori1hmic trading and issues il raises under U.S. se<urilies 

Jaws, se<, e.g., SlOven R. McNamara, Tlte /.alrmul Etllics of Algorithmic Tradittg, 17 Ml~ . 

J.L.SCJ.&TECll. 7t (20t6); Yesha Yodav,llowAigorithmicTrlulingUtultrmilles£fficiency 

in Copiu.tMnrliets. 68 VruXD.L.REV. t607 (20t5). 
87 


91818029.eps

8-Sep-18) FINTECH - drajl 29 

2. The Four Mechanisms of Synthesizing Assets and Scaling Up Trading 

Activity 

At first approximation, there are four such mechanisms that may be 

broadly - and inevitably somewhat imprecisely - temted "pooling," 

"layering," "acceleration'', and "compression." These anal}1ical categories 

refer not to any panicular type of product or transaction but rather to systemlevel operational principles, or core techniques that enable financial markets' 

continuous reproduction and expansion. In this sense, each of these categories 

may be seen as a transactional meta-technology, an embedded system 

timctionality supponing a wide variety of individual applications. 

a. Pooling 

Pooling and layering are closely related, though conceptually distinct, 

mechanisms of synthesizing financial assets and scaling up trading. As used 

here, ·'pooling" denotes the familiar technique of combining multiple 

financial assets with cenain shared characteristics, for the purpose of creating 

a new set or financial claims backed by, or detemtined by reference to, the 

resulting asset pool. This is perhaps the most ubiquitous technique in finance. 

Indeed, the very corporate fom1 is a device for pooling of various resources 

used to back the issuance of corporations' securities in the primary arkct. 1 

Mutual funds and other collective investment vehicles are products of e.xplicit 

pooling of other financial instruments - corporate stocks, bonds, and other 

claims issued in primary markets- in a portfolio used to back the issuance of 

fund shares 10 investors. Shares issued by individual funds, in tum, can be 

pooled in a so-called fund-of-funds (Fof) portfolio backing the issuance of 

the FoF shares-'06 

Benchmarking and creation of indices constitute a similarly ubiquitous, 

albeit less directly visible, system-level method of pooling securities issued 

in primary markets for purposes of synthesizing new tradable assets in 

secondary markets.

107 Among other things, major stock indices, like S&P500 

or Wilshire 5000, are used as benchmarks for - and therefore enable the 

emergence of-a wide variety of mutual and exchange-traded funds that track 

their benchmark index values.1os 

"' For a rtminder of lhe key diO'cr.'lltes betiiC~:n fln.~JCial insuumcms i\SIIed, and 

financial tmnsac1ions emcred into, in primary and secondary financial markets. see supra 

Par1 tt.A. 

106 See. e g.. Managed Funds ssocia1i~n. Fund ~r Funds. mviloble at 

hn!1§1/\mw.ll\llnage<ifunds.orgille<Jge-fund·inveslo,./fund-of-fundsl. 

"' For a discussion oflhc sys1cmic fune1ion of benchmark prices and indices. sec Robcn 

C. Hockcn & Saute T. Omaro,-a,S;~temitallySignijicmrt Prices. 2 J. r1N. REG. I (20t6). "'See Vtad a~• Sushko & Gr:un Turner, 11re hnplitations of Passire hn~Jiingfor 

Seerui1ies Markm. BtS QUARTERLY REv. 113 (March 20t8), ami/able at 
88 


91818030.eps

30 FINTECH - drajl 18-Sep-18 

b. Layering 

The lastlwo examples of pooling - FoF and indices - also illustrate the 

role of another transaction meta-technology, which may be called layering. I 

use the tenn "layering" to refer to the technique of synthesizing financial 

assetS in a manner that creates a chain of hierarchically linked claims, so that 

the perfom1ance of each new asset "layer" is determined by reference to the 

combined perfonnance of pooled financial assets underlying it. 

As this description makes clear, the layering technique often involves 

pooling, which makes these categories diOicult to separate neatly. 

Nevertheless, as pooling is repeated in several consecuth•e rounds, the 

distinct systemic implications of the resulting multi-layered stnrcture built on 

the same set .of underlying claims become increasingly pronounced. It is easy 

to see, for instance, how shares in a particular investment fund can get 

bundled with other funds' shares in the first-layer FoF, whose shares in tum 

get bundled with other FoF shares in the second-layer FoF pontolio, whose 

shares then get bundled with yet another set of FoF shares in the third-layer 

FoF, and so on. At each level, an entirely new crop of tradable lund shares is 

created, regardless of whether or not there are any additional issuances of 

corporate securities in the primal)• market. 

Securitization provides an even more vivid example of synthesizing new 

tradable assets via pooling and layering. In a typical securitization, a special 

purpose vehicle (SPV), which holds a portfolio of loans or other revenueproducing assets, issues tradable asset-backed bonds (ABS).109 These ABS 

are then re-bundled with other ABS in the next-layer securitization, such as 

a COO, which issues se1•eraltranches of its 01111 bonds. l11ese bonds are then 

used as collateral backing bonds issued in the next-level securitization, socalled COO-squared, followed by COO-cubed, and so on.110 

Derivatives provide yet another canonic example of how the layering 

mechanism is used both to synthesize new assets and to scale up market 

trading. Derivatives are contingent claim contracts that determine 

counterpanies' rights and obligations by reference to the changes in the value 

of specified ·~mde lying" assets.

11 1 Because the underlying asset is merely a 

reference point for calculating contractual payouts, there is no theoretical 

limit on counterparties' ability to enter into as many derivatives contracts as 

they desire, on any tenns they choosem In this sense, derivatives are the 

ultimate tools for synthesizing a potentially infinite number of tradable 

hnos1Ann• bi~&rnlooWqlrndl7r qll803j.odf. 

'" See r~pra notes 58-59 and actompanying text. 

'"See sources cited supra oote 59. 111 See supra notes 56-57 and actompanying text. m In praclice. of course. there are \·arious limits on thai ability, inchJding n.'l.ula1ory 

ones. 
89 


91818031.eps

8-Sep-18) FINTECH - drajl 31 

financial products on top of any single underlying asset. 

Indexing and benchmarking, mentioned above in connection with 

pooling, also allow for layering of tradable assets in a manner similar to 

derivatives. A major stock or commodity index, for example, enables the 

creation of a wide range or tradable products tracking it. For instance, the 

emergence of specialized commodity price indices in the late 1990s-early 

2000s has been identified as a major Factor behind the surge in financial 

investors' parlicipation in commodities markets and the related growth of 

trading in commodit)'·iinked financial instruments.113 

All of the examples above underscore two key Features of layering as an 

embedded S}'Stem-level functionality. 

First, la)'Cring enables a finite quantity of existing financial claims to 

sen•e as the base on which potentially infinite quantities of new financial 

claims can be produced. Thus, layering significantly blunts, if not eliminates, 

the fundamental structural constraint on the gro111h of secondary financial 

markets: the exogenously limited volume of instruments issued in the primary 

markets. 

Second, layering produces highly complex interdependencies among the 

seemingly discrete assets and markets. Financial assets that constitute a single 

product chain do not have to be linked other than through value-derivation: 

they don't have to be issued by the same or similar entities or reference same 

or similarly sourced cash nows. The many dinerent layers of financial 

products may be inherently connected, yet the precise pattems of correlation 

among their ·values may be dinlcult to discem. 

c. Acceleration 

While pooling and layering operate as the esseniial detem1inants of 

financial markets' structural complexity, the most visible and direct role of 

acceleration and compression is to amplify and sustain the gro11ing rolume 

and relocity ·Of trading. 

Acceleration occurs whenever the speed of transacting is increased (the 

velocity of trading), thus allowing more trades to be executed (the volume of 

trading). Perhaps the most mily recognizable example of acceleration as a 

mechanism of scaling up financial transactions is algorithmic, or highfrequency, trading (HFT). HFT is a trading strategy that uses complex 

algorithms to execute trades at speeds far exceeding human ability. In 

essence, HFT uses quantitative investment programs to take extremely shontenn positions in equities, currencies, and any other electronically tradable 

Ill See. e.g .. lng-Haw Cheng & Wei Xiong. Tlse Firmllcia/izmimr of Commodity 

Mtrrkfts, NBER Working Paper 1%42 (No1•. 2013}, ao'llilable ar 

hno1AI'\\W.nber.O<g!paprolw19642: Soon II. Irwin & D"ight R. Sanders, lmlex Fwufs. 

FinmrciiJ/izotiiM. amiCmmn<>di~ Furur s MIITkets, 33 APr. Eco~. P!:RSP. & POI.'Y I (20t 1). 
90 


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32 FINTECH - drajl 18-Sep-18 

financial instruments, and to move in and out of such positions as a way of 

capturing extremely small gains on erery trade.1

" By definition and design, 

HFT strategies dramatically, and successfully, accelerate and amplify trading 

activity in the relevant markets.

11; 

The acceleration mechanism also works in less obvious ways, often in 

conjunction with the pooling and layering mechanisms. The very act of 

S)11thesizing a new tradable asset may, in and of itself, help to increase the 

aggregate volume and velocity of market transactions. ll1e creation of a new 

asset eliminates potentially significant trnnsactional costs of placing multiple 

trades that would otherwise be required in order to achieve the same 

economic exposure. It makes trnding faster and cheaper relative to trading in 

the underlying assets themselves, which in tum leads to surging levels of 

trading activity. Indexing, derivatives, securitizations, and many other 

financial instruments and market practices exemplify these dynamics. 

Standardizing tradable instruments and trading practices is another 

important tool of accelerating financial transactions. The logic of this 

acceleration tool is simple: eliminating idiosyncratic variations in the key 

economic terms of a particular category of financial products significantly 

reduces the amount of time and resources that need to be spent on each 

individual trade. By establishing a common baseline, it also makes e~sier and 

faster to craft bespoke varieties of the same product, if the need be. 

As mentioned above, perhaps the best-known example of this kind is 

lSD A's success in creating an industry-wide set of standard documentation 

for over-the-counter (OTC) derivatil•es.

116 A much earlier and equally 

powerful example comes from the New Deal ern, when the newly established 

Federal Housing Administration (FHA) used its power as the national 

provider of mortgage defaull insurance to encourage the adoption of a 30-

year fixed-rate mortgage loan as the new industry standardm The FHA 's 

standard-setting actions played a critical role in facilitating the subsequent 

creation of the national secondary market for home loansY1 As these 

examples show, secondary markets need standardization because of its 

transaction-boosting potential: standardization means faster trades, and more 

of them. 

"' See Irene Aldridge, 111wlls Higlr·freqllfiiC)' Trading, Aftu All' UFFI~G ON POST 

(July 8, 2010). m-ailable 111 hnos1A'"""·huningtormosLromlirene1ldridWwhat-i<-high· 

fT\'9U<IICY·!I b §3~2QJ.hJml. 

'" /d. •u See s11pm notes I 02-t 03and a«ompanying Je>t. 

"' Ste P11blic AcrorS. supro note 39, at 133-134. For a fuller aocoun1, see Robert C. 

Hocken, A J<jforwnimJ Rcp11blic by Hamiltonian Moons. 19 S. CAL. L. REV. 45 (2005). 

"' /d. 
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8-Sep-18) FINTECH - drajl 33 

d. Compression 

I use the tem1 "compression" to refer generally to the technique of 

aggregating and compacting risk exposures and obligations associated with 

multiple trades in a manner that de facto transforms them into a single 

economic transaction. 

In this sense, it is broader than "trade compression," a rem1 of an denoting 

a common practice in derivatives trading that, quite simply, involves reducing 

the number Qf deri1•atives contracts while keeping the same net economic 

e.xposureu9 In a typical compressed trade, several derivati1•e contracts 

between the same counterparties are tom up and replaced with a single 

contract with a reduced (often, quite significantly) notional amom11.

110 

Compressing simplifies a complex transactional pattem by extracting and 

operationalizing its aggregate economic effect on the counterparties and 

reducing their gross risk exposures. By the same token, however, 

compression effectively hides theactualvolumeoftransacting that took place 

between these counterparties. While the former is the intended micro-le1•el 

transactional eOect of compression, the latter is its less obvious but 

significant macro-level effect. 

The same basic principle operates in the broader market context through 

the common practice of netting. Generally, netting involves oOsetting of 

mutual payment obligations of transacting parties in order to facilitate the 

back-office process of clearing and settlement of multiple trades between 

themm Netting does not directly generate any new financial liabilities or 

assets: it merely simplifies their ultimate settlement by eliminating 

unnecessary flows of funds and associated frictions in the process. This 

optimizing and risk-reducing function of netting is well known and widely 

acknowledged.122 

By replacing multiple gross transfers due througl1out the day "~th a single 

net transfer at the end of it, however, netting also enables a far greater amount 

of trading to take place. From that perspective, the widespread use of netting 

and trade compression has an important, and routinely under-appreciated, 

systemic effect: it empowers financial market participants to engage in 

secondary-market trading on a far greater scale, and at far greater speeds, than 

" ' See hflpsi/"""'·d<ril'sdocu.COilllblog/201;111/lO!trade-compressioo (explaining 

trade comprt$1ion in Oltr·lbc-coumerderiratires marl;ets). 

'"'Trade oompn.'~s n can also be done on a muhi-la1cral bosis. /d. 

'" Neuing ~ also used 10 offset oth<r obligations. uch as 1\oosc related to posting of 

collateral under deri,·ali,·es or repo ag.n..··e:ments. 

"' There is a 1-ast literature. both aoadcmic and industry-produced. c,,plaining the 

ad1·antages of neuing from the perspc<ti1·e of n:ducing credit. seulcment.liquidity, and other 

risk~. See. e.g .. lSDA, Netting and Ojfse11ing: Repcrti11g Deriw11ires rwder U.S. GAAP anti 

11mler /FRS ~lay 2012). arailtible at hupsiA"'"'·isda.ornla!l·eiDFJoflS.ning·under·us· 

•••J:tMd-ili8-mav-2012.!ldf. 
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34 FINTECH - draft [8-Sep-18 

would be suslainable in lhe less forgiving world of gross senlemenl oflrnding 

obliga1ions. In 1his sense, compression is more 1han simply a risk-reducing 

micro-level applica1ion: il is a S)'Siem-level funclionalily for scaling up 

secondary marke1s in financial ins1rumen1s. 

To sum up, il is lhe syslem-wide oper.llion oflhese four closely relaled 

1rnnsaclional1echniques- pooling, layering, accelern1ion, and compression1ha1 empowers and sus1ains co!llinuous qualllilalil'e gro111h and quali1a1ive 

complexificalion of modem financial markels. These are also lhe enabling 

dynamics of whal is rolllinely labeled "financial imlOI'alion." Much of 1ha1 

innova1ion is anribulable 10 1he i1era1ive application of pooling, layering, 

accelernlion, and compression lools in some new conlexl or 11i1h lhe help of 

some new lechnology. The "innovalive" naiUre of newly crealed financial 

produc1s and markel prac1ices, lherefore, should nol be confused wilh, or 

reduced 10, 1heir narrowly 1echnical or micro-levellransaclional aspoc1S.12' 

C. SySiemic lmplic(//iOns of Secondary Market Proliferation 

Analyzing 1he process of conlinuous growth of 1he financial markel 

lhrough lhe lens of ils core 1ransac1ional modali1ies - pooling, layering, 

acceleralion, and compression - allo11'S us 10 draw several imponanl 

conclusions abou1 1he nature of the financial system. 

First of all, as a resuh of lhese mechanisms' combined operalion, lhe 

financial sys1em's macrcrdynamics increasingly- and increasingly s1arklydiverge from lhe lrnnsactional micro·d)1lamics in lhe financial market.

12' 

Moreover, lhe macrcrlevel systemic factors play an increasingly imponanl 

role in de1ennining what happens in financial markels. In o1her words, 

focusing 011 transaclion-level micrcrfactors - such as, e.g., reducing 

counterparties' 1ransac1ion cosiS, informal ion as)1nmelries, and various other 

"friclions" - is less and less likely to shed any meaningful lighl on 1he 

behavior of1he markets in which these trnnsaclions take place. To understand 

how markels behave, we have 10 look 10 the broader modalilies of !hose 

markels' sel£-regeneralion and gro111h. 

The independent significance and critical role of systemic factors in 

sustaining the operation of modem financial marke1s became painfully 

obvious during the global financial crisis of2008, which explains lhe greater 

focus on macroprudential regulation in the post-crisis eram It has become 

virtually commonplace to describe the financial system as "complex" and 

"'This is, of course. an importanl and complex point that! plan to elabornte in full as 

pan of a separate research proje<:t. 

"'for a th-eorctioal and historical analysis of these general dynamics, see Fixer-Upper. 

supra note 18. 

JlS For more on the post-crisis "mocroprudcnlial tum'' in financial n.--gula1ion, see 

Hoc kelt, supra ncte 71. 
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8-Sep-18) FINTECH - drajl 35 

"interconnected," almost to the point of making these qualities appear 

"natural" and even mystical in their omnipresence. By contrast, identifying 

the core transaction meta-technologies that are used to constmct and sustain 

today's complex and interconnected financial system helps to demystify it. 

Understanding how new financial assets and markets are continuously 

synthesized ''ia pooling and layering, and then scaled up via acceleration and 

compression, helps us to visualize the logic of stmctural complexity, internal 

interconnectedness, and fragility of the system. The financial marketplace 

appears not as a nat space in which multiple parallel sub-markets operate as 

largely independent and potentially competing "financial intennediation" 

platfonns, but rather as a fractal universe driven by the unifying logic of self: 

replication. In this picture of the financial system, the most significant 

relational dynamics are not horizontal, as it is implicitly postulated in the 

"disintennediation" or "shadow banking" narratives, but vertical, as in the 

dynamic pauems of connecting the many layers of financial risk trading. tl6 

And as this system grows bigger and mOI'es faster, it also becomes 

increasingly unstable. 

Another systemic implication of pooling, layering, acceleration, and 

compression is that they naturally operate to decrease the levels of 

transparency and governability of the financial market. It is difilcult to "see 

through" the multiple layers of financial claims in a pyramid-like structure 

like a multi-layered FoF. It is even more difficult to assess the risks or to 

predict the behavior of a highly stmctured bespoke derivative referencing the 

value of other stmctured products. Similarly, the stmctural complexity and 

the speed of contagion in the financial market often render important market 

govemance mechanisms, designed to resolve various market frictions, 

potentially ineffective. The failure of Lehman Brothers in October of 2008 

provides an apt illustration of these trends. Follo11~ng the firm's bankmptcy 

filing, neither Lehman's own management nor its major trading 

counterparties were able to establish with certainty the value of its derivatives 

positions and resolve the problem through the "normal" governance 

mechanisms, thus necessitating govenunent intervention. ill 

This example also highlights the third systemic implication of the current 

patterns of the gro111h of financial markets: an increasing importance and 

intensity of self-amplifying, or recursive, market-wide collective action 

problems- and the resulting need for a more direct and effective exercise of 

'" For' full elaboration of this multi-layered arohitccture of the financial system. se< 

Finance Fronci1ise. sr1pm note 8. 

"' Se~ Michael J. Fleming & Asani $arkar. The Failure Resolurio11 of Lehman Brothers, 

FRBNY [CON. POt'Y REV. 175 (20t4), omilable at 

hnos1A""''.nt\workftd.orWmediolibt.!Nlm«JialrO!<arcltfepr!20t.U14t2nem.pd( 
94 


91818036.eps

36 FINTECH - draft [8-Sep-18 

market-wide collecti1•e agency.1

lS 

A structurally complex system based on the continuous synthesizing of 

tradable claims and scaling up trndingactil•ity is inherently prone to behaving 

procyclically. Investors in the fast-moving, conragion-prone, non-trnnsparent 

financial markets are forced to act swiftly and in unison, whether that means 

not missing out on a "hot" investment or not being left holding the bag when 

it turns "toxic."

129 While individually rntional, this behavior leads to 

collectively ihannful resulls, as upward or downward price spirnls become 

entirely divorced from so-called fundamentalvaluesn<> In a market where 

fundamental value is often hidden at the bottom of a long chain of 

increasingly 1•irtualized representations of that value, these price spirnls are 

bound to be more violent and destructi1•e, which significantly raises the 

importance of being able to arrest them as quickly as possible. 

Of course, today's financial market looks nothing like the earl)• stock 

market model that inspired classic laissez ji1ire theories. The market that 

keeps growing bigger, faster, more complex- and therefore, more vulnerable 

to sudden and contagious shoc-ks - cannot rely on the "invisible hand" to steer 

it away from trouble. That market needs an eftective counterweight to 

collectively disastrous asset price booms and busts: it needs a collective agent 

capable of acting not in pursuit of purely profit-making goals but in the 

collective interest of all market participants.13' While in theory this type of 

collective agency may be exercised by certain large private parties, the sheer 

scale of the modem financial market renders 1he private option impossible in 

practice. Only public actors, 111th their large size and unique risk lolerance, 

can realistically lake on 1his crilically importanl market-preserving role.131 

As discussed above, under the 1em1s of the New Deal settlement in 

finance, this market-preserving function has been explicitly assigned 10 the 

govemmem, the quimessential collective agem in a modem polity. The 

government's role, however, was deliberately limited in order to leave control 

over capital allocation in private hands.1ll Ironically, the very success of 

privale actors in expanding !heir freedom to generate financial risks - via 

cominuous synlhesizing of tradable financial products and scaling up 

secondary market tmding - is opening the crucial space for a much more 

lzt For introduction and dcfini1ion of the concept of ~"Cutsi\ C collective action 

problem;" or"RtCAP," and analysisofhow this phenomenon anif~ts ilselfin a 1'lllicty of 

coniCxt~ see Roben Hockcn, ec11rsi•~ Collecti•~ Acliou Problems: The Slruc1ure of 

Procyclicalily in fiiiOIJCial. Moue/Of)'. mul Macromarh!ls, 3 J. I'IN.l'ERSP. I (20tS). 

'"See id.a\20-21. 

'"/d. 

"' See id. a\25. 

Ill For a full discussion of public ~rumenialilies' martet-pn.'SCrving tlm<tion~ see Public Ac1ors. :supra note 39, at 134-137. 

"'See s11pra Pan 1.8. 
95 


91818037.eps

8-Sep-18) FINTECH - draft 37 

direct and proactive public involvement in managing the nows of capital in 

financial markets.1;. 

In this sense, the broad systemic implications of modem financial 

markets' modus operandi increasingly push against the basic premises of the 

New Deal seulement. In the New Deal paradigm, the government's principal 

role in financial markets is that of a regulator, an exogenous force with a 

clearly limited mandate to innuence private actors' allocative decisionsm 

Private market participants, by virtue of their presumed micro-infom1ational 

advantages and indi1•idualized economic incentives (also presumed to be 

fundamentally aligned or align-able with the collective good), retain the 

ultimate control over allocating capital to specific economic uses.136 Yet, as 

the above discussion shows, these presumptions do not necessarily hold in 

the context of increasingly complex, multi-layered, self-referentially growing 

modem financial markets. The systematic prioritizing of micro-transactional 

factors over macro-systemic ones, built into the New Deal seulement, is 

quickly becoming an impediment to its continuing enicacy as the over-arching 

market governance framework.1 '

7 

The rise of fintech in recent years is likely to elevate these existing 

tensions to a qualitatively new level. Viewed in this context, fintech is 

emerging as a potentially powerful tool for reselling the cunrent publicprivate balance in finance.1

3$ While it is still too early to catalogue all of the 

specific ways in which the Cl'oil•ing technologies will or might be used to this 

efiect, it is nevertheless both possible and necessary to begin a sustained 

inquiry into the macro-systemic aspects of key fintech trends. 

Ill. DECODING FINTECit TECHNOLOGICAL REVOLUTION, MARKET 

EVOLUTION, OR POWER DEVOLUTION? 

The arri11al of fintech is often equated with a "re,·olution" in finance.

139 

Recent advances in digital communications, cryptography, data management, 

"'See Public Actors. supra notel9, ot 140-144. 147-160. 

"'See supra notes 38-41 and accompanying text. 06 See supra note 42 and accompanying te~xt. 

"'The r«Xtlt gro111h of int<resl amcng lhe scholars of financial maJkeiS and regulation 

in understanding and adapling 1-arious insights from complexity studies and S)>tcms analysis 

reflects a growing rt'<ognition of the critical role of macr<>-systemic factors in finance. See, 

e.g. Roben F. \\Ieber, Stmcmral Regulmion as Amid01ero Complmity Capuu-e, 49 A)t, BlfS, 

~- J. 643 (2012). While this is a promising avenue of analysi~ it is imponant to keep in mind 

thai, in contrast to many nalural complex S)~tems (such as. e.g .• the human body or a 

panicular ecosystem~ the financial S)>lem is socially and legally ccnstMted. II is 

fundamemally a prodll(t of law, which is itself a product of explicit policy choices. 

Analyzing the financial market's intra-S)>ternic qualities and functions, therefore, cannot be 

separated from the anal)>is of its norma1i1'e qualities and social functions. 

"'See sr1pra Pan 1.0. 

'"See s11pra Pan lA 
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91818038.eps

38 FINTECH - drajl 18-Sep-18 

and machine learning promise to revolutionize financial transactions by 

making them infinitely faster, easier, cheaper, more secure, more widely 

accessible, and individually tailored to every user's needs. These claims and 

expectations also shape much of the public discussion on how fintech is 

"disrupting" financial markets and how it should therefore be regulated. 

This Anicle argues that, in order to decode the meaning of "fintech 

revolution," we must analyze fintech not as a collection of discrete financerelated micro-transactional technologies but as a macro-financial, systemic 

phenomenon. This requires, in tum, understanding whether, and how, 

specific fintech applications are going to affect - or already are affectingthe public's capacity to maintain the stability of the macro-environment. On 

the one hand, fintech may present a unique opportunity to correct the 

structurally destabilizing imbalance between private generation and public 

accommodation of financial risk, built into the existing paradigm of financial 

regulation. On the other hand, it may further intensify that imbalance, thus 

raising serious questions about the continuing viability of the New Deal 

settlement in finance. 

This Part examines some of the more established fintech applicationsincluding cryptocurrencics, distributed ledger technology, marketplace 

lending, ICOs, and robo-advising - from this perspective.1.ro While not 

making any definiti1•e claims, it highlights the degree to which these forms 

of fintech are poised to facilitate and amplify the pre-existing systemic 

dynamics of finance, thus further exacerbating the fundamental tensions built 

into the Ne11• Deal settlement. 

A. ''E/imillaling Frictions:" Cr)plocurrencies and Distribwed Ledgers 

To date, arguably the most promising and potentially impactful fintech 

applications have focused on resoll'ing specific frictions in payments, 

clearing, and settlement of financial claims and transactions - the key 

functions perfonned by financial market infrastructures (FM 1).111 Paymenrs 

is an area of panicular interest in this respect. This is partly the case because 

of the sheer ubiquity and systemic importance of the payments system."1 

Partly, it is a result of recognizing persistent problems plaguing cross-border 

payments that typically invo!l·e several banks (which increases the costs of 

making pa}~nen s) and take several days to clear the hurdles associated with 

"' For a brief description of these technologies, sec id. 1

'

1 Sec BoARD OF Goi'El!NORS OF THE F'F.DERAL ~SERI'E Y~~ Federal Resen·e 

Policy 011 Po)wrelll S)>lem Risk 2 (2017). oroil11ble or 

hnos1Amw.fe<lcrai'W!'·c.go"ioolm<ntsV>Iemslfil"'osr oolicv.odf. 

'" Genera11y, a payments systom is defined as "a set of rumcnl . procedures and 

rules for lhe uansfcr of funds between or among pat1icipams. Payment systctns include, but 

are not limited 10, large-,-alue funds transfer systems, automated ctcaringhouse sys.tcms. 

dte<k clcaringhouS<'S. and credit and debit card senlcmon1 )~tems " !d. at 6. 
97 


91818039.eps

8-Sep-18) FINTECH - draft 39 

currency C(}nversions and various other regulatory and administr.uive 

issues. "3 Fintech-driven solutions 10 the problem of slow and expensive 

paymems range from the invemion of ahematil•e cryptocurrencies that aim 

to circumvent official sovereign currency-based payments channels to 

redesigJJing the payments platfom1S on the basis of some new digital 

technology. 

I. Bitcoin: S)~llhesizing Assets 

Bitcoin is the leading example of the first strategy. It is the most 

established and prominent cryptocurrency currently in use.

144 In simple 

tenns, Bitcoi n is a form of electronic money, a decentralized 1•irtual currency 

that operates through a network of peer-to-peer computers, or nooes.14l It is 

an online communication protocol that enables the use ofbitcoins- electronic 

tokens or bits of data - as a me~ns of payment and exchange similar to regular 

currencies.140 However, no sovereign backs Bitcoin, and no state or any 

single private instinuion controls its creation and usei47 

At the heart of Bitcoin is an innovative blockchain technology, which 

allows verification and recording of each transaction within the system in a 

publicly distributed ledger. Encrypted transactions are solved by the nooes 

and grouped in blocks (e1•ery few minutes), which are recorded one after 

another in a chain. Each nooe in the system keeps a copy of the 111101e 

distributed ledger, which ensures that the entire record of transactions c~nnot 

be ahered.

148 Because of these features, Bitcoin users do not need to place 

tmst in any single institution, like a bank or a securities broker, to keep the 

system secure.

149 Bitcoins are stored in digital wallets, or data files that also 

contain recorded transactions and private keys necessary to spend or transfer 

bitcoins.1l{) The true identities of the transacting parties are hidden behind 

l'J id. 

'" Stegentrlllly NATIL\NIEl POPPER, DIGITAl GolD(2015); PAUl-VIGNA&MICIL\El 

J. CASEY. THE AGE OF CRYPTOCURREI'Cl' (2015). 

'" Ste PEDRO f'R,\~CO, UNDERSTANDING BITCOIN: CRYPTOGRAPHY, E\:GINEERING, 

AND ECOXO.IIICS 4 (2015). Bilcoin was created in 2009 by Sa1oshi Nakamoto, which is 

belie~ ed to be a pscudon)'lll for an unkno11~ per.;on or emity. O..pile I he "'l~lerious nJiure 

of Bilcoin 's crealor(s), its current proponents mainlain thai open-sourees soil ware cannot be 

conlrolled by i1S original creator and truly be<omcs a colle<live producl thai can only be 

altert'd by tensensu> arising in the community of pocrs. for a discllSlion of the imcllttmal 

origins and prc-histOI)' ofBi1ooin, see id. at 161-169. 

"'JOSE PAGLIERY, BITCO NA.~DTHEfUTIIREOF O)(EY 6(2014). Bitooin llilhact~piUll 

•·a·• typically refer.; to the em ire S)~te n supporting the virtual currency, 111tile ''bitooin" 11ith 

a lower-case ''b" denotes the J<tual unil of thai curren<:y.ld. 

"'fRANCO, supra note 145. at 3 (2015). 

"'/d. at IS. 

"' ld.atS-9. ,,. Rainer Bohnre et al., Bircori.: Economics. Tee/urology. and Gor~r111mce, 29 J. EcoN. 
98 


91818040.eps

40 FINTECH - drajl 18-Sep-18 

unique Bitcoin addressesm Bitcoin can be used to make payments and 

transfer value among digital wallet holders within that virtual system, which 

makes it a superior method of cross-border payments. 

Bitcoins are "mined" by solving the encrypted transactions that get added 

to the blockchain. The software, in efTect, creates bitcoins and awards them 

to "miners" willing to expend their time and effort to verify encrypted 

transfers from one digital wallet to another. Mining bitcoin requires 

significant computing power, and the difficulty of solving transaction 

"puzzles" is programmed to increase, in order to keep the supply of bitcoin 

from rising too rapidly.151 1mportantly, bitcoins c~n also be bought and sold 

lor U.S. dollars or any other sovereign currency. Severn! Bitcoin exchanges 

allow conversion ofbitcoins into non-virtual currencies.15' 

To true Bitcoin enthusiasts, it represents a great vehicle of social good, 

which can broaden access to faster and cheaper money trnnsfer and payment 

sel'\•ices for the poor and the unbanked around the globe114 l ibertarians 

embrnce Bitcoin as an altemati1·e to state-created conventional currencies 

and, more broadly, government monopoly on money and credit.155 And many 

tech-savvy Millennials prefer Bitcoin simply because it combines cost-saving 

efliciencies with greater privacy and security. 

156 

For the majority of financial market participants, howem, Bitcoin's main 

virtue is its value as an investment - or, more precisely, speculative 

investment- asset.157 As an asset, bitcoin is extremely volatile. On July 19, 

2010, the recorded value of one bitcoin was just $0.06. By December 16. 

2017, the value of a single bitcoin re~ched S19,343.04.tss According to a 

respected industry publication, this puts the rnte of bitcoin's appl'l.>ciation 

relative to the U.S. dollar in those seven years at 32,000,000%.

159 It is, 

therefore, not surprising that, while Bitcoin has not been able to displace 

sovereign currencies, it has successfully emerged as a brnnd new financial 

asset class.100 

PERSP. 213,120-22t ((lOtS). 

m fRA~OO,supro no1e 14), at9. 

U! PAGUERY,supranOie 1%. at 33·34. 

'" Bohme etal.,supronole tSO.at 210. 

"' P,\GLIERY.sr•pranote 146, at91-106. 

'" /d., at 29-136. 

,. /d. atll5·120. 

'"See Rosa ,\1. Lastra & Jason Alkn, Virtual Currenci.s in the £urosystem: Clu•lleuges 

Ahead. ECO'N Moneta')' Oiatogut Study 24 (July 20t8). ami/able at 

hnn·//w"w.eurooar!.euroro.eulcmsda!all50541ffitW FINAL%20uuhli<Mion.vdf. 

"'See Oanielllinge. The hum for a crypto t/lXOII0/11)'. CE.\'Ilt.II.BANKING.COM (May 4. 

2018) (citing CoinOesk da1a). 

"' /d. 

I(!O The surprising pan, howe\'cr, is the shccrmagbitudc of the Bitcoin speculath·e- hype-, 

esp«ially in 2016-2017. Ahhough in the fil'SI halfof2018 bitooin'smarl:ct l'a!uecamedolln 
99 


91818041.eps

8-Sep-18) FINTECH - drajl 41 

Importantly, bitcoin's high volatility makes it an attractive underlying 

commodity for derivatives trading. ln September 2014, TeraExchange 

established the first regulator-approved U.S. bitcoin derivatives trading 

platfonn.t

6t In December 2017, the Chicago Mercantile Exchange (CME) 

and the Chicago Board Options Exchange (CBOE), the two largest and oldest 

U.S. comm<Jdity futures exchanges, raced to launch Bitcoin futures 

contracts.t

61 As the CME's website proclaims, "Now you can hedge Bitcoin 

exposure or harness its perfonnance with a futures product de,·eloped by the 

leading and largest derivatives marketplace: CME Group, where the world 

comes to manage risk."t6l In May 2018, Goldman Sachs announced a 

decision to establish its own bitcoin derivatives trading desk, in response to 

its institutional clients' growing interest in holding bitcoin "as an alternative 

asset," and to create ··its 0\111, more flexible version of a future, kn0\111 as a 

non-deliverable forward."t

61 

Turning :bitcoin into the raw material for derivatives trading has several 

important consequences. It legitimizes bitcoin as a boua fide tradable 

financial asset, rather than merely a virtual token without any tangible value 

backing it, and incorporates it into the established financial market 

infrastructure. This instantly transfonns the dynamics of bitcoin trading by 

scaling up its volume and helping to support its price. In short, it makes 

bitcoin -a d.igital token, or a bit of encrypted data -part of the same menu 

of financial assets as U.S. Treasury Bonds and shares in General Electric.t6; 

Bitcoin's amazing journey from an obscure techno-utopian experiment to 

Goldman Sachs' market-making books is fascinating in a deeper sense. It 

provides a vivid example of how fintech technology can be, and is, used to 

significanli)' frcm iiS p<ak of nearly $20,000 in tale 1017. in May 1018 it was stilllwvering 

around $10,000. In his charactcristi<ally unsparing manner. Wa11<11 Buftcu refem:d 10 

bitooin as ""rat poison squared" a less esthetically pleasing image than that of a tulip bulb. 

See. Tao Kim. Wam11 Duffell"~' bitcoi11 is .. probl•hly rm poison sqr~orM •· CNBC.coM 

(May 5, 2018), arailahlem hnf!!I~A,ww cnbc.comi2018/Q5/05Amren-buftdt·S3VS tco

is·probablv·rat·!!9ilon·S9U3red.hunl. 

'" See, Michael J. Casey, Tera£xchauge Um~ils First U.S.-Regulated 8itcoi11 Swaps 

£xcha11ge. WAtt ST. J. (Sept. 11. 101~). 

"' See Dan Defrancesco. FCMs Dema11d Self-Cerrificatioll Olwhaul After Bitcoi11 

Debacle, RtSK.NET (Jan. 30, 1018). Both OlE and CBOE listed their respe<ti1·c Bitroin 

contracts through sdf-cenification, which allowed them to a1·oid submiuing the proposed 

contracts for ~ulatOI)' app1o1~1. They were later criticiwl for the ruslml and oon· 

transparent nature of their actions. gil· en the riskiness of tiles< complttely new produciS. ld. 

'" CME GROUP. http11mYW.cmegroyo.comll!!ding/bitcoin·fuJUW.html. 161 Nathaniel Popper. Goldmw•Sachs to Ope1111 8itcoi11 Trodi11g OpeTIItion. N.Y. TI.\IES 

(May 1. 2018). mYJi111ble at https1/mohile.nl'!imescoml20181051021lcchnology}bitcoin· 

goldman-sachs.html; \Volfie Zhao, Go/dm1111 Sachs to Begin Bitcoin Futures Trading. 

Co NOF-SK.CO~t (May 3. 10 18), m-.ilable at huos1A'""".coindesl:.comlgoldm•n·sachs·tobegin·bitcoin-finures-rroding·\\ithin-wceksl. 

'" For a prodictire an>lysis of tl>is trend. sec FlitoJJCe FrmJChise. supro note 8. 
100 


91818042.eps

42 FINTECH - drajl 18-Sep-18 

synthesize tradable financial assets effectively out of thin air. In contrast to 

even the most esoteric traditional (that is, pre-fintcch) financial products, the 

l'olume of tradable bitcoin is not tied to, and thus constrained by, any 

financial claims issued in the primary markets. The 1•olume or value of 

bitcoin bears no relation to the production of any actual goods or services in 

the non-financial economy. The supply ofbitcoins grows as a result of trading 

and transacting in bitcoin: it is, in this sense, an entirely sell: referential and 

self-reproducing secondary-market phenomenon. 

The gro\\1h of bitcoin derivatives and potentially other bitcoin-linked 

products- such as, e.g., exchange-traded limds (ETFs) passively tracking 

bitcoin's value - is a classic example of pooling and layering, two of the core 

transactional techniques used to synthesize new tradable claims referencing 

a single underlying asset. The fact that, in this case, the underlying asset is a 

digital token, as opposed to shares in operating companies or barrels of oil, 

potentially removes any "natural" limits on the extent of such pooling and 

layering - and, accordingly, on the ability of market participants to scale up 

trading in these continuously S)1lthesized crypto-assets. 

2. Distributed Ledger Technology: Scaling Up Trading 

In recent years, numerous financial institutions and fintech finns have 

been actively exploring a broader range of potential applications of the 

bloekchain- or, more broadly, ''distributed ledger" -technology underlying 

Bitcoin.166 Generally, distributed ledger technology (DLT) may be defined 

as "a set of technological solutions that enables a single, sequenced, 

standardized and cryptographically-secured record of activity to be safely 

distributed to, and acted upon by, a network of varied participants." 7 It is 

important to note that DLT is not new or unique in its ability to allow multiple 

network participants to share and view data in near real time: it is simply 

another model within the familiar category of a "distributed database 

management system."

161 Yet, the bloekchain mystique factor- the marketing 

power of the new fintech lexicon - has catapulted DL T into the l'ery center 

of the financial sector's digital "innovation" e0orts.t

69 

Because DLT is said to be ·'asset-agnostic," in a sense of being able to 

prol'idc "the storage, recordkeeping, and transfer of any asset," it can 

potentially be applied to optimizing a variety of processes, including not only 

"' Tcchnitatly. blockchain is merely a panicular kind of OlT. For purpose$ of the 

ph.'"Se!lt discussion. h<m·ewr, these diffc:rell(es art not especially rdc,·ant. and 1hesc temls 

11itl therefore be US<d interchangeably. 101 FtN \~Ct \1. COXDcCT AuTitORtTY (UK). OISCIJSSION PAPER ON DtSTR13liTED 

lEDGER TECIINOI.OGY. DP1 113. 10 (April 2017). ami/able a1 

hnpsil"""''. fcam .uk/publicationldiS<!JSSiooldo 17 ~).pdf. 

"'ld. lll fd. 
101 


91818043.eps

8-Sep-18) FINTECH - drajl 43 

payments but also post-trade clearing and seulement of any asset.110 So· 

called '·smart contracts" that reside on distributed ledgers and distill 

contractualtenns into a self:executing computer code can also be used for 

ongoing management of collateral and other counterpany obligations.

111 

Given the magnitude, complexity of institutional arrangements, and 

systemic significance of the payments, clearing, and seulement functions in 

wholesale financial markets, practical implementation of these concepts is no 

easy task. Not surprisingly, there are currently several parallel efforts to 

revolutionize these systems through adoption ofDL Tor "smart contracts."m 

These include, for example, the LBM-backed HyperLedger Fabric project that 

s.--eks to optimize cross-border trade financing and an open-source Corda 

platform for managing bank-to-bank financial agreements being developed 

by a large bank consortium, R3.17l 

In 2017, another consortium of major global banks, led by Switzerland's 

UBS, announced the next phase in the development of so-called ·'utility 

settlement coin," or uscY' The usc is a digital currency stored on a 

pennissioned blockchain and used by member-banks to make pa)qnents to 

one another to clear and settle securitie.s trades. This new cryptocurrency 

arrangement will allow for much faster and convenient clearing and 

settlement or bond and equity trade.s between the participating banks.11s In 

each trade, both the sold-and-bought securities and the payment for them will 

be ''delivered" through the consortium's blockchain system. Instead of using 

the relevant jurisdiction's oOicial payments system and waiting for 

traditional ntoney transfers to be completed, these banks will simply transfer 

the relevant amounts in USC to one another's USC accounts. The payee-

'"' D:wid Mills r1 a/ .. Distributfli Lfliger Tecluwlogy in PaJnrent~ Cleuring. m•l 

Seu/enrent, Fed. Res. Bd., Fin. & E<on. Disc. l'apcrNo. 2016 095(2016~al 11,11milableat 

huos1/ll ""'. f«leralresen·e.govl«:onr<'Sdatalfed<'20 I 6/fi les'20 I 609Soop.pdf. 111 See Luke Clancy & Sle,·e Marlin, &mks test premise of blockdwin "' CCP 

replacement. RJSK.N!rr (Apr. IS. 2016). 

m For a r«!mindcr of what ''Smart ronlracls'' arc, 54.'(' Sr£pra note 26 and a«ompanying 

lex!. 

"' See Hugh llaoono, &mk·basfli blockdUiin projects are going to trun•fomo the 

jiMncio/ sen·ices iml11stry. T«hCrunch.oom (Jan. 28. 2018). /1\"0ilab/e at 

hllps1/J«:hcrunch.c<>mi2018/0ifl8'bank·b3sed·blockchain-proi«ts·are-going-totrnnsfonn·thc·financial-smices·indusll\'/; Tanaya Matheel, R3 Makes C«/efor Finm~eilll 

Agreemenrs Platfl){m Op<m Sourre. A)l. BANKER {NOI'. 30, 2016). arailoble at 

hnos:/An, americtnbanlq.comlnewslr3-makes-code-for.financial-agn.'"tm~nls·Rlatfo111tooen·SOUrte. 

"' Michael Dd Castillo, Barela)>. HSBC Join Seu/emenr Coin as Banil Blockd111in Test 

Enters Neo•· Phase. COIN DESK (Aug. 30, 201 7). ami/able at 

hnos1illm>'.coindesl:.comlhsb£.bartlavs-join-ulility-S<ttlement-coln-!S·bank-blocl:chaintcsl-enters-final-phasc/. 

'" ld. 
102 


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44 FINTECH - drajl 18-Sep-18 

banks will then exchange their USC holdings for the relevant sovereign 

currency, on a one-to-one basis.116 This key feature of the proposed closeduniverse, blockchain-based imcrbank paymelll platfonn - USC's direct 

convenibility into major sovereigu currencies - requires an explicit 

commitment on the pan of the relevant central banks to suppon the 

arrangement.111 The consonium is reponedly working with se1•ernl central 

banks - presumably, the Federal Reserve, European Cemral Bank, Bank of 

England, and Bank of Japan- to set up a system for guaranteed exchanges of 

USC for all major sovereign currencies.l18 

There is l'ery little infonnation available on the USC project or other 

similar projects currently under way, which makes it diflicult to understand 

how exactl}• these new DLT-based payments, clearing, and settlement 

arrangements 11111 work in prnctice.

119 1t is even more diflicult to identify and 

assess their potential impact - both positi1•e and negatil•c- on the financial 

system's operation, resilience, and stability. 

Genernlly, the most frequently cited potential benefits of using DLT for 

payments, clearing and settlement include its ability to reduce complexity in 

cross-border transactions, improve "end-to-end processing speed and thus 

availability of assets and funds," increase "trnnsparency and immutability in 

trnnsaction record keeping," improve "network resilience through distributed 

data management", and reduce "operational and financial risks."110 ln 

essence, DL T is expected to make trades seltle pretty much instantaneously, 

thus significantly reducing transactional costs and counterpany risk. Some of 

the most widely cited potential risks of moving payments, clearing, and 

seulement functions onto DLT platfonns include increased cyber-security 

and operational vulnerabilities, legal uncenainty with respect to ownership 

of digital tokens or enforceability of sman contracts, and (very imponantly) 

finality of settlement in a distributed system not backed by a central bank.181 

For most of these enumerated problems, however, there appear to be 

reasonably manageable solutions, some of which im•olve things like ''more 

nimble" regulatory responses.1

Sl 

"' See lzabella Kamin>ka. Wha1 is 'Uii/io· Seulr~nem Coirt'. Real~·? fT Al.l'IIAVILLE 

(Sept 18.201 n arailable at hnps1/ftalpba, ille.ft.colnl2017/09/181219354lMal·is·Uiililysenlemeni·COin·reallv/. 117 /d. 

"' /d. 

"'The design and op<nnion of USC is CSiJ<'<ially in1riguing in Ibis r,'Specl. See id. 110 See Mills el a/ .. supra note 170 at 17; CO~IMimE ON PAY,\IF.l-'TS AKD MARKET 

INFRASTRl:CiiJRES (CPMI). Dislribuled Lfdger Tedmology i!l Po)'melll. Cle<~rilrg am/ 

Seu/eme~tl (Fcb.l017), all. m·oi/able 01 hnos1/""w.bi~orglcpmilpublldl57 pdf. 

"'See Millse~ol .. supra no1c 170al28-29: 31-34. 

"' "Regulalory sandbo<os," whioh cfl'ec1iwty mmp1 quali~ ing finlcch finns from 

o111<1\1 ise applicable regula1ions. are often pii.'SOnled as Ibis kind of a nimble ""ponse. See 
103 


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8-Sep-18) FINTECH - draft 45 

From a systemic point of l'iew, however, the prospect of 11~de~pread 

adoption of DL T-based systems for payments, clearing, and settlement of 

financial lrlmsactions may not be quite so favorably balanced. The main 

concern here is straightforward. If DLT succeeds in making wholesale 

payments, clearing, and settlement instantaneous, easy, and cheap, it will 

enable potetltially exponential gro111h in the volume and velocity of trading 

in securities and other financial assets. To put it simply, in a fully frictionless 

world of blockchain-powered transaction processing, overtly speculative 

trading will also be faster, easier, cheaper, and thus more voluminous. 

Such system-wide scaling up of trading activity goes far beyond a mere 

improvement in end-to-end processing speed. Quantitati1•e changes of this 

magnitude are bound to effect a qualitative change in the nature and behavior 

offinancial markets more generally. This will, in tum, magnify the systemic 

role of - and amplify the pressure on - central banks and other public 

instrumenlalities charged with ensuring financial stability. Hyper-fast, hyperexpansive fi~ancial markets will require a hyper-fast and hyper-;:apacious 

public actor of" last resort."

183 Envisioning the specific fomt- or fonns- this 

collective agency should take is an exercise in bold institutional imagination, 

bound to raise a host of politically salient questions.'" Unless we are ready 

to face these questions, we are not ready for the arrival of li-ictionless trading 

in financial assets. 

One more point is worth making in connection with DL T and its potential 

to revolutionize payments, clearing, and settlement infrastructure. Recall that 

the originalllitcoin p3)1nents system is designed to operate on the real-time 

gross settlement (RTGS) basis: each bitcoin transfer between wallets is 

assigned a umique identifier and, once added to the immutable public ledger, 

serves as an objectil•e proof oft he coins' mmership. In this "trustless" world, 

there is no built-in transactional credit function: no specialized intermediaries 

lending their own balance sheets to transacting parties and, therefore, no 

native neuing capability. 

This pure RTGS principle at the heart of the Bitcoin system- or, in terms 

of the market dynamics discussed above, acceleralion without compression 

- is the main reason why blockchain in its original form cannot support largescale trading in financial markets. Without the ability to net, counterparties' 

liquidity needs impose hard constraints on the volume of trading they can 

gmera//y Atl<n, supra Jl(lle 6. 111 For e'amples of"hat such a gh~paci1y public instrun,.nlalily might look like, 

see Public Actors. supra Jl(lle 39. at t40-174; Nmimllllim'fStmem Authorio: supT(I note 3S; 

oldt~l Shore. >UpTI1110le SS. 

"'Among o1her lhings, il "ill dir<elly implioale lhe recently reigniled conlroversy 01-er 

central bank powers and indcpcndenct. For r\."Cenl rontributions 10 this deOO!e. see P1\UL 

TI!CKF,R, IJXELECIID Po"<R (201&); PE!ER CO~'I'I·OROWN, THe POWER AND 

I~'DEPENIJEI(CE OF TIJE fEDERAl RESERVE (20 16). 
104 


91818046.eps

46 FINTECH - draft [8-Sep-18 

sus1ain. Furtihem10re, not only does the system have to process great many 

more individual transactions, it also does not allow for trading on credit Nor 

does it allow for using coins as collateral: a verified transfer eiTetts a simple 

change in ownership recorded in the distributed ledger. Operationally, 

leverage becomes far more difficult to use in a system that explicitly 

precludes "double-spending," or spending what you don't fully and 

exclusively 0\\11. 

It is this fundamenlal problem that the financial industry actors - the 

supposedly "disintennediated" banks and the "disruptive" nonbank 

challengers alike- are seeking to solve. Characteristically, both the problem 

and the solutions are couched in purely technological terms as a maller 

primarily of processing speed or computing power. Among the reported 

solutions is the Lightning Network, '

85 which allows people to sign smart 

contracts creating "time-locked, two-way payment channels" based on a preagreed notional amount and seeded with a single bitcoin payment186 llte 

parties can then transfer money to one another within that pre-set balance, as 

well as to and from third parties' accounts, terming "a network of traced 

payments that need not be confinned in the Bitcoin blockchain."'" By 

allowing limitless "on:chain" transactions managed via smart contracts, 

Lightning pr·omises to overcome Bitcoin's processing capacity limits and to 

allow it to compete with Visa's network. I$$ 

These efforts, however, aim to deliver far more than simply a technical 

fix for a technical problem: Lightning and similar programs are potentially 

creating a crucial system-wide capacity for lerering and netting of financial 

transactions "on· chain." Now, what gets recorded in the publicly distributed 

ledger can be simply a net result of multiple trades run by dealers: a single 

ultimate number that pr01•ides pretious liule insight into market activity 

underlying it In effect, this oiT-chain transacting replicates the familiar 

pauems of margin trading and collateralized borrowing that enable financial 

asset speculation. The new technology does not alter the etonomic substance, 

and public policy implications, of these transactional techniques: it is still all 

about private parties borrowing to make short-temt profits in secondarymarket trading. But technology makes these old dynamics much less visible 

behind the shining veil of scientific progress. What used to be done "off 

balance sheet" can now be done "offblockchain," and with the same result: 

potentially excessive financial risk and leverage hidden behind an ostensibly 

transparent ledger. Yet, focusing on the form in which that publicly viewable 

'"See hnps1nig)llning.nelworid. 116 M!CIIAEL J. C.1SEY & PAUL VIGNA, TilE TRUTll MACIIINE: l)JE 8LOCI(C1JAIN AND 

TilE FIJTURJ;OF EVER1'1111NG 75 (20t8). 111 /d. 

"' /d. 
105 


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8-Sep-18) FINTECH - drajl 47 

but infonnationally incomplete ledger ex isiS - whether it is a physical book 

or a complex piece of software- distracts attention from this basic fact. 

B. ''Democratizing Finance:,. Digital Crwdfimding and Robo-Advising 

In addition to its ability to optimize transaction processing and eliminate 

frictions in the operation of financial market infrastn•ctures, fintech is often 

praised for its unprecedented potential to make financial markets more 

inclusive and equally accessible. IS9 Bitcoin, for example, is often totued as a 

tool of financial inclusion, because it makes payments and asset transfers 

more afford~ble. Two other fintech trends explicitly credited with this 

"democratizing" ell"ect on financial markets are digital crowdfunding 

(including marketplace lending and I COs) and robo-ad1•ising. 

I. Marketplace Lending and I COs: Synthesizing Assets 

Crowdfunding is a loose category co1•ering historically varied forms of 

finance. '

90 Today, crowdfundinggcnerally refers to raising funds from a large 

number of individual im•estors, typically by using online social networks or 

specialized funding platforms191 These platfomts allow start-up companies 

and individual entrepreneurs to ''markd' their idea to a wide range of 

potential im-estors and, if successful, raise capital at a lower cost. 191 

Marketplace (a.k.a. peer-to-peer, or P2P) lending is simply crowdfunding 

of debt. It seeks to lower the costs of unsecured borrowing by eliminating the 

"'See sr1pra Pan lA 

" ' Cmwdfunding is a fonn of cmwdsourcing, a •~nn thao encompasses a brooder variety 

of mass collaboraoions on a panicular proj~ or idea. Crowdsourdng oft<n in\'OI\'<S sharing 

of inno,·ative ideas and soliciting of technical suppon. fttdba<k, or other resources from I he 

"crowd.t' r\lthough not oftieially known as ··cro"dsourcing.'' the practice of galhering 

moneoary or in-kind contributions from a dispersed group of people was widely used 

throughout history. most notably for '""ious charioable purposes. See, Craig R. Everet~ 

OrigiliS and Di!l~lopmenl of Credii·Baud Cro•·l/jimtli1.g (May 28, 2014), ami/able a/ 

hnpJ/oaprcy.m.comlsolllpapmcfm'!bs(l3{'( id~l-142897. The advent of the Internet 

enabkd the rapjdde,·elopment of numerous pecr-t<>-petronlinell3nsaelioool platfonns(c.g., 

eBay and Napster) that 1«1 to the emergentt of the cuntnt fonns of online crowd funding. 

~ COO~IlANT. MARXIoTPLACE LE.\'DIXG: A MATURING M,IRKff MEA~'S NE•' IRT~'U 

MODELS, USI~'ESS OPPORTUXITIES (July 2014), at 2. amiloble 01 

hup:tA'""'·cogni7.ano.contllnsights1Vbiteoapcrs/Marl<etplace-Ltnding-A-Maouring- Matkei-Means-New-Panntr·Modcls·BU5iness·Oooonunitits=cOOc~%9.txlf. In this Miele, 

I use the 1emo cmwdfunding 10 refer solely 10 fmaneial n~ estment·oricn «<oransactions. 

rather ohan philanthropically mooi1~1ed loons or donalions. 191 loon Macleod fteminway & Shelden R)•an Hoffman, Pnxee1l ol Your Peril: 

Cro•·djilll(/i1.g ~11d tire Secflrilil!$ Ac1 of/933. JS n,,~. L. REv. 879. SSJ (201 I). 

"' For exampl<8 of on lin< croll'dfunding •it<8 ohat provide a "inual marketplace for 

prospective tquity in\'<Siors and capital-setking <nii\'Jlreneurs. see Fundable, 

hnos:/Anlw.fitndablo.oomllanding!croll'dfundino?gclid=COX31PXI78cCFOcTH"1ld Sw 

Q1g; Eq ityN~ . hnes:I/""""·PJUit\'net.comJhow.it·\\orks.aspx. 
106 


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48 FINTECH - drajl 18-Sep-18 

need for the services of a commercial bank or any other institutional lender. 

ln the U.S., online P2P lending got its otlicial start in late 2005, when 

Prosper.com launched its online platfomt19l Prosper.com and LendingCiub, 

both of which focused initially on consolidation of consumer debt, quickly 

became the leading U.S. marketplace lending platforms. Their success 

spurred rapid gro11~h of online lending platfonns specializing in various loan 

products. ~ 

Although individual lending platfom1s' operational models may difler, 

they generally share certain basic features. They typically cap the size and 

maturity of individual loans, limit individual investors' exposure to a 

particular borrower by breaking up the loan amount among a large number 

of investors, and use internal and external credit ratings to detennine the riskadjusted interest rate on each loan. The lending platfonn operators collect 

transaction and servicing fees19; The basic idea is that, by using advanced 

technology to process infonnation and underwrite loans quickly and at a low 

cost, marketplace lending sites are able to match individual lenders and 

borrowers efliciently and transparently.196 

The proliferation of marketplace financing sites in the last decade led 

some obscrv.:rs to declare "the beginning of a revolution in how the general 

public allocates capital."191 Others welcomed it as a rising tide of ultimate 

"disintennediation."

1

n However, the business quickly auracted sophisticated 

financial players able to conduct credit analysis and run risk models to tailor 

higher returns from their marketplace loan investments. Hedge funds, private 

equity funds, banks, insurance companies, and wealthy individuals became 

the primary buyers of marketplace loan products,

199 which genemlly have 

19; 5.£ Prosper Marl<eiplace, Inc .. hnpsdl""w·P!OlM·'omlhelplgroeraV#pmlectioo. 

The model was pionecml in the United Kingdom h)' Zopa. which launched the first pett·IO· 

po<r lending platfonn in 200S. 

'" RICIIARDS KIBRE & 0RBE LLP, 2015 SURVEY DF U.S. MAR~CIPt.ACE LENDING, 

m'lliloble or htm:I/\\\\W.rltollp.oom/ass<is/anachmenl<fRK0%20Ltnder%20Swvev.OOf. 

"' See Renaud laplanchc, fi•·e Big .~61hs obcur Mllfketploce lendu~g. Mt BANKER 

(Jan. 28. 2015). 

'" Unlike banks, 1'2P platfonns typically do nol nW;e loans using th<ir 011n balance 

shceiS: they si•1ply lind indiriduals 11·illing to lend money 10 a panicular borrower al a 

panicular rate. In this model, 0\'Cil high-risk borro11·ers should be able to find potential 

lenders witting 10 lake as1nall ponion of ihc risk, if compensated mordingly. Banks and 

other balance-sheet lenders don't hare suth Oe~ibi ity. 

'"C. Stcra~ Bmdford. Cro•rdfimding and the Ftderal SeruritifS La•·s. 20t2 COLU~I. 

Bcs. L REV. 1.5 (2012). 191 Andrew Verstein, The Misregulation of Person•to-Person Lending. 45 U.C. O,\I'IS 

l. RF.V. 445. 4-19 (2011). 

"' See Kc,in Wack <I al., lirnomtion of the Year: Online Markerp/11ce Le11ding. AM. 

BANK~R (D<c. 17, 2014). Thus, in~ cstment managen1en1 giant Bl"kRock hearily invcsticd 

in mari:Ciplacc lending. "nile Goorgc Soros and fonner PIMCO CEO Mohamed EJ.Erian 

"'"'among the hig)l.proflle indiridual investors. ld 
107 


91818049.eps

8-Sep-18) FINTECH - draft 49 

higher interest rates than traditional bank loans.200 Many of these investments 

are leveraged and subsequently securitized.l1l1 

The entry of yield-hungry institutional investors led to increased 

competition in the sector and pushed marketplace lenders to grow their loan 

origination 1•olumes, to diversify their loan products, and to consolidate.

101 

Marketplace lenders now routinely fom1 partnerships with banks and other 

institutional investors, pursuant to which banks and other investors commit 

to buying a certain percentage of whole loans originated by or through the 

marketplace platform.

203 To satisfy institutional investors' demand for this 

lucrative asset class, marketplace lenders intensified their bcrroweracquisition efforts, partly by extending more high-risk loans.!Ol 

In short, within a decade, marketplace lending has effectively evolved 

from an alternative fonn of peer-to-peer finance into a post-crisis rendition 

of subprime lending and shadow-banking securitiwtion.

205 In this sense, it 

functions as a classic channel of continuous synthesizing of tradable assets 

used to construct multi-layered and interconnected chains of financial claims. 

Rather than reinventing credit as a truly decentralized and democratic means 

of mutual self-help, today's marketplace lending operates primarily as a 

means of scaling up trading volumes in institutionally-dominated wholesale 

markets. 

206 

By 2017, the tech-savvy public's attention had shifted to a new form of 

digital crowdfunding: "initial coin offerings," or !COs. In an !CO, a firm 

planning to develop and produce some form of a digital product- e.g., new 

software-sells project-specific digital tokens that can be used as units of 

, For e.xample, LendingCtub has delivered an adjusted annualized return of n<arly 

8.7% oo the fi.st S8 billioo of issued loons. and issued o1·er Sl billion in personal loons 

carrying intere;;t rate above 20%. Todd llal;cr, Morkerploce Lendm Are a S)>lemic RisK, 

A~t. BANKER (Aug. 17, 2015). In 2014, 0n0..'<k, a marketplace pbtform specializing in 

small business lending, reportedly issued loans a1 an mragc annual percentage rntc of 54%. 

Kenneth A. Posner. AllertWiire unders Hm~ a Ways 10 Go 10 Ensure "Rerolulion ... AM. 

B,INKER (Jan. 12, 2015). 

"'' See Kevin Wack et al .. supra note 199. 

,., See Ke1·in Wack. Sltokealll Is Coating. Morke1place Lendm II' am. A \1. BANKER 

(Apr. 16, 2015). 

»l See Kevin Wack ct al., supr{l note 199; Mike Cagney, l/o11· ,\l{lrkelplace Lemlm 

Will Sm~ filllliiCial Senim; A~!. BANKER (Aug. 19; 2015), 

""See Baker, s11pra note 200. 

"" See Filwmce Franchise. supra note 8. a1 1207. 

"' In June 2018, S<lfi's CEO announeed the company's new strnlegy of making loan 

decisions, fnnds disbursements, and securitizations instant. Penny Crosman. Sofi 's CEO 

Str(l{eg)'for S11ctess: 'Maket\'1!1)' TransaeliOII fas1er'. AM. B,INKER (June 20, 2018). This 

aptly underJCores the significant pot.ntial of marketplace l.nding as a broad·based platform 

for lhe ccnlinuous synth<sizing or high-yield assets and S<aling up or secondal)•-marl:et 

trnding. 
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so FINTECH - drajl 18-Sep-18 

currency in purchaser's hands once the project launches.

207 Depending on the 

scheme, these tokens may be used for different purposes: some of them 

simply allow access to an online platfom1 or grant participation and 

governance rights in a particular online network, others can be used to buy 

the product or service being funded, and yet others may entitle their holders 

to an actual portion of profits from the project in question.208 The vast 

majority of !COs to date are done using smart contracts on the Ethereum 

platfonn.lW The key advantage of using Ethereum is that its technology 

allows for smooth post-ICO trading of the tokens: i.e., it enables the 

emergence of a secondary market in these new digital "assets.''210 

In 2014, Ethcreum itself became one of the first examples of a sue<:essful 

ICO by raising about $18.4 million through pre-sales of ether, its native 

crypto-currency.

111 As the popularity and use of Ethereum as the platform of 

choice for various crypto-proje<:ts grew, the value of ether increased 

correspondingly, making it a valuable financial investment.111 ICOs went 

mainstream in 2017, which saw hundreds of offerings raise billions of 

dollars.m Notably, the most successful I COs of the year included ventures 

promising to improve the existing blockchain infrastructure, to otTer a 

"better" cryptocurrency, or to make existing crypto-assets easier to 

monetize.m 

"" See snpra note 28 and acoompanying 1ex1. 

"' See Wilson Sonsini Goodrich & Rosati, Initial Coin Offerings: An Omn·iew of 

Regulatw)' Considerations (June 30, 2017). ami/able at 

hnps'l/>.'""'·idsupm.conlilegalnc"ronitiol-coin-ofierings-on-o\·ef\'iew-of-98251/. 

"" See CASEY & VIGNA, supra note 186 at 99. Ethereum is designoo as a common 

platform for hosting an infinite ,·ari<W of so-called "decentralized apps" (or "Dapps") for 

cryptograpbically reoording and exchanging all manocr of digitizoo data: medical records, 

land titles. titles 10 goods, marriage cenifieales- copyright and other right~ contractual 

payments, etc. Computers mifying tran.<:actions erun ether, Ethereum's cryptocum:ney. 

whose ''alue accordingly increases as the n<tworl: gtl)"'· Ethereum's internal programming 

language allows third patties to "rite an unlimited variety of programs, thus enabling on 

unlimited \'3ri<t)' of"sman contm<IS." /d. at 79·81. 

!l(l Ethereum's standardized set of smart-contl'a(t instructioos allows \'arious digital 

1okrns 10 retain a common. consistent formal allowing these tokens lobe traded on lOp of 

Ether<um • s blockchain <:\'en aller theconclusion of an I CO. /d. at 102. 

"' "'at 84 99 !I! /d~ , , 

'" Attor1iing to some estimates, the total amoum raised in I COs in 1017 e~cookd Si.6 

billion. Oscar Williams-Gnn, Only 48%of/COs Were S!ICcessfull.ast l'eilf but Start11ps Stj/1 

Mmraged ro Raise Si.6 Billion, BuSt:<ESS ~StD R (Jon. 31. 2018), {ll~ilable at 

hno'lA'""'·bu.<i,.,_<inside<.comlhow·muth-raise<l·icoo·20 17-tokendata-20 17·201 8·1. 

'" Oscar Williilllts·Gnn. The II Biggest/CO Fundraises of 2017. BUSINESS I~SIOF.R 

(Jan. I. 2018). ami/able ar hnp1/"""'·businessinsider.com'the-IQ.bigml·ioo-fundraises· 

of-2017-2017-12'?1"'UK&IR•T. The biggestiCO of1017 wos Fileeoin, which raised about 

$?57 million from sales of a Ioken en1itling itS holder; to blockchain-based dau storage 

space. /d. Perhaps the most interesting case from the perspeeti\'eofthis llnicle was the SilL T 
109 


91818051.eps

8-Sep-18) FINTECH - drajl 51 

To fintech enthusiasts, !COs signal a profoundly democratic shill in 

market power from traditional venture capital finns to users of the relevant 

digital prod~ct or service.m Yet, it is undeniable that I COs are often seen as 

purely speculative financial plays.

2

t

6 Throughout 2017, investors were ready 

to snap up ICO ··assets," often in a matter of minutes, without much due 

diligence conducted in traditional securities ofleringsm They didn't appear 

to worry about whether or not the tokens they were buying were related to an 

economically viable enterprise, or to 1/11)' economic activity outside the 

crypto-asset spacem Undiseriminating investor demand for tradable tokens 

drove inflated ICO valuations, a familiar sign of a speculative asset bocmm 

Predictably, surging ICOs raised alann among financial regulators 

concerned with investor fraud and criminally-connected fund-raising.

220 

From a systemic perspective, however, I COs implicate a far more structurally 

significant shift. Complete virtualization of tradable assets enables- at least 

in principle. but very likely in practice as well - a virtually complete 

separation of the financial system from the real economy. Free of any 

"natural" productivity-related constraints, linancial markets will easily 

morph into sites of pure crypto-speculation. Left unconstrained. this 

continuous generation of tradable bits of encrypted data will easily transcend 

the limits of traditional systemic stability regulation, leaving both the 

financial system and the real economy vulnerable to shocks originating in an 

increasingly self-referential crypto-space. It will also render regulators' task 

of protecting investors and capital markets from abuse and misconduct 

inherently impossible to perfonn via traditional means. 

Lending Platform I CO, with the tokens designed to allow holders of cryptocutTencies to use 

them as collalernl for borrowing in fiat currendes./d. 

"' See Ricllard \Vatcrs, To Cain a Crtct: Silicon Yollr;>s Cryplocurret•CJ' Bocm, Ft~. 

l)MES(Septll, 2011). 

"'ld. 

"' SeeCAS~Y & VtGl41.supro note 186ati03·1Q.I (citing to an tCOby 0no5is. wh()S< 

platform allo11~ usm to m31c prediction markets for b<uing oo an)1hing, in 11hich the 

rompany raised Sll.S mittioo in tweh•e minutes). 

"' See Wa1crs, supra note 215. 

"' /d. Even high po~·ICO failure rates did 1101 dampen this sp<'f.ulativc dW!and, See 

i\aroo Hankin. N.arly half of all 1017 /COs foiled. MARKETIVATCII.COll (Feb. 26. 20t8). 

m·ailabfe at hllos1Am"·.market"J!eh.comlst0Jylnmh•·half=9f·all·201 1-iros-hare-faii<JI· 

2018.02-26. 

"" For example. China and South Korea banned !COs in 2011. See Darryn Pollock, 

From Gibrolrar to Australia: /low C01tnrries Approocfl /COs, COI~'I'ELEGRAI'II.COM (Feb. 

16, 2018~ m·aifabfe "' httm11rointelegrnph.romlnews/from·gibrnhar-to·a!!Stralia-howrountries·•o!!!ooeh·i<os. In the U.S., the SEC announctd that ccnain ICOs may constitute 

se<uritics iss~ subject to fedNal sceuritics laws. Su hnm:lill\\w.w;.govnco. 
110 


91818052.eps

52 FINTECH - drajl 18-Sep-18 

2. Robo-Adl'ising: Scaling Up Trading 

Robo-ad,•isors are "automated interfaces that otTer investment advice and 

discretionary investment management services without an intervemion of a 

human advisor, using algorithms and asset allocation models that are 

advertised as being tailored to each individual's investmem nceds.'"111 Robo· 

advising is quickly becoming a mainstream financial service. Charles 

Schwab, Vanguard, and Fidelity oiler robe-advising services.m Even 

Morgan Stanley, one of Wall Street's most 1·enerable investment banks, 

launched a robo-advising unit in Detember 2017m 

Because robo-advisors eliminate expensive human labor and use 

algorithmic 1rading to maintain or adjust clients' portfolio allocations, their 

services are significantly cheaper than those of traditional wealth 

managers.m Robo-advisors are potentially able to otTer relatively simple and 

cost-ell'ttti1·e inve.stment options - mainly, index mutual funds and passive 

exchange-traded funds (ETFs)- to a wider array of clientsm The absence of 

human intervention is also touted as an anractive feature of robo-advising 

because it promises to eliminate potential conflicts of interest plaguing the 

lund management industrym Forthese reasons, proponents of robo-advising 

routinely portray it as a valuable tool of financial inclusion and 

"democratizing" wealth management by broadening its availability beyond 

the exclusive world of wealthy people?21 Critics, on the other hand, contest 

these claims as significantly overstating the cost-elliciency and integrity of 

robo-advice and warn against channeling retirement and retail investors' 

money into these automated accounts.m 

"' Cbiu. s11pro noce4 atSS. Robo-ad1isors r<ly on online questionnaires, filled out by 

prospecti"e clients, to de1•ise asset allocation and trading strategies that most closely track 

each client's e.press<d inl'estment goa ~ preferences, and general risk parameters. 

'" See Ma11hcw Frankel, Robo-Adrisers: II'!Jor The)' Cosr irml 11'/wr l'ou Gtt, MOlUI' 

FOOL (Nov. 13, 2011), ami/able til huo<#"\\W.fooJ oomln.~irement/201711 t/131roboadlisors-what·th<y·cost·and·"ilat·VOU·W.asP'· Cllarles Sch"~b and Vanguard are, of 

C()UfSe, already well kno'm as pioneers in democratii::in.g mess 10 weallh management 

m Maria Terekhova. Morgau Sroulq• l.irunclres a Robo-Ad•·iser After 6 Monr!J Pilot. 

BUSINESS INSIDER (Dec. 5, 2017). hnp:/Amll.businessinsid<r.oomlmorgan-stanley· 

launchearobt>-adl iS()(·afltr·l&.month·pilot·2017-12. 

"'Chiu, '"P'" noce 4 at &9. mid. "'I d. For an easily accessible and oomprehcnsi"e account of ptrnsil'eagency problems 

in the mutual fund industry. s<:e WtLUI~I A. BtROTHimE. EMPIRE Of lllE f'tll'O: ThE IVA Y 

IVESAVENOW (2016). 

"' See Deloine, Tile f:xpo11sio11 ofRobo-Mdsory i11 Wro/th Ma11ogeme111 (Aug. 2016). 

~ 

hnos:IAnn•Q.dcloine.romlcontent/darn/Dc\oitte'de!Documentslfinancial-sm;ces!De\oiu .. 

"'See. e.g .. ,\felanic Fein. Roho-A<h-ism: A Closer Look (June 30, 2015),amilableat 

hnos1!paoers.ssm.comfsoll!papers.efin°ai>s!!l!ct id-265$70 I. 
111 


91818053.eps

8-Sep-18) FINTECH - drajl 53 

It is hardly surprising that the continuing gro\\1h of robo-advising invites 

debate. Rep ~c ng humans with algorithms in an area traditionally based on 

relationships and exercise of professional judgment by trusted fiduciaries is 

not simply a matter of lowering fees. It raises a host of important legal and 

regulatory issues, especially with respect to advisors' fiduciary duties and 

investor protection under securities laws.m These issues, however, are 

beyond the scope of this Article. For present purposes, it is critical to focus 

on the broader potential systemic signiticance of robo-advising. 

One important factor in this respect is that the lower cost - and thus 

broader accessibility- of robo-advising is not simply a result of eliminating 

the expense <lf hiring a human expert. This cost efiiciency is also a product 

of passive index-tracking strategies typically pursued by robo-advisers. 

Robo-ad\'ice tends to channel clients' money into ETFs and other passive 

investments, often also detennined by algorithms, which arc inherently 

cheaper than actively managed fund products.130 

In this sense, robo-advising appears to amplify both fundamental patterns 

of secondary market growth, discussed above: it enables synthesizing of new 

tradable assets, and it serves to scale up the aggregate trading activity in 

financial markets.lll Reaching significant segments of the population 

previously unable to participate in capital markers potentially improves 

ordinary people's access to investment opportunities. At least as importantly, 

however, it also improves the market's "access" to their savings. Through 

robo-advisin,g, new market entrants' money is used to create new financial 

products thai can then be pooled and layered, potentially many times over. 

This constant intltLx of new "base" products is critical for sustaining the 

financial market's built-in tendency to keep scaling up. 

Fu~he nore, as discussed abo\'e, the central role of algorithmic trading 

in the robo-ad\'ising business model has a direct- and potentially massive -

acceleration etTect on financial asset trading.m The fact that, in generating 

all of this additional trading actil'ity, robo-ad\'iSCrs tend to use similar 

algorithms raises serious stability-related concerns about potential herding 

behavior and the possibility of rapid unidirectional pcrt[olio shiftsm Not 

only are there many more super-fast trades being executed via robo-ad\'isors' 

algorithms, these trades are likely to fom1 potentially highly correlated tidal 

m See. e.g .. Chiu, srtpra note~; Melanic Fein, Are Robo·A<Msrm Fiducituies? (Sept.), 

20 t1 ), arailabl e "' hnps:Jiparers.ssm.oom!sollipapcrl.£fm ?abs!r3£t id= 30182§8. 

"' fi \NCIAL STA61LITY IJo,IRP. Fiuancilll S{{lbiU9· lmplic01ions from Fin Tech: 

Supen·isoryond Regulillory lssftes 1ha1 MI!Til Alllhorilies' Allell/ion (21 June 2011). at43-

4S, m·ai/ob/e or hnp:l/"""'·fsb.orglwp·rontent/uploods!R210617.J?df. 

2ll See supra Pan II.B. "' See supra Pan ti.B.2.c. 

m fiNANCIAL STA!ItUlY llOARD. supra note 2)0. at 45-16. 
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91818054.eps

54 FINTECH - drajl 18-Sep-18 

waves of money mo1•ing in and out of the same asset classesm 

In fact, there are serious reasons to belie,·e that the real driver behind the 

rapidly rising popularity of robo-advising is not the commonly toUied 

"democratizing" impulse butt he growing appeal of algorithmic trading as a 

portfolio-enhancingstr.ncgy for wealthy investors. Thus, it appears that roboadvisors are increasingly targeting wealthy (or relatively wealthy) investors 

who are already in the market, rather than the truly "under-served" lowincome people.23; Many large robo-advisors are introducing minimum 

account balance requirements for access to digital investment services, 

ranging from $25,000 to $50,000 and possibly higher.236 For this contingent 

of wealthy investors, robo-services are a source of new, cost-efficient 

portfolio di1•ersification opportunities. In line with the same logic, Morgan 

Stanley's robo-advising unit is said to target primarily the Millennia! children 

of the bank's existing clientsm 

Tellingly, there are stark parallels between these developments and the 

dynamics in marketplace lending, discussed above. There. what started as a 

promise of a peer-to-peer credit system quickly evolved into another 

rendition of the institution-dominated market for high-yield consumer 

debt.m Here, what started as a promise of opening the world of im•estment 

to the poor is quickly evolving into the reality of opening the world of (yet 

more) speculative trading to the wealthy. 

As these examples demonstrate, technology alone cannot make the 

financial sys1em more "democratic" or ')ust." Democratizing finance c~nnot 

be reduced to a purely technical exercise in decentralizing financial services 

or making them cheaper through the use of algorithms. II is an inherently 

polilical exercise, and only a democratic polity can achie1•e that goal through 

a coherent and comprehensive program of institutional refonnsY~ The real 

!~ /d. 

"' See Bl)'llll Yur,-an & Sulcman Din, Will Clreap Ad>·ice Tum Off ll'ealr/9· Cliems' 

A.lt 13ANKER (JIIDC 5, 2018). ol"lliloble or hnps://""w.amcrieanbanker.oomlntwtlfofth. 

third-S«uritieHnd-fock!iiV-partner-on-automate:d-M•ice-for·smallin''es!Ors'tutm camooign .. intrada)'=C· 

Jun%205¥o202~18&U1m me:dium•email&utm source-"llell>lcner&eid•t2aM-ldQ69cdl6c 

fddaa391c2~ci>7Q.I2 (disoussing how large financial ins~itutions' robo-ad\'ising sor~ ices 

"11£" the "mass aftluent audience"). 

,;o /d. ("IV ells fargo and U.S. Bank hare digital ad1'ice acrotult minimums double that 

of Fifth Third's. And by bll'llding hwnan SCFI ice with digital platforn>s. other fim>S ha~ e 

soug)n Matthicr clients. Schwab's lnt<ltigen1 Ad•·isory scnice has a S25.000 aceount 

minimum. 11hi!e Vanguard's Personal Ad1isor Ser~ ices (the largest digital adrict platfonn. 

with OI'CI SIOO billion in assets under management) has a $50.000 accoont minimum."). 

"' See Tcrekhora, supra note 223. 

m See supra Panlii.B.I. 

m For 3 comprehensive theore1it'.al and prac1ical analysis and justilication of, and 

specific proposals for implcmeming. such institutional reform. sec. <.g .. Public Acrors. supra 
113 


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8-Sep-18) FINTECH - draft 55 

question is whether the exciting new technology will be used to aid or to 

impede this process. 

C. Finrech as a S) lemic Phenomenon: Uuseu/ing rhe Public-Privare 

Balance 

This brief overview of certain key developments in the rapidly evolving 

fintech sector is not meant to be an exhaustive catalogue of everything that 

this sector has to offer. Nor does it claim to present a full analysis of specific 

legal, technical, and policy issues these developments raise.

2

.ro Instead, the 

purpose of the presem discussion is to trace the fundamental continuity 

behind the fintech "disruption," in search of a new conceptual and nonnative 

perspective for understanding fintech as a systemic phenomenon. 

Standard accounts of the systemic implications of fintech activities tend 

to present lengthy sets of fintech-related factors that are likely eitherto reduce 

various systemic risks or to amplify them. Some of the commonly listed 

financial stability enhancers include, for example, systemic risk-reducing 

eiTects of making transacting faster and easier (i.e., eliminating market 

"frictions") and greater competition in the financial services industry.2

l

1 

Potential systemic risk amplifiers, on the other hand, include the heightened 

tendency toward herding behavior and procyclicality, greater vulnerability to 

technical glitches and operational failures, and the rise of systemic 

importance of non-financial firms242 

Although these are valid and serious arguments worthy of auention and 

study, the focus of this Article is on the deeper - and broader - dynamics 

within the financial system. As argued above, the New Deal political 

seulement established the fundamental balance of public and private roles, 

competencies, and responsibilities in the financial sphere.w Under its tenns, 

private market participants are primarily in control of allocating financial 

capital, while sovereign public is primarily responsible for maintaining the 

macro-financial stabilityw From this perspective, the emerging fintech 

technologies and activities are not merely recreating some of the familiar 

sources of systemic risk or rearranging the familiar institutional landscape of 

financial services. At the higher level of magnitude, fintech's systemic impact 

has to be assessed in tenns of its potential to cause a decisive shift in the 

currently existing public-private balance in finance. 

While it is diOicuh to generalize across the evolving and varied fintech 

note 39; Nlllimrollmu rmem Authoril)~ Stlpr(l nole 39. 

'"for rec<:nl analyses oflhese ss~ S<e sour«s ciled supra no1es J-4. 

"' See flllANCIAL STABilln' BO,\RD. sr1pra nole 230, al 16-1 7. 

'"/d. a1 20-11. 

'''See sr1pra P3!1 I.B. Z« fd. 
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56 FINTECH - draft [8-Sep-18 

space, the new technologies' self-proclaimed unifying raison d'etre is 

qualitative transfonnation and optimiuuion of transactional capacity in 

financial ma.rkets. hnportantly, that refers primarily, if not exclusively, to 

privale transacting capacity245 In some instances, this goal of directly 

empowering the private, as opposed to the public, side of the financial market 

is quite explicit. Bitcoin enthusiasts, for example, openly tout that 

cryptocurreltcy's ambition and ability to do away with sovereign 

go1•emments' control over money.2l61n most instances, howe1·er, the rhetoric 

of finteeh consciously emphasizes its potential to yield significant public 

benefits: financial inclusion, greater financial autonomy, and greater 

convenience, among otherthings.241 

Yet, even a brief examination of these new technologies reveals the sense 

in which they systematically tip the scale in favor oflhe primte, as opposed 

to the public side of the New Deal settlement. By making transacting in 

financial markets infinitely faster, cheaper, and easier to accomplish, fintech 

critically augments the ability of private actors to synthesize tradable 

financial claims- i.e., private liabilities- and thus generate new financial 

risks on an unprecedented scale. Moreover, as the discussion of Bitcoin and 

ICOs shows, new crypto-technology enables private firms to S)1lthesize 

tradable financial assets eO'ectively out of thin airN& This may be thought of 

as the crucial last step in the decades-long process of virtualization of 

financial claims - e.g., through creation of derivatives and other highly 

structured financial products - which will finally render financial markets 

entirely self-referential. 

It is diflicult to overestimate the significance of this leap for the financial 

- and, more broadly, economic- system. Making financial trading explicitly 

divorced from the production of any actual goods or services in the real, or 

non-financial, economy will have enonnous consequences both for financing 

and organizing the entire economic system and for managing the financial 

sector. 

Among other things, it will make it increasingly diOicult, if not 

impossible, for the sovereign public to continue safeguarding and 

guaranteeing macro-financial stability. The sheer scale and complexity of the 

financial market effectively "liberated" from exogenously imposed 

constraints on its gro111h will make it inherently more volatile and unstable-

"' This is, of course. natural, given lhat most finte<h applications are being dcwlopcd 

by profit-seeking pril'ate market participant~ Go,·cmments may and do panicipate in fin tech 

projects, especially as they e'plore potential for issuing sovereign cryptocurrencies. butt hey 

ha1•e not yet rommande<rcd any particular tcchnol"!!~' for principally public use. 

'" .lee Laslra & Allen, supra note 157 at 18-20. 

m /d. See clso. Troosruy Repor1, s•tpra norc 3, at 17. 1

'' .leesr~praPanlll.i\.1. 
115 


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8-Sep-18) FINTECH - draft 57 

and, consequently, both far more dependent on public support and requiring 

far greater quantities of such support. The same factors, however, will also 

make it increasingly difficult, if not impossible, for the public to control, or 

even track, new technology-driven proliferation of risk in the financial 

system. Moreover, the underlying policy rationale for the public 

accommodation of privately created financial liabilities - i.e., the publicly 

salient role of financial markets in channeling investments in the real 

economy- will efTectively disappear.ln short, in this new environment, the 

public will be forced to bear a vastly greater (and diOicult to quantify in 

advance) burden of stabilizing an increasingly unstable and uncontrollable 

financial system that keeps growing for the sake of its 01111 gro111h. 

The key point here is not to assert the inevitability of this, or any other, 

specific scenario. My purpose is to show why fintech as a systemic 

phenomenon cannot be reduced to a mere collection of specific transactional 

friction-solving tools. Fintech has to be appreciated for its potentially gamechanging efTect on the existing balance of public and private power to define 

the fundamental purpose and direction of the financial system. Even at this 

early stage, it is increasingly apparent that various forms of "disruptive" 

fintech technologies, in fact, operate in tandem with and amplify the same 

long-standing financial market dj1lamics- pooling and layering of financial 

assets and acceleration and compression of financial transactions- that have 

been gradually eroding the New Deal settlement. If (or when?) fintech 

delivers on its promise to make these mechanisms virtually frictionless, thus 

taking their operation to a qualitatively diflerentlevel, the financial market 

will completely forsake the frail confines of the New Deal settlement. We 

need to start thinking seriously about what should replace it. In this sense, 

fintech is ultimately a matter of public policy of the highest order. 

CONCLUSION 

Fintech is visibly "disrupting" traditional methods of delivering financial 

services and conducting financial transactions. Less visibly, it is also 

changing the way we think about finance and envision its future trajectory. 

The rise of fintech is gradually recasting our collective understanding of the 

financial system in seemingly objective - science-driven and norn1atil•cly 

neutral - terms, as simply another sphere of app ng advanced infonmation 

technologies and computing power to eliminate specific transactional 

"frictions" in financial markets. By making transacting faster, easier, cheaper, 

and instantly adjustable to individual partie.s' needs and preferences, new 

technology seems to promise a '\vin-win" solution to the financial system's 

many ills. 

This Article has presented an alternative account offintech as a systemic, 

as opposed to merely transactional, phenomenon. Grounding the evolving 
116 


91818058.eps

58 FINTECH - draft [8-Sep-18 

fintech trends in the broader institutional context of the financial markets' 

operation, tiH: Article exposed the nomtative and political significance of the 

current fintech moment. The arrival of these new-generation technologies 

enables a pottemially decisive shift in the underlying balance of the sovereign 

public's and private actors' relative powers, competencies, and roles in the 

financial sys1em. By making transacting faster, easier, cheaper, and instantly 

adjustable to individual parties' needs and preferences, new technology is 

empowering private actors to engage in virtually unconstrained financial 

speculation. Unless the public side proactively counters new technologies' 

potentially destabilizing systemic efrects, it may soon find itself in an 

impossible position ofhaving to back up an uncontrollable and unsustainably 

self-referential financial system. 

To be clear, the purpose of this Article is not to over-dramatize potential 

dangers, or to deny potential benefits, of fintech. Far from it. New technology 

opens a wide range of previously inconceivable possibilities for improving 

our shared financial lives and for creating fuller, more capacious fonns of 

financial citizenship.ll9 At this relatively early stage, it would be premature 

to issue any definitive conclusions as to what fintech's ultimate impact on 

society is going to be, or what specific risks individual technologies are going 

to pose to financial stability. It is vitally important, however, to take an 

informed systemic view of the unfolding fintech "revolution" well before 

these risks materialize. Only by doing so can we begin harnessing the 

transfomtative power of fintech for our collective long-term benefit. This 

Article takes a critical first step toward that goal. 

1'9 For a fascina1ing. and fascinatingly optimistic, acooun1 of 1hese possibilities, see 

CASEY & VIG~A, Sllprll nole 186. 
117 

RESPONSES TO WRITTEN QUESTIONS OF SENATOR BROWN 

FROM STEVEN BOMS 

Q.1. Given that companies like Google and Facebook collect enormous amounts of information, and are also in a position to influence what information consumers are exposed to. For example, 

Facebook might show payday loan or private student loan advertisements to servicemembers or to minorities but not its other 

users. 

Should fair lending laws be updated to cover not just the provision of credit, but also targeted advertisement of such products on 

social media platforms? 

A.1. CFDR members believe that fair lending laws represent important public policy. The content of those laws, however, is determined solely by Congress and, when authority is delegated, to regulatory agencies. Each company in the CFDR membership—which 

does not include Google, Facebook, or any similar ‘‘big tech’’ company that operates a social media platform—strives to abide by all 

applicable fair lending laws, at both the State and Federal levels, 

and will continue to abide by fair lending laws if they should 

change in response to your concerns addressed in the predicate to 

this question. 

RESPONSES TO WRITTEN QUESTIONS OF SENATOR SCOTT 

FROM STEVEN BOMS 

Q.1. My ‘‘Making Online Banking Initiation Legal and Easy’’—or 

MOBILE—Act allowed banks and credit unions to use a scan of a 

driver’s license through a mobile device to verify a customer’s identity when opening an account. 

Approximately 16 million adults live in households without a 

checking or savings account and an additional 51 million adults 

live in households that rely on nonbank lenders with sky-high interest rates. 

Yet about 90 percent of unbanked and underbanked adults own 

a mobile phone, of which 75 percent are smartphones. 

Please answer the following with specificity: 

What impact does linking personal finance with mobile and data 

technologies have on the financial well-being of consumers? 

A.1. The ability to link personal finance with mobile and data technologies could significantly decrease the number of unbanked or 

underbanked households in the United States. The first step in 

analyzing the impact of a more seamless flow of data transfer 

through mobile technology would be to asses why these householders are unbanked or underbanked. For some, including those 

who live in rural communities, it may be that the nearest branch 

bank has closed and that the next closest bank is tens of miles 

away. For others, it may be a distrust of the traditional banking 

system, informed perhaps by prior bad experiences or lack of 

knowledge about the services and solutions offered. Either way, 

having access to—and actually availing oneself of—financial services products is critical to consumer financial wellness as it helps 

families manage budgets, establish credit, pay bills, and save for 

the future. 

118 

1 Anna Maria Farias, ‘‘Housing Discrimination Complaint: Assistant Secretary for Fair Housing and Equal Opportunity v. Facebook, Inc.’’, August 13, 2018, https://www.hud.gov/sites/ 

dfiles/PIH/documents/HUDl01-18-0323lComplaint.pdf. 2Facebook has not been found liable for any such acts, and to my knowledge it has not admitted to the allegations in the Assistant Secretary’s complaint. 3Some courts have found that algorithms like those used by Google are speech protected by 

the First Amendment. See Langdon v. Google, Inc., 474 F. Supp. 2d 622, 629–30, (D. Del. 2007). 

Additionally, the Supreme Court in Texas Department of Housing and Community Affairs v. Inclusive Communities Project, Inc., acknowledged that disparate impact liability must be limited 

to avoid ‘‘serious constitutional questions.’’ See Texas Department of Housing and Community 

Affairs v. Inclusive Communities Project, Inc., 135 S. Ct. 2507, 2512 (2015). 

The mobility of technology driven by the near ubiquity of modern 

mobile telephones and digital networking holds great promise to 

reach underserved areas of the country with tailored financial services solutions. The MOBILE Act is a great example of a forwardthinking legislative approach that embraces new ways of using and 

transmitting data. CFDR supports Congress’s building on this success to further erode barriers to the free flow of consumerpermissioned data across interfaces so that all consumers, whether 

presently underserved or not, can make the best use of a 21st century, mobile, data-driven financial services marketplace. 

RESPONSES TO WRITTEN QUESTIONS OF SENATOR BROWN 

FROM BRIAN KNIGHT 

Q.1. Given that companies like Google and Facebook collect enormous amounts of information, and are also in a position to influence what information consumers are exposed to. For example, 

Facebook might show payday loan or private student loan advertisements to servicemembers or to minorities but not its other 

users. 

Should fair lending laws be updated to cover not just the provision of credit, but also targeted advertisement of such products on 

social media platforms? 

A.1. It is reasonable and appropriate to prohibit social media platforms from enabling lenders to use prohibited characteristics to target or withhold credit offers, and regulators should have the ability 

to enforce this prohibition. An illustrative example in a related 

area is found in the Assistant Secretary for Fair Housing and 

Equal Opportunity filing’s of a housing discrimination complaint 

against Facebook for violations of the Fair Housing Act. 1 In its 

complaint, the assistant secretary alleges that Facebook allowed 

advertisers of housing and housing-related services to directly target or withhold ads on the basis of protected classes such as race, 

religion, age, and gender. Such conduct should be prohibited. 2

The question of whether social media sites should be prohibited 

from using neutral data that may correlate with protected classes 

is more complex. Concerns about disparate impact must be balanced with the fact that accurate algorithms based on neutral data 

may also be the most effective way to communicate useful information to potential customers. Additionally, seeking to prohibit the 

use of algorithms using neutral data for conveying ads to customers could face potential constitutional issues. 3 Beyond identifying these potential issues, I have not done sufficient study to 

come to a conclusion on the issue. 

119 

RESPONSES TO WRITTEN QUESTIONS OF SENATOR HELLER 

FROM BRIAN KNIGHT 

Q.1. In Nevada, Industrial Loan Companies (ILCs) play an important role in our economy. There is a growing demand for ILCs 

which have proven to meet consumer needs throughout the country. The current FDIC Chair has said that she welcomes ILC applications. Do you believe that a FinTech company that meets FDIC 

requirements should be allowed to be chartered as an ILC? 

A.1. Expanding competition and innovation in banking services will 

benefit consumers. Therefore, we should have a presumption that 

a FinTech firm that meets the statutory and regulatory requirements for an ILC charter should be granted a charter. Risks created by granting a charter could likely be addressed through existing regulation and competition protection mechanisms. To the extent that additional protections or limitations are needed to handle 

unique circumstances, Congress should pass legislation to create 

those protections or limitations. 

RESPONSES TO WRITTEN QUESTIONS OF SENATOR REED 

FROM SAULE T. OMAROVA 

Q.1. In your testimony, you state that ‘‘Technology is not an end 

in and of itself, it is merely a tool: it can be used to improve our 

collective future or to destroy it. The Committee’s task is to ensure 

that the latter does not happen, while everybody is looking the 

other way.’’ You also mention elsewhere in your testimony that 

FinTech could lead to ‘‘potentially systematic misallocation of credit, and other cross-sectoral abuses of market power.’’ 

Could you please provide us with a couple of concrete examples 

of precisely what we should be trying to avoid? Do you have any 

suggestions for how to avoid these examples? 

A.1. Finance is the lifeblood of the economy, and information is the 

lifeblood of the digital economy. By definition, ‘‘FinTech’’ combines 

both. That means that FinTech firms, either individually or as a 

group, can potentially exercise an unprecedented degree of control 

over the flow of money, information, and physical goods in e-commerce—all at the same time. This potential for extreme concentrations of power across previously separate economic markets raises 

a spectrum of significant public policy concerns, including concerns 

about dominant FinTech conglomerates stifling (instead of promoting) competition in affected markets and misallocating financial 

and other economic resources throughout the economy. 

More narrowly, it also implicates the venerable U.S. principle of 

separating banking from commerce. Goldman Sachs’ recent foray 

into metals warehousing provides a recent real-life example of how 

a large financial institution can combine and abuse market power 

across different, seemingly unrelated, markets. Thus, it has been 

well-documented how Goldman Sachs’ acquisition of Metro, a metals warehousing company, allowed it to control supply—and therefore price—of aluminum in North America, by creating artificial 

bottlenecks in the delivery of physical aluminum to purchaser-companies. Goldman Sachs’ control over the critically important storage facilities gave it both the incentive and the ability to drive up 

the price of aluminum to benefit its own physical commodities tradVerDate Nov 24 2008 14:36 Dec 18, 2018 Jkt 046629 PO 00000 Frm 00123 Fmt 6602 Sfmt 6602 L:\HEARINGS 2018\09-18 ZZDISTILL\91818.TXT JASON
120 

ing and financial derivatives operations. The artificial rise in the 

price of aluminum, however, significantly increased American companies’ production costs and ultimately resulted in higher consumer prices for a wide range of products, from soft drinks to automobiles. 

Big FinTech conglomerates are well-positioned to commit similar 

abuses of market power on a far larger scale. This is one of the 

principal reasons why the direct or indirect formation of such conglomerates, in any organizational from, should not be permitted as 

a matter of public policy and public interest. 

Here is a simple hypothetical example of what can happen if, 

among other things, the Federal Reserve narrows its presently 

flexible interpretation of what constitutes ‘‘controlling influence’’ 

under the Bank Holding Company Act of 1956 (the ‘‘BHC Act’’). 

Thus, Amazon Inc. can buy 24.9 percent of voting equity in multiple U.S. deposit-taking banks, without technically being deemed 

a ‘‘bank holding company’’ (or ‘‘BHC’’). As a result of the Federal 

Reserve’s newly ‘‘clarified’’ interpretive approach, Amazon can easily structure these equity acquisitions in a way that leaves it free 

to continue all of its online commerce, logistics, cloud warehousing, 

and other data management businesses. Yet, Amazon’s size and 

power in these markets will effectively guarantee it a de facto ability to exercise outsized control over each individual bank’s management and business decisions. Amazon’s heft as a potential business 

client, a service provider, or a strategic partner will put it in the 

driver’s seat with respect to the banks in which it technically holds 

‘‘noncontrolling’’ stakes (let us call them ‘‘Amazon-owned banks,’’ 

for simplicity’s sake). 

Amazon can then use its outsized de facto power over these Amazon-owned banks to do the following: 

• It can get sensitive financial or other information on its competitors—i.e., various nonfinancial companies that also happen 

to be Amazon-owned banks’ banking clients—and then uses 

that information either to drive those companies out of business or to force them to do business with Amazon on unfavorable terms. 

• Amazon can also pressure Amazon-owned banks to extend 

credit to businesses affiliated with or favored by Amazon, 

which will give it additional leverage over those ‘‘favored’’ companies and thus increase its market power in the affected sectors. 

• Amazon can also make Amazon-owned banks refuse credit to 

its direct competitors or to any other ‘‘un-favored’’ local companies. 

In each case, Amazon’s self-interested behavior will result in significant market distortions and inefficiencies and compromise federally insured banks’ ability to perform the critical task of channeling capital to its more productive uses in the real economy. 

From this perspective, allowing the formation of big FinTech (or 

TechFin) conglomerates will pose a grave danger to the country’s 

long-term economic growth—and, ultimately, its social and political 

stability. 

121 

To prevent this and many other similarly dangerous outcomes, it 

is crucial that policymakers always place the arguments that, in 

one way or another, call for ‘‘facilitating innovation’’ or ‘‘modernizing financial regulation’’ in the context of how they impact the 

broader financial and economic market structure and integrity. 

Rhetoric notwithstanding, no FinTech-related proposals and arguments that could potentially result in the creation of large financetechnology (or tech-finance) conglomerates should be adopted into 

actual policy. 

122 

ADDITIONAL MATERIAL SUPPLIED FOR THE RECORD

LETTER FROM THE AMERICAN ACADEMY OF ACTUARIES SUBMITTED 

BY CHAIRMAN MIKE CRAPO 


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123 


91818060.eps

We appreciate the opportunity to provide these comments. If you have any questions or would 

like to discuss further, please contact Nikhail Nigam, the Academy's policy analyst for risk 

management and financial reporting issues, at 202-223-8196 or ni2am®actuary.ore. 

Sincerely, 

Robert Beuerlein, MAAA, FSA, FCA, CERA 

Chairperson 

Big Data Task Force 

American Academy of Actuaries 

CC: Members of the Senate Committee on Banking, Housing, and Urban A flairs 
124 


91818061.eps
125 


91818062.eps

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126 


91818063.eps

Big Data 

and the 

Role of the Actuary 

JUNE 2018 

American Academy of Actuaries 

Big Data Task Force 

Sob Beuetlein, MAAA FSA. FCA. CEAA-Choilpef!OII 

Oorolhy Andrews, MAAA, ASA. CSPA-'fKeChoir(JfflOII 

Mal)' Bahna•Nolan, MAAA. FSA. CERA 

Elena Bia<:k. MAAA. EA. FCA. FSA 

Elil<lbelh Bril( ~lAM FSA 

Palrick Cau~grove, MAAA, ACAS 

Roben Curl)'. MAAA, fCAS 

lan 0\Jn<an, MAAA, FSA. FCIA,FIA 

Seong-Min Eom, MAAA. FSA 

Andy Ferri~ MAAA,FCA,FSA 

Audrey Halvorson, ~lAM FSA 

Will~m Hine>, MAAA, FSA 

liaw Huafl!> MAAA, EA. FCA. FSA 

Roben Mkcoli~ MAAA. FCA. FCAS 

David Sandberg. MAAA FSA. ClAA 

Manin Snow, MAAA, FSA 

Jim Toole, ~lAM CERA. FSA 

Jason Von8erg<r1. MAAA, fSA 
127 


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Contents 

Executive Summary 

Section 1: Current and Emerging Practices 8 

What Is Big Data' 

The American Academy of Actuaries' Role 10 

Data AnalyticsTechniquesand Methodologies 10 

Application of Predictive Analytks 12 

Practke-Aiea-Specific pplications 14 

Considerations in the Use of Predictive Arlalytics 17 

Data Sources 19 

Section II: Regulatory Considerations 23 

Benefits and Challenges o lnrurers, Rf9ulators, and Consumers 23 

ExistingRf9ulatoryframework 2S 

Eme~ging Rf9ulatory Developments 28 

Section Ill: Professionalism 36 

Actuarial Professionalism 37 

Ethical Considerations 40 

Algorithms, Techniques, Correlation, and Causality 41 

Role ofthe Actuary 44 

Appendices 45 

Appendix l:lnsurTech 4S 

Appendix 2: Actuarial Standards of Practice (ASDPs) S 1 
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Executive Summary 

Remarkable advances have been made over the past decade in the use of Big Data, including the 

Internet ofThings, machine learning. cognitive computing. and artificial i telligence, and the fiel 

continues to evolve. These advances have led to the development of a multi·billion·dollar industry 

referred to as lnsurTech, the innovative use oftechnology in insurance, which is expected to have a 

significant impact on insurance and the work that actuaries perform. 

While the use of Big Data in the propertyand casually insurance art':l is more dmloped 

than in some of the other areas of actuarial practice, signifi ant advances have been made 

in recent yt':lrs in the use of Big Data in health and life insurance. Similar advances in the 

pension art':l ha1•e not been as noticeable. However, it c:~n be expected that over the next 

dec:~ de, all areas of actuarial practice "'" be signifi andy impacted by the use of Big Data. 

What Is Big Data? 

"Big Data" has become a common term and topic of discussion throughout the world. A 

glance at any ne"~ outlet will likely fi d a story that describes some facet of the Big Data 

phenomenon. 

Broadly speaking. Big Data refers to the collection of extremely large data sets that may be 

analyzed U5ing advanced computational methods to reveal trends, pattems, and association~ 

Big Data can support numerous uses. from search algorithms to lnsurTech. The defin tion of 

Big Data generally includes the "5 V's": 

l'olume Large amounts of data are collected and require processing. 

l'elodty Data is available and must be processed at lightning speed, frequently 

installlaneously. 

l'ariety The data being used comes in different forms. 

l'eracity The reliability ofthe data is not uniform. 

l'alu~ The data being extracted must be usable or be able to be monetized. 
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Big Data is not only about data. New, ad1~nced tools are av•ilable that enable Big Data to 

be processed and utilized in"")~ that were not previously possible. These tools include 

data handling capabilities and computational te<:hniques such as predicli1·e anal)1ics and 

advanced algorithms that havt signifi antly ncr~d data spted and storage capacity. 

With the rapid ad1~nres in the 3\~ilability of data and the de..-elopment and proliferation of 

ad\ "anced data ana1)1ics techniques, the insurance industry's interest in Big Data analj1ics 

capabilities has grown commensurately.lnsurTech is the use of recent technology to bring 

effici cies and innovation to the insurance induslr)'· It has led to new products. new 

distribution channels. new risks for insurance companies, and changes to clain1s handling 

methods. It also can lead to greater emphasis on market conduct examinations. potential 

jurisdictional arbitrage. and a more complex regulatory en,~ronment As the utilization of 

Big Data becomes a potential disruptor for the insurance industry, the need for professionals 

who are bound by a code of conduct, adhere to standards of practice and qualifi ation, and 

subject to counseling and discipline ifthey fail to do so, will become more apparent 

The American Academy of Actuaries' Role 

The focus of the American Academy of Actuaries regarding Big Data has been and will 

continue to be around the concepts of professionalism and public policy. From a public 

policy standpoint, the Academj•continues to work with regulatory bodies on how these 

complex issues impact the public through the regulation of insurance and go1•ernance 

of retirement S)~tem~ The American Academy of Actuaries continues to work 11~th 

polic)1nakers and regulators to address and refi e regulatory frameworks in which Big Data 

work may appropriately be governed. 

From the perspe<;tiw of the U.S. actuarial profession, the pillars of actuarial 

professionalism-the Code of Professional Conduct, achtarial standards of practice, and U.S. 

Qua.lifl ation Standards- provide a framework for actwies to perform actuarial services 

related to Big Data. 
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Data Analytics Techniques and Methodologies 

With regard to advanced data anal)1i<:li techniques for Big Data, four types exist: 

Descriptive; What happened! 

Diagnostic: Why did it happen~ 

Predictive: What will happen? 

Prescriptive: What should I do? 

Most insurers have a long history of performing descriptive and diagnostic anal)1i<li. 

Included in diagnostic ana1)1ics are traditional statistical inference techniques that seek 

to characterize the relalionships between variables or elements. Recently, there has 

been a signifi ant increase in the use of predicti1~ anal)1ics that differs from traditional 

inferential statistics in that it is not concerned ,,;lh proving the "why" behind what's dril•ing 

a relationship but only with whether variables help predict a given outcome objective. 

Determining the optimal action to take considering these anal)1ics is the function of 

prescripti••e anal)1ics. 

Descriptive data analysis and feature extraction/selection, as well as data ,;sualization, 

use sophisticated mathematical tool~ including principal component analysis, ridge and 

lasso regression~ and clustering algorithm~ Understanding the data and the relationships 

between variables is of utmost importance before engaging with the models designed to 

predict. Visualization tools such as box·plots, histograms. scatter diagrams, and scatter 

matrix are used for this task. 

When using these techniques, actuaries need to consider that t~ not l~>-a)~ possible 

to de.·elop a precise and defin live formula where complex human beha,;or is im•olved. 

Accordingly, actuaries need techniques in addition to predictive analytics to signi!l antly 

increase t~eir understanding of anticipated beha~• oror events and support their strategies 

and decisions. Th s becomes a professionalism issue for actuaries. 
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Regulatory Considerations 

Benefits and hallenge~ to Insurers, Regulators, and Consumers 

Despite its polenlial,lhere are a number of concerns regarding Big Dala !hal impact insurers, 

regulators:, and consumers. 

Insurers 

The use of predictive anal)1ics can lead to a better understanding of risk than traditional 

methods. New sources of data not only increase dimensionality of data dramatically, but also 

allow for the use of more direct indicators ofindil'idual risk. New methodologies allow for 

a potentially better understanding of risk drivers and relationships between them, as well as 

detecting potential fraud. The benefit of a better understanding of risk is protection against 

adverse selection and improved reserve adequacy, such as with health care models that can 

be used to more accurately predict utilization of health care sen~ces. 

Potential drawbacks of new insurance models driven by predictive analytics include 

disruptions the fundamental pricing principles of the industry, such as the collapse of the 

law of large numbers, disruptions in risk peaks and subsequent difficulty in assessing shortterm risk, and premium inadequacy resulting from both new pricing models and substantial 

upfront build costs. 

Regulators 

Regulators may benefit from better adl'ance knowledge of outcomes and could apply some 

predictiv~ anal)1ics techniques directly to their review processes. Potential benefits for 

regulators: include the enabling of a more streamlined process for approval of pricing and 

rate filings as well as scanning of annual statement filings to detect pre,,ously unknown 

patterns. Regulators can also use predictive anal)1ics to detect fraud. 

Re .. ie"'ng predictive anal)1ics can be a challenge to regulators given the amount of data 

used to del'elop a model the complexity of the techniques. and limited regulatory resources. 

Regulators also may ha,·e difficulty explaining complex models to consumers and other 

interested parties who are tl)ing to understand the impact of the models on insurance rates. 

The NAIC Big Data (EX) Working Group is proposing additional support for regulators for 

reviewing new models that contain predicti\•e analytics capabilities. 
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Consurn~rs 

Anal)1ics em lead to more competition and more competition can lead to more options 

for consumers. Predict ire analytics can result in quicker e(~ions on underwriting. where 

allowable, because of the use of external data. Claim settlement (all also be auelmted tiling 

pmlictiv~ anal)1ics. Anal)1ics also can result in better offerings by insurers to policyholders 

from the use of external data that can help inform decisions regarding better fit of COI"<rage. 

The main challenge to consumers is lack of transparency: tl)ing to understand the data 

and anal)1ics being used to determine their eligibility for products and the price they are 

being charged It may not be clear to the consumer how they are being underwritten or 

what beha1•iors they can modi~· or steps they can take to get a better rate. A potential issue 

with pricing based on predict ire anal)1ics is that it can lead to more granular pricing, which 

benefits some consumers but not others. Th s broader distributed range of prices could be 

perceil-ed as unfair. Privaq• issues are also a concern for consumers because of a lack of 

transparency regarding how data is collwed and used. 

Existing Regulatory Framework 

The legal and regulatOr)' requirements that potentially govern the use of Big Data by insurers 

at the state, federal, and international!el'els fall into two categories: I) those designed to 

protect consumers in general; and 2) those intended to prohibit discrimination against 

certain prote(ted classes ofindil•iduals. 

Emerging Regulatory Developments 

NAIC Activity (NAIC Big Data (EX) Working Group) 

Adl'ances in statistical modeling te(hniques and eroMng sources of data are challenging 

existing regulatory processes. Methods, such as those used to calculate premiums. are more 

complex than ~er before. Current algorithms and models are not as easy to under>1and 

and follow as traditional algorithms. In addition, 1\'ith the exploding availability of data, 

including consumer data, insurers are utilizing types of data not pre iou~y incorporated 

into advanced modeling te(hniques. Moreover, for many aspects of the insurance business, 

companies differ in methods and approaches employed and in their documentation and 

explanation of such methods and approaches. 
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The complexity and evolution of the methods and approaches used by insurers is threatening 

to outpace the rate at which regulators can educate themselves on these new methods and 

approaches. To address these issues, the NAIC has increased training opportunities, such 

as the predicti1-e model training that was organized by the Ameritan Academy of Actuarits 

at the 2017 Summer NAIC Insurance Summit, and information-sharing forums to address 

current gaps in knowledge. The NAIC also formed a Big Data Working (EX) Group (the Big 

DataWG). 

RegulatO!')' Sandboxes 

"Regulatory sandboxes" ha1•e recently retti1•ed signifi ant attention from regulators, 

companies, and start-upsaclil'e in the r. ancial ser~iccs industries. Although the concept 

can take a ~~riel)' of forms. a regulatory sandbox is generally a discrete regulatory 

em1ronment designed to encourage innOI'lltion in a regulated industry. Depending on the 

context, a sandbox might function primarily as a forum for encouraging earlier and more 

frequent engagement between innOI'<ltOrs and regulators, without necessaril)' allowing 

for wai1·ers of e:tisting law. Alternatively, a sandbox can relax regulator)' requirements, 

elfecti\'ely creating an alternative, less restricti,•e regulatory regime for proposed innovations. 

Given the regulatory issues inl'olved, it is not difficult to imagine this concept being applied 

to insurance companies in the context of Big Data. 

Professionalism 

Actuaries hal'e professional obligations to uphold the reputation of the actuarial profession 

and fullill the profession's responsibility to the public in the emerging area of Big Data. An 

important part of this responsibility is to comply with the law. In many situations, actuaries 

a.lso hal'e unique insights into the resu.lts and implications of the use of Big Data and must 

be 11illing and capable to explain such insights. where appropriate, to the ker stakeholders 

of the II'Orl<, such as regulators. consumers, compan)' management, auditors, etc. The value 

of the 3Ctuaries' II'Ork is enh3nced through adherence to the Code of Profession3l Conducl, 

actuarial standards of practice, and U.S. Qualifi at ion Standards. A key attribute of the 

applicable standards is the requirement for actuaries to pro\ide explanations and rationales 

for their conclusion~ 

Professional judgment from actuaries is critical in the utUization of Big Data in acmaries' 

work. Actuaries pro1•ide added ,~Jue to Big Data work in their ability to "connect the dots• 

through a deep understanding of the subject matter. In exercising professional judgment, 

it is important for actuaries to be cognizant of the fact that ll'ithout performing proper 
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anal)"SeSorvalidation, the results of Big Data can be misleading. A combination of a good 

understanding ofthecontext in which the data 11~S obtained and avoidance of unthoughtful 

adherence to the results of a model can aid in better Big Data outcomes. 

There are man)' professionalism issues that may be encountered when working with Big 

Data and predictive analytics. The work of actuaries is governed by the Code of Professional 

Conduct (Code) and must comply with applicable actuarial standards of prnctice (ASOPs). 

The Code and ASOPs prO\;de a framework for dealing with issues of professionalism that 

might arise in the work of actuaries. While actuaries ha1·e traditionally dealt ll;th large 

volumes of data and a variety of modeling techniques, Big Data may pose new challenges 

that differ from those that actuaries encountered in the past. In addition, actuaries 

historically have built anal)'Ses and models based on traditional inferential statistical 

methods (descriptive and diagnosticanalytics); however, predictive anal)1ics techniques 

offer unique and different challenges to consider. 

Role of the Actuary 

In many applications of Big Data in businesses in which actuaries are employed. 

multidisciplinary teams are utilized to effici tlyand effectil·dy complete the project. The 

teams are commonly composed of statisticians. computer scientists, data scientists, and 

actuaries. Actuaries on these teams ma)' be thought of as the subject matter experts. But 

actuaries may be positioned to be the quarteroad:s of the Big Data teams. With the 

proper background, an actuary can understand and direct the work of the Big Data 

multidisciplinary team based on their professionalism requirements and subject matter 

expertise. 

As the evolution of Big Data continues in the areas of practice in which actuaries pr01•ide 

services, the professionalism and technical expertise pro1•ided b)• actuaries art essential 

elements upon which the public and regulators can place reliance. The professionalism 

requirements of actuaries pro,;de guidance for the proper application and disclosure of 

Big Data assumptions and methodologies. Tht)' require actuaries to adhere to the high 

standards of conduct, practice, and qualifi ations of the actuarial profession, thereb)• 

supportin,g the actuarial profession in fulfilling its responsibility to the public. 
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Section I 

Current and Emerging Practices 

Remarkable advances have been made over the past decade in the use of Big Data, including the 

Internet ofThings, machine learning, cognitive computing. and artificial in el/igence, and the fie I 

continues to evolve. These advances have led to the development of a multi·billion·dollar industry 

referred to as lnsurTech, the innovative use of technology in insurance, which is expected to have a 

significa t impact on insurance and the work that actuaries perform. 

Section I of this monograph provides examples of current and emerging applications of 

Big Data in the various practice areas of actuarial work. While the use of Big Data in the 

property and casualty insurance area is more developed than in some of the other areas 

of actuarial practice, signifo ant ad1<ances haw been made in recent years in the use of 

Big Data in health and life insurance. Similar advances in the pension area have not been 

as noticeable. Howewr, it can be expected that over the next decade, all areas of actuarial 

practice ll'ill be signifi antly impacted by the use of Big Data. 

What Is Big Data? 

"Big Data" has become a common term and topic of discussion throughout the world. A 

glance at any ne11~ outlet will likely fo d a story that describes some fucet of the Big Data 

phenomenon. 

Broadly speaking. Big Data refers to the collection of extremely large data sets that may be 

anal)ud using advanced computational methods to ct~'fal trends, patterns, and associations. 
136 


91818073.eps

Big Data can support numerous uses, from search algorithms to lnsurTech. The defin tion of 

Big Data generally includes the "5 V'S': 

Vol11me Large amounts of data are collected and require processing. 

Velocity Data is available and must be pJ'O(essM at lightning speed, fr~uently 

instantaneously. 

Variety The data being used comes in different forms. 

Veracity The reliability of the data is not uniform. 

Vnl11e The data being extracted must be usable or be able to be monetized. 

Big Data is not only about data. New,ad,'llnced tools are available that enable Big Data to 

be processed and utilized in ""l~ that were not previously possible. These tools include 

data handling capabilities and computational techniques such as predicfi,·e anal)1ics and 

ad\'llnced algorithms that have signifi antly increased data speed and storage capacity. The 

value of the data in the absence of these tools might be orders of magnitude less than it is 

currently. Within the context of this monograph, Big Data refers to both the data and the 

associated anal)1ics applied to the data. 

ll'tth the rapid advances in the availability of data and the development and proliferation of 

ad\'3nced data anal)1ics techniques, the insurance industry's interest in Big Data ana~1ics 

capabilities has grown commensurately.lnsurTech is the use of recent technology to bring 

eflki des and innovation to the insurance industrj•.lt has led to new products, new 

distribution channels. new risks for insurance companies, and changes to claims handling 

methods. It also can lead to greater emphasis on market conduct examinations, potential 

jurisdictional arbitrage, and a more complex regulatory en,~ronment. lnsurTech is discussed 

in depth in Appendix I of this monograph. As the utilization of Big Data becomes a 

potential disruptor for the insurance industry, the need for professionals who are bound 

by a code of conduct, standards of practice, and qualifi ation standards will become more 

apparent. 

Th s monograph describes some uses of Big Data and predictire analytics in the work of 

insurance and pension actuaries. The primary focus is on the regulatory and professionalism 

aspects and the roles of actuaries who work with Big Data. 
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The American Academy of Actuaries' Role 

The focus of the American Academy of Actuaries regarding Big Data has been and will 

continue to be around the concepts of professionalism and public policy. From a public 

policr standpoint, the Academy continues to work with regulator)' bodies on how these 

complex issues impact the public through the regulation of insurance and governance 

of retirement systems. The American Academy of Actuaries continues to work ";th 

polic)1nakers and regulators to address and refi e regulatoq• frameworks in which Big Data 

work may appropriately be governed. 

From the perspective of the U.S. actuarial profession, the pillars of actuarial 

professionalism- the Code of Professional Conduct, actuarial standards of practice, and U.S. 

Qualifi at·ion Standards-provide a framework for ac~m es to perform actuarial services 

related to Big Data. 

Data Analytics Techniques and Methodologies 

10 

With regard to advanced data anal)1ics techniques for Big Data, four types exist: 

Descriptive: What happened? 

Diag11ostic: Why did it happen? 

Predictive: What will happen' 

Prescriptive: What should I do? 

Most insurers have a long history of performing descriptive and diagno>tic anal)1ics. 

Included in diagnostic analytics are traditional statistical inference techniques that seek 

to charact<rize the relationships between variables or elements. Recently, there has 

been a signi.fi ant increase in the use of predictive anal)1ics that differs from traditional 

inferential statistics in that it is not concerned with proving the "why" behind what's driving 

a relationship but onl)• with whether variables help predict a given outcome objectil•e. 

Determining the optimal action to take considering these analytics is the function of 

prescriptive anal)1ics. Th s monograph primarily discusses predictive analytics. 
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Any data analytics project starts 11ith data, and a 1~riety of techniques are used to reconcile, 

scrub, and pre-process that data. A common rule of thumb for most predicti1•e ana1)1ics 

projects is that 80 percent of the time is devoted to ensuring data quality, understanding 

the data and relationships within, and extracting/selwing features (or predictors).' Many 

techniques exist to ensure quality of modeling data, including reconciliation using a 

range of data sources, dealing with data issues such as missing values, and reducing data 

dimensionality, if necessary. 

Descriptil'e data analysis and feature extraction/stlection, as well as data 1isualization, use 

sophisticated mathematical tools, including principal component analysis, ridge and lasso 

regressions. and clustering algorithms. Undmtanding the data and the relationships among 

variables is of utmost importance before engaging with the models designed to predict. 

Visualization tools such as box-plots, histograms, scatter diagrams, and scatter matrix are 

used for this task. 

Most data anal)1ics techniques and tools use various forms of optimization and stat~lical 

algorithms. as well as machine learning methods. ~1odel inferences are then e1~luated and 

analyzed. The tasks of implementation and documentation, as well as the purpose of the 

predictiv~or data anal)1ics model, provide for additional considerations for model stlection, 

including the lml of transparenC)', ease of use, update, and feedback loop execution. 

Some models are harder to interpret than others, and precise formulas and causal 

relationships are not al11~ys discernable. To this end, other techniques are typically used to 

supplement and explain models' results, such as expert opinions, customer questionnaires, 

existing relevant industry research, and research from other industries. 

Commonly used modeling techniques include the following: 

Generalized linear modeling 

linear discriminant analysis 

Time series anal)~is 

Sun•i,>al anal)%is 

Association algorithm 

Sequence anal)~is 

• Clusteringalgorithms 

Classifo at ion algorithms 

Neural network anal)~is 

Dec~ion trees 

Random forests 

Gradient boosted machines 

Support vector machines 

Na'il"e Bayes analysis 

Bayesian estimation 

Ensemble models 

Text mining 

Beha,;oral economics models 

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When usi~g these techniques, actuaries need to consider that it is not always possible 

to de.·elop a precise and defin live formula where complex human behavior is involved. 

Acoordingly, actuaries need techniques in addition to predictive analytics to signifi anti)• 

inmase ~e r understanding of anti~ipated behavior or events and support their strategies 

and decis on~ Th s becomes a professionalism issue for actuaries. See Section Ill on 

professionalism for more information. 

Application of Predictive Analytics 

12 

The follo"•ing are examples, by function, of how insurers use predictive anal)1ics: 

Marketing; Insurers use predictive anal)1ics to market to consumer~ Companies can 

observe consumer beha,•ior in a variety of forms and build targeted advertisements to 

appeal to customers. Companies can gather information about consumers using cookies 

or other mechanism~ Companies also can build "propensity to buy models to target 

consumm who are more likely to make a purchase.lhese acti,;tiescan reduce marketing 

costs, leading to overall cost reductions or the reallocation of marketing funds for other 

pur~ 

Engagement; After an insurance purchase has been made, companies engage with targeted 

customers using customer-specific ethods, as research shows that an engagement focus by 

the company leads to more future sales and better retention as compared to a transactional 

focu~ These targeted customers and engagement methods are selected using predicti,•e 

analytics. Customer value propositions should improve, as should internal performance 

managem~nt. Howem, companies should recognize that this increased engagement can 

off et some or all the cost reductions achieved through more effici t marketing. 

Underwriting; Predictive anal)1ics can improve underwriting processes where this is 

permitted by regulation. Streamlined application processes and shorter unden ~it ng 

wait times can improve company placement rates. The elimination of costly underwriting 

methods, such as the use of bodily Huids in life insurance, can signifi antly reduce expenses. 

These enhanced riskassessment processes can then reduce the cost of the polic)• through an 

improved ratio between mortality and expen~ 
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Product Development: Insurance companies can use predictive analytics techniques to fi d 

new markets and design new products for it. Companies can offer a better-fitting product 

line to the market by analyzing prior history data on insurance, driving history, health 

r~ords, and lifestyl~. 

Claims and Reserving: Claims management for fraud detection is another area where 

predictive anal)1ics can be useful, as are processeffici cy, cost reduction, fast·tracking, and 

principle-l>ased reserves (PBR) assumption-setting for life insurance. 

Decision-Making Analytics: Predictive analytics can be used to mimic human decisionmaking. to produce decision-making rules that are better than those used pre,•iously, and to 

map potential outcomes more quickly and with more accuracy. Each of these can provide 

major benefits, but also come with certain constraints. For example, the matching of human 

decision-making means that human biases will be preserved. Producing decision-making 

rules requires an investment of signifi ant effort, and the mapping of potential outcomes 

requires v:ast quantities of data. 

Behavior Anal)1ics: Acquiring a comprehensive understanding of customer beha,iors and 

needs is important so that insurers can anticipate future behaviors, offer relevant product~ 

and appropriately segment their business. For example, anal)1ics systems can spot if a 

customer is likely to lapse by detecting a large number of calls to a customer senice center. 

Customer Satisfaction and Upselling: In addition to pro,iding predictions about when a 

customer is likely to lapse, gaining customer insight with predictive anal)1ics also can help 

insurers to develop trusted relationships and engage customers with accurate information. 

As a result, i.nsurerscan be more successful in achieving posit ire outcomes such as solving 

customer problems in real time and upselling and cross· selling products. 

Targeted Marketing: Developing a more complete understanding of customer beha,•ior 

allo\\~ inrurers to become more effici 1 in targeting products and sm~ces. Th scan be 

accomplished by offering perwnalized set\ices, contacting the customer for special offers 

when they are likely to lapse, or offering a package for a family life C)'cle event. 

13 
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Practice-Area-Specific pplications 

life I nsura nee 

14 

Big Data and predicti1·e ana1)1ics are used in each of the four actuarial practice area~ 

Some predictil•e ana1)1ics offerings for accelerated undem·riting develop scores from 

biometric information. Others look at predictors less commonly used in traditional 

underwriting, such as public records, social media activity, motor vehicle reports, credit 

information, and wearable devic~ Examples of predictive models that are used include: 

triaging indi,;dual requirements (e.g., determining ifblood is needed), best classifi at ion 

model, multiple classill alion model, and a true mortality prediction model. 

For life insurance, the application of predictive ana~1ics to actuarial assumption 

developm~nt, such as morrality or lapses. sometimes starts 11ith term insurance and then 

is expanded to permanent coverages. Predictive ana1)1ics techniques are applied to term 

insurance to improve term com·ersion rates (the rates at which customers convert their 

term policy into a whole life policy). For annuities, predictil•e anal)1ics is used to develop 

morlality<~ssumptions, improve longevil)• analyses, and to model policyholder beha~• or 

under guaranteed riders such as Guaranteed Minimum Withdrawal Benefits (GMWB). 

GMWBs and Guaranteed Minimum Income Benefits (GMIB) are ~ro common product 

features where predicti1·e analytics are used in the selling of poliq1l01der behavior 

assumption~ Specifi ally, companies are using predictive analytics to model how 

policyholders exercise guaranteed benefit option~ For example, policyholders can wail 

longer than the initial waiting period to gain additional guarantees. Predictiveanalytics will 

examine ~uch things as policy size, funding le1•el, asset allocations, percent of guaranteed 

amount withdrawn, and prior 11ithdra11~l history to predict the likelihood of a future 

withdrawal. 

Companies Clln uti.li.re apps and wearables that enable the proactive tracking of their 

customers, whi.le helping the customers to manage their health. For example, a company 

may make post-issue changes in underwriting classifi at ion based on health-related data 

from wearabl~ 
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Property and Casualty Insurance 

Historically, property and casually insurance companies have utilized predictive anal)1ics 

for purposes such as pricing. especially personal lines; underwriting; claims management; 

quoting: fraud dete.:tionlprevention: premium audit: and agent sele.:tion/wention. 

While th~ techniques have been used for many years, more sophisticated and broader 

applications continue to evolve. These advanced methods are often used for rating and 

underwriting, risk management, targeted marketing. behavioral anal)1ics, and product 

development. 

The types<lf anai)'S<S that can be developed, including entire new rating algorithms, new 

classifi ation plans such as territory structures that incorporate geographical elements, or 

scoring algorithms such as insurance scoring. may not look like traditional simple rating 

steps. These comple.~ algorithms and models may be difficult for a reviewer to follow and 

understand. As a result, such algorithms and models, if used for rating and underwriting 

purposes, may attract additional scrutiny from regulators as the regulators seek to 

understand the new and emerging practices. 

Anal)1ics also can assist risk management efforts by providing feedback on unsafe actions 

or conditions and generating alerts for potential fault or failure situations. Ihe "Internet of 

Thi gs• enables sensors to pro1ode continuous monitoring and feedback. Telematics can 

provide information on driving actions or conditions that may be used to pro1ode discounts 

for safe drivers. 

Health Insurance 

An important application in health care modeling is the task of risk adjustment, utilizing 

risk scoring models that can be both predictive and descriptive. Risk adjustment in health 

insurance became prevalent in the t990s, before the 11idespread use of predicti1·e modeling. 

The models employed (often referred to as grouper models) were developed using linear 

regression to predict resou.rce utilization in a period from a set of covariates (frequently age, 

sex, and d.iag ose~ . They are referred to as "grouper models" because they group together 

diagnostic International Classifi ation of Disease (ICD)-9 (tS,OOO) or ICD-10 (80,000) 

codes' into a smaller number of hierarchical codes consisting of similar diagnoses. Grouper 

models aT( powerful tools for both risk adjustment and for predictive modeling because 

they signitl antly reduce the dimensionality of predicti1•e modeling ";thout signifi ant loss 

of accuracy. 

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Pensions 

16 

Risk adjustment is widely used as a predictiw anal)1ics tool in reimbursements of many 

government heahh S)'Siems (Mediaue Advantage; Medicaid; ACA exchanges) and private 

insurance contract~ in reimbursement for pro1•iders taking on risk under capitation and 

r~k shnring arrang~mtnts, and for determining tht eff~tiven~ of providers in building 

high-performance networh 

Early applications of predictive modeling in underwriting and "cast fi ding" (identifi at ion 

of high-risk patients for management) used grouper models be<:aust these were frequently 

available (being required by insurers for risk adjustment). Howe1·er, in the early 2000s 

purpose-built predictive models began to proliferate, ofien for case fi d<ng purposes for 

patients 11·ith specific c nditions (cardiovascular distast, diabetes, mental illn~ orthopedic, 

etc.), as well as specific roblem areas such as hospita.l readmissions follo11ing the Centers 

for Medicare and ~iedicaid Sen;ces' (CMS) introduction of penalties for excessi1•e 

readmissions. Currently, predictive anal)1ics is widely used in case fi ding for medical 

management programs. 

All health insurers; many pro,;der groups; many hospitals, pharmacies, and pharmacy 

benefit management (PBM) companies; and all medical managementcompaniesemploy 

predictive modeling in some form or another to identify high-<:ost or high-risk patients. 

Predictive modeling was used, prior to the passing of the Affordable Care Act, to predict 

high-cost members of insurance pools for underwriting, rnting, and pricing. It may still be 

ustd in ra1ing and pricing for blocks of business but not at the indil~duallevel. Its use is 

often limited for undem'Titing, although it mar be used to price an entire group under large 

group lines ofbusiness and/or association business in some states. 

1lte use of Big Data and data ana1)1ics in the pension area current])' is limited, but its use 

is gro11ing with the emergence of new roles for pension actuaries. One notable use is 

mortaUty improvement assumptions for pension valuations. These assumptions often are 

derived via extensi1•e mortality data analysi~ grnduation to smooth out random noist, trend 

identi6 ation, and pattern extrapolation. Th s also is an example of data anal)1ics used to set 

actuaria.l assumption~ 

Pension actuaries hare begun to analrze and model embedded options in emplorer benefit 

programs and potentially suboptimal choices made b)' plan participants. Th sis an emerging 

area for the use of predictive ana1)1ics in pension practice to set appropriate participant 

beha~;or assumptions. A related emerging use of predictive anal)1ics is in the fi Ids of 

pension risk transfer and Ionge~;!)' risk management 
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Considerations in the Use of Predictive Analytics 

Business Considerations 

Before dn·cloping, implementing, and emplO)~ng predie~il e anal)1ic.s and other Big Data 

analytics models, companies need to carefully assess what their objectil•es are, what barrie~ 

they are !&ely to face, and how best to proceed. Barriers that need to be overcome include 

how to bt<ild the necessary infrastructure and synchronize it with existing infrastructure. 

Other issues that also need to be addressed include obtaining the exptrtise to effecti,~ly use 

predictive anal)1ics, data a,·ailability, potentiallyconfli ting priorities, and cost. 

Because predictire analytics may im·olve multiple business functions and objectives, 

companies may wish to consider the benefits of dereloping a comprehensire and integrated 

strategy to support their efforts and a cost-effective means to test their strategy. Companies 

will need to de,·elop robust sets of data principles to govern enterprise· wide handling of data. 

For example, it can be extremely challenging to harmonize, cleanse, and certi~· data from 

multiple internal (often legacy) and external systems. Th sis a critical step, especially when 

using data to deril·e assumptions used in fi ancial reporting or for key company decision· 

making. 

As companies aggregate data into data warehouses (often structured, more traditional data 

for reporting) a11d data lakes (oft.n unstructured data combined with structured data), 

investments in data infrastructure are needed. Companies also will need to consider what 

else may change because of the use of predicti,·e anal)1ics. For example, if underwriting 

were to be streamlined, would changes to the application process be needed' 

The evaluation of predictive models, important in actuarial professionali•m, typicall)' 

includes retrosptetive studies to measure model effectirenessand to establish criteria 

for "·hen the new methods are used alone or in combination "ith old methods. Scenario 

analysis can aid in the determination of criteria that best align with companies' goals. 

Sensitil~ty testscM bt used to assist i~ unde~tMdi~gvariations in contributing variables 

and how interactions among those variables impact model outcomes. 

After imp~ementation, the model must continue to be monitored to measure its continued 

fit to new <lata. Doo the model meet the objectives? Are the emerging results consistent 

with the j>TOjoctions based on the historical data on which the model was built' Is there all)' 

change in the strategy that ma)' require the model to be adjusted' Must traditional methods 

be maintained to supplement someorall the new methods? 

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Companies also will n«d to address legal, regulatory, professional, ethical, and pril~cy 

concerns. These considerations typically are factored in before models are built, but, at a 

minimum, before implementation. Regulators may ha,·e queslions about how predictive 

a.nal)1ics·generated assumptions wm demonstrated to be credible. PrMicti\'e analytics may 

be found lo gil•e more effici tly generated, evidence-based assumplions than traditional 

methods. 

Model Development Considerations 

There are man)• considerations in de\'eloping a prMictive analytics modeL Man)' of the 

considerations also apply when using more traditional ana1)1ical methods. The questions 

that might be asked include: 

Is the model appropriate for the situation for which it is being used? 

• What are the evaluation criteria used to assess accuraq•, effectiveness, and statistical 

appropriateness of the model? 

Is the data used in the anal)1ical method acceptable to regulators? Some variables may 

not be allowed by current regulation. 

Is the data ''erifiable and credible? 

Is there a way for the policyholder to challenge and correct values' 

Is the relationship between predictor variables and the target variable intuiti\'e? While 

causation is not a requirement of the actuarial standards regarding classifi at ion plans 

(there are general!)• four classifi at ions for life insurance: preferred plus. preferred, 

standard plus, and standard), an attempt is generally made to explain the rationale for 

the relation~lip. 

Is the new ~riab e replacing a previously used variable? When a new \<lriable 

is replacing a historical variable, an explanation as to why this replacement is an 

imprO\•ement is generally developed. An example of such an improrement is the use of 

actual drhong patterns from telematics devices replacing variables like age and gender. 

Clearlr. the use of the actual driving experience is a bener match to the expected claims 

than the historical rating variables of age and gender that have acted as proxies for 

driving beha,1or. 

Could the data variable be considered a prO>)' for a disallowed variable? Insurers are 

not permitted to use certain variables, such as race and nationalil)•. Ho11·erer, thm is a 

possibilil)• that some other \<lfiables might be proxies for disallowed variables. Caution 

should be exercised to avoid using variables that may be considered as proxies for data 

elements not pennined, although determination of proxy status may not be feasible. 

How are missing values handled in the preprocessing stage of the data and/or in the 

modding? 
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What steps hal'e been taken to ensure quality of the modeling data1 

How frequently will the values be refreshed? From an implementation standpoint, the 

modeler must decide on how frequently the model will be measured against new data to 

determine if the model needs to be "rtfreshtd" or "rebuilt.• Refrtshing a model ini'Ol\~ 

updating the model with parameter estimates that result from running the algorithm 

on new data. A complete rebuilding of the model may become necessarr if there are 

major changes in company underwriting. risk, or if emoronmental and behavior factors 

impact the level of loss experience. 

Data Sources 

life Insurance 

The insurance industry has long relied on multiple sources of data. Emerging sources of data 

utilized iB Big Data often are external to a company or can be internal data that pre,oousl)• 

was not available or difficult toextrnct. In legac)' systems, for example, inconsistent sources 

and historical infrnstructure may have created barriers to utilizing data. The explosion of 

structured and unstructured data 0\'ailability, computing power, and new methods of data 

extraction provide for new opponunities regarding data collection. 

Many observers believe that social media and consumer data may hold promise, but their 

lack of structure and the signifi ant prevalence of missing data make them more difficult to 

process. 

Specific ata sources by area of practice are summarized below. In many instances, data 

sources are common among multiple areas of prnctice. 

Traditional data sources used for life insurance include the following: 

Experience study data, much of it coming from companies' internal administrative 

systems, including the policyholder's age, gender, account value, face amount, and other 

kq,ustomer and poli')' data. Poli,yholder use of electil'e benefits, death, withdrawal, 

and surrendernapse data are also included in this category. 

Underwriting data that includes the policy application, attending ph)~ician statements, 

bodil)• Ruids test result~ Medical Information Bureau (MIB) information. and motor 

l'ehicle reports (MVRs). 

Emerging data sources used for life insurance include the following: 

Data capn.red by sales and marketing to target customer segments, as well as customers 

within those segments. 

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Electronic inspection reports for accelerated underwriting (AU) progroms (i.e., 

underwriting "ithout ifl\'asive testing such as fluids and exams). 

Other emerging data for unden<riting includes public records such as bankruptcy 

filings and criminal hiltory, demogr.tphic data, genetic information, mdit scores, 

electronic medical records (EMRs), prescription histories, and lifest)1e and belm;orol 

data captured from wearobles like Fitbit devices. Some of these are used for pre· policy· 

issue anal)1ics, while others are used for ongoing monitoring. Some are used as part of 

formal underwriting and others highlight the need for additional anal)~i~ 

Social media interactions including website clickstreams used both to verify 

undm1~iting data and as a lead·generation tool. For example, undem~iters may check 

social media outlets, such as Facebook, lnstagram, and Snapchat, for signs of nicotine 

use and other health·related information. 

Facial aJlal)1ics and facial visuals to assist with identi~ing elements that were previous 

difficult to ve i~. e.g., smoking status. 

Income and wealth information for risk dassifi at ion, marketing. and to assist with 

identifying lapse propensity. 

Proptrty and Casualty 

20 

The application of anal)1ics for predicti1·e purposes in the property/casualty (P&C) area 

of praelice has been commonplace for some time and has become an important aspect 

of underwriting. ratemaking. and reserving. The data used for most P&C lines includes 

location and claims loss histor)'• while other data is used specifi ally for the personal or 

commercial lines. 

Traditional sources for P&C insurance include the following: 

For personal and/or commercial auto insurance- age and gender of the drim, type of 

rehicle, miles driren, as weU as DMV information. 

For property insurance-type of construction, fi e protection (e.g., smoke detectors, 

sprinklers), distance to water, and age of roof or utilities. 

For commercial liability insurance-the type of business being insured. 

Emerging data sources for P&C insurance include the follo"·ing: 

For some personal lines models, data sources that refl ct more specific ersonal 

information. Howe\·er, these variables are fi ding d~faror 11ith some regulators. due to 

potential discrimination issues. 

For all lines of insurance, non·insurance information like weather data, crime statistics, 

population densil)\ traffic d sity, and census information that might be predict ire of 

claims. 
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Telematics de1ices in cars that make detailed infonnation about drirer behavior easier 

to obtain. Telematics data has started to be used in rating and underwriting for personal 

and commercial auto. 

For many lines ofP&C business, cellular technology, the Internet ofThi gs and other 

ad1~nced technologie$, and new source; of data like home telematics and social media 

offer new insights into risk. 

Health Insurance 

Pension 

Traditional data sources for health insurance include the following: 

Physician referral information or medical chart information, which can be useful in 

identifying diagnosis codes and other information about a patient. 

Enrollment information, including effect ire dates of coverage. 

Medi ~l claims information, including diagnosis codes. 

Prescription drug claim information to provide additional insight into a patien(s 

condition. 

Laboratory results information for understanding member outcomes. status. and 

morbidity. 

Self-r~ported data, such as from health risk assessments (although possibly not reliable 

because it is self-reported). 

Emerging sources for health insurance include the foUo11ing: 

Dt\ice-reported information, such as from wearable de~ices or home use de1ices. 

Electronic medical records. which are emerging as highly raluable information and 

often are used for risk adjustment supplemental information and audits. Th s data may 

be in a standard format or of an unstructured nature. 

Consumer and social media data, such as web searches. 

Traditional data sources for pensions include the foUo11ing: 

For pension plan design purposes- compan)•·specific, roprietary, and confide tial 

data, such as participant information. 

For projects involring new plan designs, assumption setting, and risk managementcompanr· or client-provided proprietary data on plan participants. 

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22 

For models that use macroeconomic or geographic input- data from the Census 

Bureau and the Department of Labor, data from household surve)~ conducted by 

other gorernment agencies such as the Centers for Disease Control and Prerention, 

longitudinal studies, as well as tax statistics milable from the Internal Revenue Sen ice. 

• Company data on other retirement plans, such as 40 I (k), to be aggregated 11ith 

traditional pension plan data for benefit adequacy anal)~is. 

Emerging data sources for pensions include the following: 

Data available to a company from different benefit programs or from a different part of 

its business. For example, a data warehouse consisting of payroll and human resources 

data, pension administration information (defi ed benefit and defi ed contribution 

plans), and medical, dental, and disability claims can be constructed. Aggregating 

rarious existing data sources allo11~ more patterns and relationships to be found via data 

ana1)1ics. 

Plan participant beha1ior, preferences, and the Jerel of participant satisfaction from 

participant sufl't)'S or pension plan admin~tration data. Pension plan administration 

data provided by record· keepers can include data across different empiO)~l'S, not 

just a company's own employees. Also, beha1ioral economists, who study the impact 

of psrchological, social, cogniti1·e, and other non· rational factors in the economic 

decisions of indiriduals, conduct research to identify factors influencing participant 

choice. The results of this research are useful in identifying attributes to use in predicti1·e 

anal)1ic model~ A company can look for data associated with such attributes from its 

own data 1varehouse or from other data vendors. 

• Consumer data, such as credit scores or consumer purchase patterns, and other forms 

of dig,ital data, such as social media data, background checks, motor vehicle records, or 

facial anal)1ics, for participant beha~ior modeling. 

Mortality data from broader public sources 

For pension r~k·transfer business-age, gender, benefit amounts, and actuarial 

a~umptions a~ociated with the group of plan participants in question. The emerging 

practi.ce is to use other data a''llilable from a company's data 1varehouse or information 

from similar employers (usually pro1ided by pension administrators or benefit plan 

consuhants) to better assess the mortality experience of a group of plan participants, as 

well as the benefit options likely to be elected by plan participants. 
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Section II 

Regulatory Considerations 

Benefits and Challenges o Insurers, Regulators, and Consumers 

Insurers 

Regulators 

Despite iiS potential, there are a number of concerns regarding Big Data that impact insurers, 

regulators; and consumers. 

The use of predictil'e anal)1ics can lead to a better understanding of risk than traditional 

methods. New sources of data not only increase dimensionality of data dramatically, but also 

allow for the use of more direct indicators ofindil•idual risk. New methodologies allow for 

a potentially better understanding of risk drivers and relationships bet11•een them, as well as 

detecting :potential fraud. The benefit of a better understanding of risk i.s protection against 

adverse selection and improl'ed resen·e adequacy, such as with health care models that can 

be used to more accurately predict utilization of health care ser~oces. 

Potential drawbacks of new insurance models driven by predictive ana1)1ics include 

disruptions of the fundamental pricing principles of the industry, such as the collapse of the 

law of large numbers, disruptions in risk peaks aJld subsequent difficult)• in assessing shortterm risk, and premium inadequaq• resulting from both new pricing models and substantial 

upfront build costs. 

Regulators may benefit from better adl'ance knowledge of outcomes and could apply some 

predictive anal)1ics techniques directly to thei.r re,oew processes. Potential benefits for 

regulators include the enabling of a more streamlined process for approval of pricing and 

rate filings as well as scanning of annual statement fi~ngs to detect previously unknown 

patterns. Regulators can also use predictil'e anal)1ics to detect fraud. 

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Consumers 

24 

The main regulatory rnte standard in P&C rnte making is that rates not be "excessive, 

inadequate or unfairly discriminatorY:' Anal)1ics that result in a premium that is more 

closely correlated with the future expected cost could assist regulators in ensuring that this 

standard is met. Additionally, the not unfairly discriminatory standard could be addressed 

with a more granular dassifl ation model that is supported by anal)1ics. Increased solrency 

could result, to the extent that the analytics supporting a classifi ation plan result in a better 

match of price to risk. The use of ana1)1ics may also increase competition resulting in better 

ser\'ice (coverage option~ claims settling. etc.) to poliq•holders. 

Ho"·e,·er, r~k pooling requirements in health insurance may not necessarily result in this 

type of ad-ditional benefit in rate setting due to restrictions in pricing and underwriting 

based on indi,~dual member characteristics. 

Revie\\~ng predictive ana~1icscan be achallenge to regulators gi\-en the amount of data 

used to develop a model. the complexity of the techniques, and limited regulatory resources. 

Regulators also may ha\'e difficulty explaining complex models to consumers and other 

interested parties who are tf)~ng to understand the impact of the models on insurance rates. 

The NA!C's Big Data (EX) Working Group is proposing additional support for regulators for 

revie"ing new models that contain predicti\•e anal)1ics capabilities. 

Anal)1icscan lead to more competition, and more competition can lead to more options 

for consumers. Predict ire analytics can result in quicker dec~ions on unden,•riting. where 

allowable, because of the use of external data. Oaim settlement can also be accelerated using 

predictiveanal}tics. Anal} tics also can result in better olferings by insurers to policyholders 

from the use of external data that can help inform decisions regarding better fit of co1•erage. 

The main <hallenge to consumers is lack of transparency: trying to understand the data 

and anal)1ics being used to determine their eligibility for products and the price they are 

being charged It may not be dear lo the consumer how they are being underwri~en or 

what beha,•iors the)' can mod ~· or steps they can take to get a better rate. A potential issue 

with pricing based on predictive anal)1ics is that it can lead to more grnnular pricing, which 

benefits some consumers but not others. Th s broader distributed range of prices could be 

percei1·ed as unfair. 
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Privacy issues are also a concern for consumers because of a lack oflransparency regarding 

how data is collected and used. Consumers also may object to the use of some data either 

because they do not believe it is related to the cost of pro1•iding insurance, does not fairly 

distinguish risk, or because they do not believe the data isacwrate. For example, the use of 

credit-related data in ratemaking for private passenger auto insurance is an example of data 

to which some consumers have objected, resulting in a variety of treatments from regulators 

ranging from complete prohibition in some states to allo11ing certain credit-related data in 

rating and underwriting in others. 

Existing Regulatory Framework 

The legal and regulatory requirements that potentially gorern the use of Big Data by insurers 

at the state, federal, and international levels fall into two categories: I) those designed to 

protect consumers in general; and 2) those intended to prohibit discrimination against 

certain protected dasses of individuals. 

Gh·en the wide span of potentially applicable requirements, the following is a highlevel Ol'el'\>iew ofthe legal and regulatory landscape. It is not intended to pro,;de a 

compreh~nsive legal analysis of any laws or regulations.' 

Consumer Protection Requirements 

Consumer protection requirements cover a broad span oflawsand regulations designed in 

a variety of areas. These requirements can be di1ided into privacy protections and general 

protections. 

lhe collection and use of personal data by insurers is go1-erned by privacy requirements that 

fall under regulatory review. In general, consumers have control over how their protected 

6 ancial and health information, and other sensitive personal mformation, is sha.red by 

insurers with third parties. In addition, insurers may use consumer reports (as defi ed in 

applicable la11~ and regulations) onl)' for specifi d permissible purposes. The increasing 

variety, velocity, and nati,·e digital formal of available personal consumer data also are 

increasing focus on cybersecurity regulations and their connection to pri1~cy concern~ 

25 
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26 

In terms of general protection~ insurers overall must notify and explain adverse 

underwriting decisions to consumers. In addition, regulations exist that prohibit P&<: 

and health insurers from charging excessive, inadequate, or unfairly discriminatory rates. 

Regulations also exist that prohibit life insurers from unfair rate discrimination between 

indi,•iduals of the same class and equal life expectation .. 

E~amples of potentially rele\~111 consumer protections include: 

The ramm-Leach-Bliley Act (GLBA).' Title VofGLBA includes specific ules 

gover11ing how insurers may share and disclose consumers' personal information, 

including consumer reports and protected health information. The NAIC Pri,~cy 

of Consumer financial and Health Information Model Regulation' implements the 

requirements ofGLBA as they apply to insurers. Specifl ally, insurers are required to 

provide consumers with an annual privacy notice explaining the information collected, 

how such information is used and shared, and how it is protected. Subject to certain 

exceplion~ consumers ha\•e the right to opt out ofha\ong their protected r. ancial 

information shared \lOth unaffiliated third parties and must opt in before their protected 

health information can be shared. 

The air Credit Reporting Act (FCRA).' The FCRA regulates the use and 

dissemi11ation of consumer reports. Users of consumer reports are subject to certain 

requirements under the FCRA, such as notice requirements for ad\•erse actions with 

respect to insurance transactions based upon consumer report information. 

European Union General Data Protection Regulation (EU GDPR).' The EU GDPR 

effective as of May 2018 is intended to simplify the regulatory en,1ronment across 

the EIJ and give morecontrolto consumers over how their personal data is used by 

businesses. Companies go,·erned by the GDPR, including companies based in the EU 

as well as companies collecting/processing data on EU residents, will hare an obligation 

to erase data when customers ask to exercise their "right to be forgotten" and withdraw 

their consent to storing or using their personal data. The GDPR also requires companies 

to obtain explicit consent before collecting personal data. 

51SUS.CU .• q)lctSt4 '~IC Iodll67l 

iiSUS.C~l6Sl. 

S:lttpa11M(EU)2016.'6i"9. 
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NAIC Insurance Information and Pri"acy Protection Model Act (the Model PriYacr 

Act).' 1l1e Model Privacy Act governs insure~' collection, use, and disclosure of 

const1mer infonnalion in connection with insurance transactions. Among other things, 

it provide5 acce55 to ptr!Onal information and the consumer's right to veri~· and correct 

such on.formation. The Model Privacy Act also requires insure~ to provide consumers 

with notice of the reasons for an adverse underwriting decision (or notice that such 

reasons can be requested). 

Rate Regulation. In the P&C space, state insurance laws and regulations ensure that 

premium rates-which can be developed using sereral different data sources-are not 

excessive, inadequate, or unfairly discriminatory. Additional requirements regarding 

the use and review of predictive models in detemtining rates vary widely by state and 

context. For example, certain states require P&C insurers to file predictive models used 

to determine premium rates, rnting classes, etc." In addition, state and federnl rnte 

regulations in health insurnnce also limit the ability to use certain variables for rating, 

particularly in the indil>idual and small group markets. And fi ally, the NAIC Model 

Unfair Trade Practices Act prohibits life insurers from unfair discrimination between 

indh;duals of the same class \11th equal life expectation. 

Cybersecurity Regulation. In early 2018, the New York State Department of Financial 

Sen;ces issued a fi st·in·the·nation regulation sening forth minimum requirements 

for covered entities to address cybersecurity risks. Covered entities must establish 

cybe~uri~· programs that address encryption, access controls, and limitations on data 

retention. 

Anti-Discrimination Requirements 

Anti-discrimination laws are meant to prohibit discrimination with respect to protected 

classes of people. State insurance laws include anli·discrimination requirements, and there 

are se~•ernl federal anti-discrimination laws that could be relevant to insurers' use of Big 

Data. Potentially applicable anti·discrimination requirements include, but are not limited to, 

the following: 

State insurnnce law anti-discrimination requirements: These laws prohibit unfair 

discriminalion.11 

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Discrimination based on sex, marital status. race, religion, and national origin also is 

gener.illy prohibited.11 ln addition, certain state·specific equirements mar apply. 

Federal Laws: 

·Equal Credit Opportunity Act: Th! prohibituny creditor from di!Criminating 

against any applicant based on race, color, religion, national origin, sex, marital 

statu~ or age. Title VII ofthe Ci1il Rights Act prohibits discrimination by corered 

employers based on race, color, rdigion,sex or national origin. Americans with 

Disabilities Act (ADA) extends the coverage of the Ci1il Rights Act oft 964 to 

Americans "ith disabilities. 

·Age Discrimination in Employment Act (ADEA): Th s forbids employment 

discrimination under certain circumstances against anyone at least 40 years of age 

in the United States. 

·Fair Housing Act (FHA): Ths makes it unlawful to refuse to sell, rent to. or 

negotiate with any person because of that person's inclusion in a protected class. 

·Genetic Information Nondiscrimination Act (GINA): Th s prohibits the use of 

genetic information in health insurance and employment. 

Emerging Regulatory Developments 

NAIC Activity (NAIC Big Data (EX) Working Group) 

23 

The e~~luation of insurers' compliance with state law and regulation relies. in large part, on 

the information that is provided to regulators. Th s information can come from various 

sources, including fi ancial statements, fi ancial and market conduct examination~ filings, 

specific "JUests and data calls, or from statistical agencies. 

Advances in statistical modeling techniques and el'ol1ingsources of data are challenging 

existing regulatory processes. Meth~ such as those used to calculate premiums, are more 

complex than ever before. Current algorithms and models are not as easy to understand 

and follow as trnditionalalgorithms.ln addition, 1~ith the exploding availability of data, 

including consumer data, insurers are utilizing types of data not previously incorporated 

into ad,;mced modeling techniques. Moreover, for many aspects of the insurance business, 

companies dilfer in methods and approaches employed and in their documentation and 

explanation of such methods and approaches. 
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The complexity and evolution of the methods and approaches used by insurers is threatening 

to outpace the rate at which regulators can educate themselves on these new methods and 

approaches. Insurance regulators may choose to educate insurance department staff n 

these ne1v techniques or employ external resources er:~ed in techniques to evaluate of these 

new methods. From an insurer perspective, any delay on the review of new methods due 

to expertise limitations could result in reduced speed to market ofinnOI'<ltionsand new 

products, which could create a non-level playing fi ld, allowing some companies to exploit 

regulatory shortfalls. 

To address these issues, the NAIC has increased training opportunities, such as the 

predictive model training that was organized by the American Academy of Actuaries at the 

2017 Sum mer NAIC Insurance Summit, and information-sharing forums to address cumnt 

gaps in knowledge. 

The NAIC also formed a Big Data (EX) Working Group (the Big Data WG). The Big Data 

WG's charges are to: 

"Review current regulatory frameworks used to oversee insurers' use of consumer 

and non-insurance data. If appropriate, recommend modifi at ions to model laws/ 

regulations regarding marketing. rating. under\\Titing and claims, regulation of data 

rendors and brokers, regulatory reporting requirement~ and consumer disclosure 

requirements. 

Propose a mechanism to provide resources and allow states to share resources to 

facilitate slates' ability to conduct technical analysis of, and data collection related to, 

states' review of complex models used by insurm for underwriting. rating, and claims. 

Such mechanism shall respect and in no 1\'<IY limit states' regulatory authority. 

Assess dala needs and required tools for regulators to appropriately monitor the 

marl<etplace and evaluate underwriting. rating, claims, and marketing practices. Th s 

assessment shall include gaining a better understanding of currently available data and 

tools and recommendations for additional data and tools as appropriate. Based upon 

this assessment, propose a means to collect, house, and analyze needed data~" 

Th s Big Data WG recently proposed the exploration of a predictive anal)1ics team staffed 

by the NAIC to provide predictive anal)1ics modeling. insurance, and actuarial expertise to 

the slates. The suggestion is that slate regulators could rely on the expertise of the team to 

assist them in the review of ad ~nced modeling techniques presented in insurance company 

models. The team would not opine on compliance with state la11~ or regulations but would 

sen·e in a technical ad1isory role at the request of a state regulator. 
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Another r«ent proposal by the Big Data IVG proposes the creation of a Predictiw Anal)1ics 

Working Group (PA\I'G). The PAWG would dmlop guidelines and processes to govern 

how stale regulators would work with the team. An example of such a guideline would 

bt a vtrsioning S)~ttm for (Ompany models, which would allow for the identifi at ion of 

company models prevlously submitted for a technical m;ew. The objedive is to have a more 

flex ble and cost-effe<tive resourcingapproac.h for the states, bringing increased technical 

understanding to model rt\;ews for the t\'3luation of state-specific a11~ and regulatory 

compliance. 

Some of t~e concerns raised thus far include whether the NAIC will be able to obtain the 

necessary staff or such a team and the legality of housing such an organization within the 

NAIC; such concerns are currently under review. Beyond staffing and legal concerns. there 

are additional concerns regarding a centralized organization's ability to manage model 

versions. data security, models based on machine learning, and the prote<tion of intellectual 

property. 

Permitted Uses of Big Data 

As regulation of Big Data evolves, defini g what is and is not aii011'3ble-and what 

parameters and restrictions should apply under what circumstances-for insurance 

modeling and other uses of Big Data will be key decisions for legislators and regulators. 

An outstanding question from a regulatory perspective is whether, and to what extent, 

leg~lators and regulators will adopt different approaches with respect to: 

new uses of traditional data elements, such as using new types of models for mortality 

assumptions as opposed to a traditional actuarial act\UJI-to-expected approach; and 

the introduction of new data elements, such as data from online shopping, social media, 

or telematics, into the insurance decision-ma.king process. 

The regulatOr)' issues associated with the use of new data elements are potentially more 

complex, For example, driving telemetry data could include information on the specifi 

roads traveled by an individual and the lime at which they were traveled, which could pose 

issues from a pril'liC)' perspectil>e. 
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Data Ownership, Transparency, and Portability 

As the us~ of new data sources and anal)1ic techniques increases and ero]ves, lawmakers and 

regulators 11ill face difficult issues when crafting rules around how and when data can be 

wned, accessed, and transported. 

Various models for governing the collection and dissemination of consumer data exist in 

different jurisdictions. For example, in the United States, consumers generally h31•e the right 

to opt out of data collection or sharing actility." In contrast, in the EU, consumers generally 

must explicitly opt in before data can be collected or shared. 11 

Examples of potential regulatory questions 11ith regard to data 01111ership, transparency, a.nd 

portabilit)' includ~ 

Are existing privacy protections adequate? 

Should individuals "own" their data? To what degree should individuals ha1·e the right 

to access their own data? Who exactly should be able to access such data? 

Should individua.ls have the right to challenge, amend and/or correct their own data1 

Should there be limits on what can be corrected, e.g., medical diagnostic data? 

Should individuals hm the right to "blur" their data (while also bearing the 

conse-quences of such blurring)? For example, in certain instances individuals can 

choose to limit their smartphone GPS location to a set radius to maintain their privacy. 

However, doing so renders pi7.za delivery and Uber/L)1't equests ineffeclil'e. Th s could 

have an unintended effect as those indi1iduals willing to share more accurate data could 

end up with less expensive insurance coverage and/or enhanced benefits. 

Should indi1•iduals have the right to "transport" their data? Can an indil•idual with 

auto coverage with one insurer take the perwnal data that the insurer has collected to a 

competing insurer to shop for a bener quote? Current pricing is mainly driven by public 

information (accidents/violations), but if driving habits hal'e been monitored, could that 

data be transferred? What are the possible elfects on anti-selection and cost spirals? 

Are there relevant distinctions antong dilferent lines ofinsurance business that 

necessitate or justify different regulatory approaches or treatment1 

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Regulatory Sandboxes 

"Regulatory sandboxes" ha1•e recently receil•ed signifi ant attention from regulators, 

companies, and startups actire in the fi ancial services industries. Although the concept can 

take a variety of form~ a regulatory sandbox is genmlly a discrete regulatory environment 

designed 10 encourage innovation in a regulated industry. Depending on the context, a 

sandbox might function primarily as a forum for encouraging earlier and more frequent 

engagement between innovators and regulators, without necessarily allowing for wail•ers 

of existing Jaw. Alternatively, a sandbox can relax regulatory requireme11ts, effectively 

creating an alternative, less restricti1•e regulatory regime for proposed nnovation~ Giren 

the regulatory issues invoh·ed, it is not difficult to imagine this concept being applied to 

insurance companies in the context of Big Data. 

Several regulators have implemented some form of regulatory sandbox, both in the United 

States and internationally. For exan1ple, in the United States, the Consumer Financial 

Protection Bureau {the CFPB) and the Office f the Comptroller of the Currency {the OCC) 

each has projects designed to encourage innovation. The CFPB launched Project Catalyst in 

2012. Th s project includes dedicated CFPB staff ocused on encouraging innovation that is 

"safe and benefic al" to consumers. In 2016, the OCC announced a new framework designed 

to encourage "responsible innovation." The framework includes the establishment of an 

OCC Office flnnovation with dedicated staff hat will serve as a central point of contact 

for lnsurTech innovators and will conduct outreach and provide technical assistance for 

lnsurTech innovation~ 

In thecoBtext of the U.S. insurance industr)', in 2017 the Illinois Department of Insurance 

proposed legislation that would ha~•e created a new "Innovation Division" within the 

insurance department and granted this division broad authority to support the development 

ofinsurance innovations and assist insurers with compliance." As of the publication of this 

monograph, this legi~ation has not been acted upon. 

A major reinsurance company has proposed a Future Insurance Technology Lab {FITl.ab) 

framewofk to the NAIC. The FITl.ab is intended to sem as "a 'safe space' for open 

communi<ation between industry and regulators surrounding new innovati1·e effort ~ It 

would create a confide tial forum at NAIC meetings during whkh companies could discuss 

and recei1•e feedback on proposed inno1•ations from a working group of state regulator~" 
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There are still a number of open questions around the F!Tlab and the regulatory sandbox 

concept in general, such as how long the "innovation wai\'er" would last or how material the 

innO\'lltion needs to be. 

In the United Kingdom, the Financial Conduct Authority, the primary fi ancia\ product 

and market conduct regulator, launched an inno\•ation project in 20 I 4 and created an 

"lnno\'lltion Hub~ If an inno,·ator demonstrates that it is de\'elopinga real innO\'lltion that 

benefits consume~ it can apply to receive dedicated support and feedback from Innovation 

Hub staff 

Potential Regulatory Disruptions 

In any regu.lated industry, changes in business practices may e\'O[\'e so quickly that 

regulators, and regulation, will need to sprint to keep pace. Big Data is already accelerating 

the pace of change in certain aspects of the insurance business. 

The dt\·elopment of accelerated underwriting (AU) in the life insurance industry- made 

possible in large part by the availability of new data sources and anal)1ic techniques-and 

the associated reserving implications under the NA!C's PBR framework are a useful 

example. Guidance set forth in the initial PBR '~luation manual did not anticipate the use 

of Big Data and the emergence of AU, so it did not address the question of how reserving 

standards should incorporate AU. Regulato~ are working on bridge solutions for 2018 and 

beyond. 

In other instances, it is possible that a regulated entity, or possibly a startup, may follow the 

examples ()fUber and Airbnb and bring a new solution to market irrespecth•e of existing 

regulator)! protocols or the fundamental permissibility of the solution. Th s could create 

unintended regulatory consequences for traditional insurm. 

These mnts could impact the insurance business model \'ia changes in the distribution 

model (e.g., robo-advisers, social media adl·errising. smartphone tie-ins), changes in 

covernges, changes in premium and claim payment practices, and operational risks, among 

othm. Based on experience in the P&C insurance and other industries, some of the 

potentially critical success facto~ for these innovati\'e approaches include the follo"ing: 

Are the offerings \'Oiuntary? 

Do they create dear value for consume~? 

Do the offerings elicit a groundswell of public support? 

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Some con<eil'able examples of potential disruption include the following: 

Offerings may cross regulator)' boundaries, such as a fin Tech" company pr011ding 

long-term insurance coverage or auto insurance rates based on sa11ngs account balances. 

Driverless (ar! may mo1·e regulators to mandate commerdal insur:~nce rather than 

perso~al insurance co1•erage. 

Offerings of"all-in-one" risk packages for a major portion of the life cycle may become 

available. 

Insurers will need to consider the regulatory response to their use of Big Data and what level 

of regulatory risk they are prepared to assume. There is current!)' considemble uncertainty 

in the industry around how insurers' use of Big Data 11111 be regulated. Meanwhile, many 

companies continue to make signifi ant investments inlnsurTech, new models, and Big 

Data infrastructure. To help limit potential losses and foster the confide ce needed for 

insurers to continue to invest in Big Data, lawmakers and regulators will need to watch these 

developments carefull)' and be prepared to respond quickly. 

Regulatory Challenges 

Regulators will continue to face challenges as they review and respond to insurers' t~'Oiv ng 

uses of Big Data. The following highlights important challenges, which often have 

professionalism considerations as well (outlined in Section Ill): 

a. Privacy. As insurers' collection and use of data evolve, insurance regulators seek to 

better understand company algorithms and the types of data used for areas in which 

regulatory and legal review is necessary. To pro11de state-of-the-art products, many 

insurers are investing hea1~ly in data, te<hnology. and related resources. Given the 

competitive nature of the marketplace, insurers often are reluctant to share data-related 

intelle<tual property and market insights with regulators, which can create challenges 

for regulators 11)1ng to understand evoh1ng practices. The degree of prote<tion afforded 

under state freedom-of-information la11~ varies substantially by jurisdiction and often 

does not provide suffici t protections from insurers' perspectives. Stronger privacy 

protection for Big Data information might increase transparency and thereby enhance 

regulators' understanding of evolring practices and facilitate better regulation. 
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In addition, as both the US(S and complexity of data grow, consumer opinion mar 

increasing!)' influence regulators' views and reactions. For example, if individuals believe 

that the use of certain l)1J(S of data is inappropriate, regulators mar need to understand and 

account for thm expectations of consumer privacy. 

b. Staffing. Shortages of staffing and expenise for regulators willlikelr prore problematic 

given the increasing complexit)' of data and approaches. To address this, proposed 

addition ofNAIC staff hat could support technical!)' rigorous and data-intensive 

revie ~ could facilitate a more effici t use of regu.lator)' resources. 

c. Correlation \'S. Caui3tion. lfindi,oduals and competitors do not know their risk 

exposure \'ersus others, then large heterogeneous pooling works well. As insurers 

identify beha,oors (or controllable risk drivers) through empirical research or data 

anal)1ics, insurers can signal to the market how to lower collective risks or appropriate!)• 

charge those who take on riskier behavior. For example, owners of commercial 

buildings understand the value of automatic sprinklers, which result in lower insurance 

premiums and claims. Individuals who smoke are charged for their elected riskier 

behavior. Howew, predicti\·e anal)1ics can onlr uncover correlations among data 

elements. These data elements mar be driven at a deeper level by other factors. Both 

insurers and regulators \\ill need to ensure that spurious correlations are not driving 

pricing and cowrage decisions. For those events where the true dri\·ers are not known, 

risk pooling can be used to smooth out the impact of costly events randomly striking 

members of a group. 

The American Academy of Actuaries has historically worked close!)' with regulators and 

poliq•nakers in pro,•iding objective, unbiased, and nonpartii3n insights into issues of an 

actuarial nature. In these interactions, these panies have relied on the professionalism and 

technical skills of actuaries to provide clear information for the benefit of the public. 

Section Ill will address professionalism considerations for actuaries working ""th Big Data. 

As Big Data continues to eYolve,the Academr will continue to work with regulators and 

the public to provide insights and infom1ation to address the challenges that Big Data mar 

present. 

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Section Ill 

Professionalism 

Actuaries have professional obligations to uphold the reputation of the actuarial profession and 

fulfill the p ofession's responsibility to the public in the emerging area of Big Data. An important part 

of this responsibility is to comply with the law. In many situations, actuaries also have unique insights 

into the results and implications of the use of Big Data and must be willing and capable to explain 

such insights, where appropriate, to the key stakeholders of the work. such as regulators, consumers, 

company management. auditors, etc. The value of the actuaries' work is enhanced through 

adherence to the Code of Professional Conduct, actuarial standards of practice, and U.S. Qualific tion 

Standards. A key attribute of the applicable standards is the requirement for actuaries to provide 

explanations and rationales for their conclusions. 

36 

Professional judgment from actuaries iscritical in the utilization of Big Data in acn•aries' 

work. Actuaries provide added value to Big Data work in their ability to "connect the dots' 

through a deep understanding of the subject matter. ln exercising professional judgmen~ 

it is important for actuaries to be cognizant of the fact that l<ithout performing proper 

analyses or validation, the l't$uhs of Big Data can be misleading. A combination of a good 

understanding of the context in which the data "~s obtained and avoidance of unthoughtful 

adherence to the e~u ts of a model can aid in better Big Data outcomes. 

It should be noted also that "spurious correlations" that might be exhibited in a Big Data 

31lal)~is do not imply causality. There are man)' examples of two pieces of data that are 

very closely correlated o1•er a period oftime that do not have a causal relation~1ip. While 

causality is not a requirement forthe application of Big Data anal)1ics, users of Big Data 

should be aware of that these correlations exist. 

ll1ere are man)' professionalism issues that may be encountered when working with Big 

Data and predictive analytics. The work of actuaries is governed b)' the Code of Professional 

Conduct (Code) and must comply with applicable actuarial standards of practice (ASOPs). 

The Code and ASOPs pro1ide a framework for dealing with issue~ of professionalism that 

might arise in the work of actuaries. While actuaries ha1•e traditionally dealt \\ith large 

volumes of data and a variety of modeling techniques, Big Data may pose new challenges 
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that differ from those that actuaries encountered in the past. In addition, actuaries 

historically have built analyses and models based on traditional inferential statistical 

methods (descriptive and diagnostic analytics); however, predictive anal)1ics techniques 

offer unique and ditfertnt challenges to consider. Some professional organizations. such 

as the Data Science Association, have codes of conduct that apply spedfi ally to the 

key elements of Big Data, such as data quality, 1-olume, variety. and associated analytical 

techniques. R>r instance, data scientists must"use reasonable diligence when designing. 

creating and implementing machine learni.ng srstems to avoid harm."" 

Th s section reviews the professionalism requirements for actuaries "-orking with Big Data 

and engaging in predictire analytics. Some professionalism and ethical issues that arise in 

this context are also highlighted. 

Actuarial Professionalism 

Code of Professional Conduct 

In 2001, the fi1't U.S.·based actuarial organizations adopted a consistent Code of 

Professional Conduct. The Code sets forth what it means for an actuary to act as a 

professional. It identifies he responsibilities that actuaries have to the public, to their clients 

and employers, and to the actuarial profession. The purpose of the Code is to require 

actuaries to adhere to standards of conduct, practice, and qualifi ation. The Precepts of the 

Code identify the professional and ethical standards "'ith which an actuary must comply 

to fulfill their responsibility to the public and the actuarial profession. The law (i.e., statutes, 

regulations, judicial decisions, and other statements ha,;ng legally binding authority) may 

impose additional obligations upon an actuary. Where requirements oflaw conOi t with the 

Code, the requirements of law shall take precedence. Many ohhe 14 Precepts in the Code 

wiU ha1't relevance to work performed related to Big Data. 

Se1•eral Pm:epli deal wilh general conduct ijiues that apply to mrr wvice provided 

by actuaries, such as acting honest!)'• with integrity and competence; using tides and 

designations only as authoriwl by the relevant actuarial organization; prohibitions against 

disclosing confide tial information; and requirements to cooperate with others. Most of 

the Precepts focus on the conduct of an actuary when pro1oding actuarial services. The 

Code defi es actuarial services as "Professional senoces provided to a Principal by an 

indi1•idual acting in the capacity of an actuary. Such sen•ices include the rendering of advice, 

37 
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mommendation~ fi dlngs, or opinions based upon actuarial considerations."" An actuary 

"·ill need to consider whether the Code applies to their performance of services that involve 

Big Data based on whether those services meet the defin lion of actuarial services and if a 

particular sel\ict ini'OII'~ actuarial considerations. Consider a marketing effort to gain new 

customers that uses predictive anal)1ics to determine the customm who would be most 

likely to buy an insurance product. Actuarial considerations for such an effort might include 

data quality, appropriateness of use, and the accuracy of predicti,•e results. 

Actuarial Standards of Practice 

Precept 3 <>f the Code requires an actuary to ensure that actuarial services performed 

by or under the direction of an actuary satis~· applicable standards of practice. In the 

United States, the appticable ASOPs are promulgated by the Actuarial Standards Board 

(ASB). When a question arises about the applicability of a standard of practice, or where 

no applicable standard exist~ an actuaq• shall utilize professional judgment, considering 

generally accepted actuarial principles and practices." When an actuary uses procedures 

that depart materially from those set forth in an applicable standard of practice, the actuary 

must be p1epared to justify the use of such procedmes. 

A full treatment of the rele,<ant sections of each of the ASOPs is beyond the scope of this 

paper. Following are some of the ASOPs that may be relerant to sen~ces involring Big Data. 

Further details regarding these ASOPs are included in Appen&x 2. 

I. ASOI' No. 23, Dnra Qualiry, pro,~des guidance to actuaries when selecting data, 

performing a review of data, using data, or rel);ng on data supplied by othm in 

performing actuarial services. It also applies to actu3Iies who are selecting or preparing 

data or who are responsible for the selection or preparation of data that will be used by 

other actu3Iies in performing actuarial services when making appropriate disclosures 

regarding data quality. 

2. ASOP No. 12, Risk Classifimtion (for All Practice Areas), applies to all actuaries when 

performing professional services with respect to designing, reviewing, or changing risk 

classift alion systems used in connection with fo ancial or personal security systems 

regarding thedassifo at ion ofindi\~duals or entities into groups intended to refl ct the 

relative likelihood of expected outcomes. 
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3. ASOP No. 38, Usi11g Models Outside the Actuary~ Expertise (Propertymrd Casualty), 

applies to actuaries who use models that incorporate specialized knowledge outside of 

the actuary's own area of expertise when performing professional services in connection 

wilh properly and (aSual~· insurnnte co,•ernges. Th s standard applies 10 lhe use of all 

models whether or not they are proprietary in nature. 

4. ASOP No. 25, Credibility Procedures, applies to actuaries when performing actuarial 

services invoh1ng credibility procedures: a) when the actuary is required by applicable 

law to evaluate credibility; b) when the actuary chooses to e-~luate the credibility of 

subject experience; c) when the actuary is blending subject experience with other 

experience; or d) when the actuary represents the data being used as statistically or 

mathematically credible. 

S. ASOP No. 41, Actuarial Cammrmicatior~, pro11des guidance for preparing actuarial 

communications within any practice area. Included in this guidance are requirements 

regarding: a) form and content; b) clarity; c) timing of communication; and d) 

identifi at ion of responsible actuar)'. Additionally, guidance regarding disclosures 

with an actuarial report, explanation of material differences, oral communications. 

responsibUity to others, and retention of materials are included. 

6. ASOP No. 21, Respo11di11g to or A.<Sistillg Auditor:; or £mmi11er:; ill Comrectio11 witlr 

Fillnllcial Audit~ Fillnllcial Rt~•i~ a11d Fillalldnl ExamillatiollS, applies to acmaries 

when performing actuarial sen1ces as a responding acmary or as a re1•iewing actuary 

in accordance with generally accepted auditing standards or a fi ancial examination 

for the purpose of oversight of the fi ancial condition of an enti~ . An actuary needs to 

be semitil•e to the possibility that when Big Data and predictive anal)1ics are used for 

fi ancial reporting purposes, the responding actuary may haw to explain the use of Big 

Data 10 the revie11ing actuary. 

The examples of applicable ASOPs are nol exhaustive. Other ASOPs ma)' be applicable 

depending on the assignment. As the use of Big Data and predictive modeling continues 

to e-·olve, it is possible that it will become the basis for de-·eloping actuarial assumptions or 

contribute to the construction of models or be integrally irwolved in pricing and ratemaking 

or the evaluation of risks in general. With these innovations, the actuary would be well 

served to understand the implications, benefits, and considerntions in using Big Data and 

predictive modeling. 

39 
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Qualifi ation Standards 

Precept 2 of the Code stales that "An Actuar)' shall perform Actuarial $eC\ices only when the 

ActUat)' is qualifi d to do so on the bas~ ofbasic and continuing education and experience, 

and only ~~hen the AciUarysatisfies pplicable qualifi ation standards." Annotation 2-2 goes 

on to state: "The absence of applicable qualifi at ion standards for an assignment or for the 

jurisdictions in which an Actuary rendm Actuarial SeC\'ices does not relie1•e the Actuary 

of the responsibility to perform such Actuarial Services only when qualifi d to do so in 

accordance with this Precept."" The actuary should alwa)~ refl ct on their qualifi at ion~ 

and must be prepared to document their qualifi at ions (USQS Section 6.2) for any project 

being undertaken, and Big Data/predictive anal)tics projects are no exception. As an 

evolving area, it may not all'")~ be a dear-cut determination, and professional judgment 

ma)' need to be applied. 

In addition, U.S. Qualifi ation Standards section 4.3" addresses emerging or nontraditional 

areas of actuarial practice. It states that an actuary practicing in an emerging or 

nontraditional practice area can satisfy the continuing education requirements by 

maintaini~gk.nowledge of applicable standards of practice, actuarial concept~ and 

techniques rele1•antto the topic of the Statement of Actuarial Opinion. 

Ethical Considerations 

40 

Many actuaries are well equipped to integrate innovati1•e anal)1ics with traditional actuarial 

practices. A new paradigm inl'olves a demand for new skills and can raise a wide range of 

ethical and professional challenges. The Code and the ASOPs guide actuaries in navigating 

these challeng~ and dealing with new implicatio~ whilecontinuouseducation and the 

highly developed quantitalil'e skills of actuaries can aid them in acquiring new skill sets and 

slaying al>reast of emerging technologies. 

The traditional·look in the mirror" test (which is implied but not spelled out in the Code) 

means that an actuary objectil•ely examine his or her qualifi ations (basic and continuing 

education and experience) :llld make a professional judgment about whether the actuary 

can fulfill the actuary's obligations under the Code to: 

Act honestly, with integrity and competence-perfonn act\tarial seC\ices with skill and 

care (Precept t); and 

Perform actuarial seC\ices only when qualifi d to dow (Precept2). 
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Algorithms, Techniques, Correlation, and Causality 

Th s S«tion reviews the potential professionalism issues that may surface when using Big 

Data and predictive analytics in anr actuarial area. 

Many newly introduced methodologies, whether pmiouslyemployed in other professions 

or recent!)' de,•eloped. represent sophistia~ted models that borrow from other areas of 

science, such as artiflc a! intelligence. Some methodologies invOI\'e extreme!)' difficult and 

complex mathematics that may require someo1te specifl all)' trained in that area. Other 

models may be hard to interpret, even if fully understood. Th scould result in what is 

perceiYed as nontransparent outcomes. 

To the extent that an actuary employs a model, the actuar)'S level of effort in under:~tanding 

and evaluating a model should be consistent with the intended use of the model and its 

materiality to the results of the actuarial analysis. At times an algorithm or model ma)' 

lack transparency or may not exhibit a clear connection between the input and output. If 

the application of an algorithm or model results in an outcome that regulators or others 

percei1•e as unfair or unfairly discriminatory, its use may be restricted or disallowed. As 

noted in Section II, the actuary should be a11~re of regulators' concerns that a variable could 

be considered a proxy for, or be correlated with, a prohibited f.tctor. 

Actuaries often are asked to lead projects that utilize predictive model~ ASOP No. 38, 

a.lthough referenced as a property and casually A SOP. ma)' provide some guidance beyond 

P&C work, as it contemplates that actuaries may make use of a model that is outside of their 

area of expertise." In addition, a re1ised version of ASOP No. 38 is pending that11·ould 

com all practice areas. The current ASOP No. 38 requires the actuary to: 

I. Determine appropriate reliance on experts; 

2. Have a basic understanding of the model; 

3. Evaluate whether the model is appropriate for the intended application; 

4. Dttemiine that appropriate validation has octurted; and 

5. Determine the appropriate use of the model. 

Understanding what an actuary's responsibilities are and what roles the actuary plays on 

the predictive anal)1ics team is key. These are important professionalism questions for the 

actuary wno may not have an explicit role or defi ed responsibility in the de~·elopment or 

use of the models but who nonetheless has some implicitle,·el of professional or ethical 

responsibility. 

UASOPS'O).I.Sa;ful-IJ~·A~~oASOPshooldnotbt~td-shn'frtJaprl.lCINCilybt)tond.u~K:Of'tmJ~ .... lfi'II)AS09s;~olbtltik.w.tpp!Jt.toNt.tht.lmWftftl!.hllisM~~to.tl'CIIlltrlht~inrriattdA'JOP.I." 

41 
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42 

There are currently no ASOPs spedfi ally dealing with Big Dara or predictive models that 

differ in material aspects from traditional actuarial methods, models, and techniques. 

Consequently, users of such models may choose to look to ASOP No. 3S or, if they are 

performing stn1ces in conne<tion with PS:C insurance coverages to which ASOP No. 38 

applies, they will need to justify any material de~ iation from the obligations identifi din 

ASOPNo.38. 

For example, in employee benefit plan design~ if an actuary is unfamiliar "'th the 

algorithms used to model employee behavior, employee preference, and employee choice 

and those considerations are material to the actuarys work, ASOP No. 38 may pro,;de 

useful information in terms of model e,~Juation, validation, and documentation. The 

actuary's work product may not invoh'e the creation of such models, but their use could 

impact the actuary's work, assumptions, or communications. 

Applications of Big Data can be useful in denti~;ng correlations based on patterns 

discomed by analyzing data that tracks well with the behavior of individuals. In some 

cases, however, the correlation indicated by the data might be coincidental or there may be 

a confounding factor-i.e., a spurious correlation. Th s may suggest an algorithm problem. 

Actuaries working in this area need to ensure that specialists who analyze the data and 

build the models/algorithms h3\·e appropriate training and use the tools and procedures to 

test and correct for issues such as spurious correlat on~ for example, following standard 

model-building practices such as data partitioning with training. \'l.lidation, and testing sets 

will most likely identify and eliminate such spurious correlations. Without correcting for 

spurious correlations, undesirable results may occur. 

Underwriting is an area where it is important to understand the distinction between 

correlated results and causal relation~1ip~ While actuarial standards do not require an 

actuary to esrablish a causal relationship, man)' regulators h3\•e, for public policy reasons, 

disallowed the use of underwriting indicators unless it can be shown there is a causal 

relationship with the insurance claims that might occur under the insurance contract. In 

some cases, causal relationships are self-evident or can be presumed or explained. In other 

cases, such causal relationships can be demonstrated with data and analyses. However, there 

can be cases where the relationship is subject to some uncertainty about the \'alidity or the 

quantlfi ation of the relationship, and the undem~iti g indication may not be allowed. 
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Algorithms can be used in the underwriting process to assign a poliC)11older to a risk 

class and/or rate class. Generally, such assignments must be objecti1•e, transparent, and 

explainable to regulators and to insureds. There can be regulatory, statutory, or other legal 

rtstriclions regarding explanations and justifi ations of mtings and risk class assignment. 

Data anal)1ics also brings the potential benefit of uncovering previously unknown or 

hidden relationships in highly dimensional data. Once indicated by the data anal)1ics, 

such relationships or correlations may indicate a need for further investigation. In health 

insurance, data analytics may suggest that a gap in diagnostic coding of a condition may 

exist as part of a risk adjustment program, when the condition that appears to be missing 

a diagnostic code in claims may not actuallyex.ist For example, if prescription drug claims 

are used to determine potentially missing diagnoses in medical claims and an asthma 

medication claim is present without a diagnosis in the medical claims data, it may suggest 

that a gap exists for the asthma condition. However, some asthma medication also is used 

to treat chronic obstructive pulmonary diStase (COPD} and, if this is the case, the model's 

result may be erroneous. The descriptive and predictive models, consequently, may pro,;de 

opportunities for identi~;ng potential issues that can be researched through review of 

medical records, or through a care coordination 1•isit, or further investigation into potential 

waste, fraud, or abuse. if the method or approach does not result in an unsupportable action, 

the algorinhm can be tested for its ability to be a good predictor, and adjusted as necessary. 

Using Big Data for claim/care management outreach may give an incomplete or even 

an inaccurate picture of the issues a member may have. For care management efforts in 

health ins11rance, outreach on asthma education or disease management programs may 

be inaccurate if the member is using an asthma meclication for treatment of COPD. It is 

important for the actuary to be aware of the correlation of the data to other potential causes 

before using the information. Often Star Ratings in Medicare Advantage and Prescription 

Drug and Affordable Care Act business for health insurance are used to measure how well 

a plan performs in se1•eral categories, such as quality of care and customer serl'ice, include 

patient sat~faclion scores. If outreach is performed based on an inaccurate result from an 

a.lgorithm, this can lead to patient dissatisfaction and lower Star Ratinss of a plan. 

43 
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The use of Big Data models is an extension of the traditional work of the actuary go\'erned 

by the Code and ASOPs. There are se'·eral challenges not seen in traditional actuarial work 

including, but not limited, to: 

Reliance on and the need to suptn~se the work of other technical experts; 

Drawing conclusions from correlated relationships without clear .-idence of a causal 

relationship; and 

Public policy concerns regarding the use of personal data. 

These challenges require the actuary to carefully consider the professionalism and ethical 

considerations associated with these data models in wal~ that may not apply in traditional 

actuarial work. 

Role of the Actuary 

44 

In many applications of Big Data in businesses in which actuaries are emplored. 

multidisciplinary teams are utilized to effici tlyand effecti,·ely complete the project. The 

teams are commonly composed of statisticians. computer scientists, data scientists. and 

actuaries. Actuaries on these teams mar be thought of as the subject ma~er experts. But 

actuaries mar be positioned to be the quarterbacks of the Big Data teams. With the 

proper background, an actuary can understand and direct the work of the Big Data 

multidisciplinary team based on their professionalism requirements and subject mauer 

expertise. 

As the evolution of Big Data continues in the areas of practice in which actuaries prO\•ide 

senices, the professionalism and technical expertise provided by actuaries are essential 

elements upon which the public and regulators can place reliance. The professionalism 

requirements of actuaries pro,ide guidance for the proper application and disclosure 

of Big Data assumptions and methodologies. The)' require actuaries to adhere to the 

high standards of conduct, practice, and qualifi ation of the actuarial profmion, therebr 

supportil}$ the actuarial profession in fulfilling its responsibility to the public. 
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Appendices 

Appendix 1: lnsurTech 

lnsurTech is a blending of the words "insurance" and "technology.' It is the insurance 

industryanalog oft he term Fin Tech. a blending of the words "fi ancial• and "technology.' 

1he application of lnsurTech is marked by the innovative use of technology to transform 

the insurance customer's buying. under11~it i ng. and in force management experience by 

replacing traditional constructsofinsurancewith technology-driren systems that use 

predictive anal)1ics and are often independent oft he traditional approaches. 

lnsurTech inno,oationscontinue to occur at increasing rates of speed throughout the 

insurance marketplace, ranging from marketing to claim~ and including fi ancial 

managem~nt, although the current focus is signifi antly on marketing and distribution. 

lhese innovations are happening in all lines of insurance business. 

Below are three examples of ways in which lnsurTech is transforming the industry: 

Insurance companies are changing the customer bu)ing experience through lnsurTech 

applications. Under one such app-driven product, underwriting utilizes Big Data· 

based algorithms to issue policies in less time than consumers ha1~ e.'perienced under 

traditional unden<riting. Th s company primaril)' targets Millennia!~ an app-dri1·en 

generation that cares about causes. The company donates a portion of their revenues to 

charities insureds elect through the app-mediated application process. 

Life insurance companies are deploying life insurance applications using lnsurTech 

devices and approaches. For instance, one company has deployed lnsur Tech processes 

to spe-ed up the issuance of!ife insurance policies and another introduced a program 

that integrates lnsurTech technologies with its life insurance products. 

Attracting and retaining new customers is a top priority of some insurers using 

technology-dri,~n de>ices to transform the customer engagement relationship. 

lnsurTech consulting fi ms are cropping up in the life insurance space to address the 

challenges insurers a.re facing to understand the e~ olution currently taking place in the 

marketplace. 

Momentum in the industry is growing to increase the capitali1.ation on the benefits of 

lnsurTech both for additional functionalities and in other insurance practice areas. 

45 
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46 

The "how" oflnsurTech,like FinTech,ls highly dependent upon Big Dm sources and Big 

Data anal)1ics such as predictive anal)1ics The most pervasive examples of lnsurTech 

applications include wearable devices.telemalics devices. customer technology apps. data 

porrals, and platformt lnno ativ~ lnsurTel:h applications uliliu pr~dicti1•e and artific al 

intelligence methodologies and technologies that simplify underwriting algorithms, and 

improve claims management, retenlion,targeted markeling. and other processes after 

issue. Companies are measuring the accuracy of traditional models against Big Data-based 

models and often fi ding the Iauer just as accurate, if not more so- and, more imponantly, 

signifi antly less expensive than traditional models. Additionally, many real-time analyses 

that pre,;ouslycould not be performed are now performed using predictive anal)1ics. 

lnsurTecl'l approaches deploy Big Data to manage, expand, and remediate, if necessary, 

the customer experience and other aspects of insurance transactions. as well as insurance 

company management and strateg)'• often with signifi ant savings and effici cies. However, 

infrastructure changes to manage Big Data capabilities can involve large investments. 

The driving force behind the de1-elopment oflnsurTech companies is the belief that the 

insurance industry is ripe for inn01~1ion and disruption. One force dri,;ng this disruption 

is beha,;oral. Millennials pursue a different consumer engagement paradigm than prior 

generations. The follo~>ing generations will be eren more media-enabled, forcing additional 

evolution in how companies engage consumers, simplify the issuance of polices, and 

manage those policies after issue. 

The offering of uhra<ustomized policies. social insurance, and new streams of data from 

internet-enabled devices characteri.ze the mali;et approach oflnsurTech companies. In 

addition to new pricing models,lnsurTech startups are testing deep learning-trained 

artific al intelligence models to handle the tasks of brokers and fi d the right mi.< of policies 

to complete an individuals insurance coverage. There is interest in the use of apps to pull 

disparate policies into one platform for management and monitoring. creating on-demand 

insurance for micro-events like borrowing a friend's car and the adoption of the peer-to-peer 

model to both create customi1.ed group coverage and incenli,;ze positive choices through 

group rebates. 

The industry may be ripe for these innOI'lltions. but incumbent players are sometimes 

reluctant !o adopt them. Insurance is a highly regulated industry with many larers of 

jurisdictional legal limitat on~ Regulators are still developing the expenise to regulate 

the use of Big Data in the context of insurance. Thu~ they may be resistant to relaxing 
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Observations 

regulations btfore the)' fully understand predictive algorithms. Insurance companies may 

err on the side of <aution and shy away from startup ventures rather than risk regulatory 

challenges. 

Many JnsurTech startups still require the help of traditional insurers to handle underwriting 

and manage catastrophic risk. In addition, change always requires a transformative mindset. 

However, insurance is dependent upon consumers, and as more lnsurTech capabilities 

garner consumer interest with a more refi ed,tech-enabled, and user-friendly approach, 

insurers will likely embrace the idea oflnsurTech, buying up son1e of the innovations or 

creating their 0\\11 innovation~ 

While innO\'<ltions come with re11'ards, they also im·olve risks. There is a need to e''aluate 

the risks t~~ innO\'ations pose to the 6 ancial standing of insurance organizations. 

The foliO\''ing are some key obsef\'ations of the potential impact of emerging insurance 

technologies on life, health, pension, and property and <asualty insurance. 

Observation I: The distribution of many insu.rance products is moving awa)' from the 

traditional and exclusive agent/broker-policyholder relationship tO\\'ard a more impersonal, 

internet-l>ased relationship. Th swill likely btnefit insurers in the followu1g wa)~: 

Provide signifi ant strategic advantage to those companies that effectively, and in a 

time!)• manner, deploy its use. lnsurTech companies can provide signifi ant guidance as 

to how insurance companies can market bttter and more cost-effectively; 

Improve how insurance companies manage their in-force blocks of business; and 

Moti,'llte regulators to develop Regulatory Technology (Reg Tech) to monitor the use of 

lnsurTech. 

Observation 2: For insurers, the key risks associated with the emergence oflnsurTech 

include data pri\'2cy, regulatory compliance, product marketing. cyber fraud, and 

operational, underwriting. and strategic r~ks. 

Obsemtion 3: Insurers adopting and leveraging advanced technologies to deliver 

innovative insurance products face the risk of confli ling outcomes derh·ed from the used 

oflechnologies such as artifk al intelligence, machine learning algorithms, and natural 

language proce$Sing techniques. Cloud computing sm;ces pose a unique risk associated 

with unauthorized sharing of consumer data. 

47 
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Observation 4: The increasing use of third-party data to reduce and simpli~· traditional 

unden1Titing metltodologies poses risks to post-claim review processes for insurers, 

especially within the contestable period. It may also be more difficult to use claims 

exptrienct as a learning tool for the underwriting process. 

Obserntion 5: lnsurTech derelopments may increase the scrutinyofinsurer market 

conduct and operations b)' regulators as nontraditional data sources ma)'Contain proxies for 

variables disallowed by regulators. In addition, the te<hnologies will likely undergo scrutiny 

by regulators to ensure simllar outcomes for similar risks. 

Obserntion 6: Regulators will need to augment their skill sets to supervise the use of 

lnsurTe<h,adranced modeling techniques. aJtd Big Data by insurance companies. Insurers 

and regulators likely will need to strike a balance between regulatory supervision and 

industry i~nOI'lllion to delirer an improved level of services to consumers at competitiw 

costs. 

The obseMtions pro1ide insight into how lnsurTe<h will hkely transform the insurance 

industry. The)' do not directly address risks that are a function of how the technologies were 

developed or the standards br which these te<hnologies are evaluated against model risk 

and validation criteria. The following outlines considerations for assessing lnsurTech 1ocndor 

risk and developing model risk and validation criteria. 

lnsurTech Vendor Risk 

Many companies (lnsurTechs) have been formed in recent years that focus on leveraging 

technology to address the issues and opportunities presented to insurers. These lnsurTechs 

are vendo:rs to insurance companies as the insurance marl.:etplace and regulators take up 

these innovation~ Considerations for working 11ith lnsurTechs follow. 

Product Quality 

Criteria must be established to assess the quali~· of the lnsurToch startups and the products 

thq• can potential!)' offer imurers. Areas intporranl in assessiJtg quality might include: 

Insurance product expertise; 

Qualil)' of company management; 

Insurance-backed funding sources; 

Knowledge of insurance distribution channels; 

Financial strength to suggest ndt~try sustainabuity; 

Unders.tanding of the regulatory insurance em;ronmenl and pri\'llC)' issues; and 

Demonstrated proficie cy del'eloping tech-based customer engagemenl media. 
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Integration and Maintenance 

A signifi ant problem 11ith any technology is its susceptibility to obsolescence.ll can be 1•ery 

costly and resource-intensive for companies to integrate innovative technology 11ith existing 

company :S)~t<mt Hwmr, the integrotion of digital tt{hnologies can help insurers dmlop 

the following; 

Advanced methodologies to exchange data between facilities; 

Advanced machine learning anal)1ics capabilities; and 

Ability to denti~· and acquire new sources of consumer data. 

External Oata Dependencies 

The main «>ncerns involve the consistency of data from a myriad of sources and how to 

measure the impact of data inconsistency on models and ultimately the consumer. Specifi 

considemions include: 

The credibility, validity, and traceability of data sources; 

The independent validation and reconciliation of data sources; 

The epoch of data sources and alignment to measures assessed by model~ and 

The validity and re1•iew of underlying models generating external data sources. 

Compliance Standards 

The ad1·mt of the age of Big Data has challenged regulators with issues that current 

regulations are not equipped to address. Regulators are rapidly augmenting their education 

and regulatory tools to deal with the following: 

Prii~C)' issues poised by the inclusion of Big Data sources in models; 

Ethical issues raised using Big Data in models impacting consumers; 

The inclusion of variables in Big Data masking disallowed 1~riables; 

The reconciliation of consumer risk metrics derived from different models and data 

sources, and across different geographies; and 

The structuring of modeling data sets to assess geographical influences. 

49 
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Model Risk & Validation 

50 

As with an)' innol"ation, Big Data represents an e~plored frontier for insurers, regulators, 

and consumers. Every model poses a certain amount of model risk to an organization. 

Model risk c.~n be introduced through such things as: 

Applying models incorrectly; 

Usin~ improper models; 

Developing inaccurate conclusion~ and 

Utilizing improper data. 

Other forms of model risk can be introduced through items that are uniquely associated 

with Big Data. lnsurTech vendor models use Big Data and technology for dri~ng decisions 

based on data rather than traditional underiiTiting methods. Howem,the validation 

methodologies oflnsurTech technologies are still developing. Some considerations in the 

development of validation methods might include the following: 

Controls around authorized access and authorized use; 

• Controls around the proper operation oflnsurTech technologies; 

Assessing controls around data transmission and security from hacking; 

Validation of underlying algorithms and temporal consistency of result~ and 

• Analytical and surveillance tools to trigger alerts to refresh or rebuild models. 

It is unlikely that the use of Big Data will become obsolete. The insurance industry will 

need to develop model gomnance policies and standards of practice to monitor the use 

and application of lnsurTech technologies, as well as to collaborate with the regulatory 

community on issues that these innol'ations raise. 
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Appendix 2: Actuarial Standards of Practice (ASOPs) 

A ful11rea1men1 of lhe relt~'anl seclions of eacl1 oflhe ASOPs" is beyond I he scope oflhis 

paper. The pertinent sections of some relel'dnl ASOPs are highliglued and commemed on in 

lhe following. Th s lisl is nol inlended lobe exhausli1•e or all inc.lusil•e. 

1. ASOP No. 23, Data Qua!iry 

Section 4.1.gslales: jAn acluarial communicalion should disclose when malerial and 

relevan1]"1he exislence of resuhs thai are highly uncerlain or ha1·e a potentially signifi ani 

bias of which the actuary is aware due to the quality of the data or other information 

relevant to the use ofthe data, and the nature and potential magnitude of such uncertainl)' 

or bias. ifthey can be reasonably determined ... " 

Big Data cannot be expecled to be comple1ely error-free. Data may come from third-pari)' 

sources or ma)' require frequent updating in near real lime for use in certain applications. 

Section 4.1.g is just one of the II disclosure requirements in the ASOP. The disclosures in 

ASOP No. 23 tie into ASOP No. 41,Aclrtarial Commrmications. 

2. ASOP No. 12, Risk Oassrfi ori011 !lor All Practice Areas) 

Section 3.2.1 stales: "The acwaryshould selecl risk characlerislics 1ha1 are related 10 

expected ouloomes." 

Seclion 3.2.2 states: "While the aCiuary should select risk characteristics 1ha1 are relaled 

10 expected ouiComes, il is no1 necessary for lhe actuary 10 eslablish a cause and elfec1 

relationship belween lhe risk characteristic and expecled oulcome in order to use a specifi 

risk chara<lerislic~ 

S..><tion 3.3.3 stales: "When eslablishing risk classes, lhe actuary should (a) comply wilh 

applicable Ia"·; (b) consider induslry praCiices for lhal type of fi ancial or personal securily 

sys1em as known to lhe acluary; and (c) consider limitations crealed by business practices of 

the fi ancial or personal securil)' system as known to 1he aciUarf. 

As no led abo1•e, this ASOP S3)~ that " ... it is nol necessary for I he aeluary lo eslablish a cause 

and effect relalionship beh<een I he riskcharaelerislic and expecled omcome 10 use a specifi 

risk chara<terislic~ However, this cause-and-elfecl relalionship may make il easier to explain 

the resuhslo policyholders. agents, regulators. underwriters. and management. 

Sl 
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It should be noted that this ASOP is not confi ed to pricing and underwriting. A Big Data 

project to ident · liability claims that have a high potential for ad1·erse development would 

use many data elements. each of which can be thought of as a risk classifi at ion. Care 

should be raken to ensure that the data elements, perhaps in combination, do not result in 

discrimination th•t would violate applicable law. 

3. ASOP No. 38, Usmg Models Outside the Adual}"s Expertise (Property and Casualty) 

Section 3.3.1 states: "The actuary should be reasonably familiar with the basic components 

of the model and have a basic understanding of how such components interrelate within 

the model. In addition, the actuary should identify which fi Ids of expertise were used in 

developing or updating the model and should make a reasonable effort to determine if the 

model is based on generally accepted practices within the applicable fi Ids of expertise. The 

actuary should also be reasonably familiar with how the model was tested on~lidated and 

the le~ el of independent expert m1ew and test ng~ 

ASOP No. 38 covers topics in the P&C area that ma)' be rele1~nt to reliance on models 

de~·eloped by others, rdiance on other actuaries on the modeling team, responsibilities in 

understanding the model, model structure, and model assumptions and parameters within 

the limits already discussed. 

As of the writing of this paper, the ASB is considering the adoption of an actuarial standard 

of practice that more broadly addresses the use of models by actuaries in all practice area~ 

The proposed modeling ASOP has completed its 3''exposure draft nd ";II be considered by 

the ASB in June 2018 fora 4th exposure. 

4. ASOP No. 25, Credibiliry Procedures 

S2 

Section 3.5 states: ·rn car');ng out credibility procedures, the actua')' should consider the 

homogeneity of both the subject experience and the relevant experience. Within each set 

of experience, there may be segments that are not representative of the experience set as 

a whole. The predictive value can sometimes be enhanced by separate treatments of these 

segments. The actuary should also consider the balance between the homogeneity of the 

data and the size of the data set." 

ASOP No. 25 also covers such top ~ as selecting or developing credibility procedures, 

selection and blending of "perience, and homogeneity of the data. Appendix I of ASOP 

No. 25 co,,tains a section on emerging techniques that discusses generalized linear models 

and other multi1•ariate modeling techniques. However, there is no e.xpress commenta')' 

regarding the applicability of this ASOP to Big Data. 
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5. ASOP No. 411 Aduanal CommunicaC•ons 

Section 3.2 stales: "ln the actuarial report, the actuary should stale the actuarial fi dings, 

and identify the methods, procedures, assumptions, and data used by the actuary with 

suffici 1 darity that another actuary qualifi din the san1e practice area could make an 

objective appraisal of the reasonableness of the actuary's work as presented in the actuarial 

report." 

Section 3.4.4 states: "An actuarial communication should identi~· the party responsible 

for each material assumption and method. Where the communication is silent about such 

responsibility, the actuary who issued the communication "ill be assumed to haw taken 

responsibility for that assumption or method. The actuao'J's obligation for identi~ing the 

other party who selected the assumption or method depends upon how the assumption or 

method was selected." 

ASOP No. 41 also co,·ers topics such as clarity, liming, who the responsible actuary is, 

the actuarial report, reliance on others for data and other information, responsibility 

for assumptions and methods, and disclosures, but there is no specific dscussion of the 

applicability to Big Data. 

6. ASOP No. 21, Responding co or Assisting Audocors or Exominers in Connection with Finandof Audits, 

Finonool Review!, and Financial Examinations 

Section 3.5.4 states: "The responding actuary should be prepared to discuss with the auditor 

or examiner, including the revie\\ingactuary, the following items underlying those elements 

of the fi ancial statement or other elements ''ithin the scope of the fi ancial audit, fi ancial 

review, or 6 ancial examination for which the actuary is the responding actuary: 

a) the data used; 

b) the methods and assumptions used, and judgments applied, and the rationale for 

those methods, assumptions. and judgments; 

c) the source of any methods and assumptions not set by the responding actuary; 

d) the models used; 

e) the design and etfecticeness of controls around the process, procedures. and models; 

f) any signifi ant risks to the entity considered by the responding actuary; and 

g) the reasoning to support results and conclusions~ 

Therefore, where Big Data and predictive analytics are used for fi ancial reporting purposes, 

the responding actuary should be able to explain the use of Big Data to the reviewing 

actuary. 

Sl 
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182 

STATEMENT FROM FINANCIAL INNOVATION NOW SUBMITTED BY 

CHAIRMAN MIKE CRAPO 


91818119.eps

Statement for the Record 

Senate Banking Committee 

'Fintech: Examining Digitization, Data, and Technology' 

September 18, 2018 

Financial Innovation Now ('FIN') is an alliance of technology leaders working to modernize 

the way consumers and businesses manage money and conduct commerce. We believe that 

technological transformation will make financial services more accessible, safe and 

affordable for everyone, and we promote policies that enable these innovations.' FIN 

appreciates the Committee's efforts to examine how consumers and small businesses are 

using their own financial data to empower themselves and make better-informed decisions. 

There is now a wide range of tools for consumers and small businesses to better manage 

their finances, including myriad apps that enable consumers to view and manage consumer 

financial aocount information on a consolidated basis across accounts and financial 

institutions. These tools help consumers analyze account activity, make better-informed 

financfal decfsions and become aware of, and ultimately avoid, unnecessary fees. 

Consumers also are using savings tools, informed by permissioned access to consumer 

financial aocounl data, to help meet their savings goals, as well as apps that can educate 

and advise them on the range of financial products and services that may be available to 

them, including recommendations for credit and other financfal products or services. 

Open data can also enable efficient and more reliable tools that provide verification of 

aooounl ownership or loan application information. Aocount verifiCation tools enable 

consumers lo access other financial products and services, including peer-to-peer payment 

services, in real time, rather than by delayed verification options, such as micro-transfers. 

In spite of strong consumer interest and lhe potential for signif~cant consumer benefit, 

consumer access to financfal aocounl data has, at times, been restricted. Some accountholding financial institutions have blocked access to permissioned entities (e.g., personal 

finance applications) that the consumer directs to access consumer financial aocount data. In 

addition, sometimes account-holding financial insmutions change data formals and URLs or 

online forms in ways that disrupt automated access to consumer financial account data by 

these permissioned entities acting on behalf of consumers. 

1 FIN member companies include Amazon, Apple, Google, Intuit, PayPal, and Stripe. 
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FIN offers the following principles for a safe and accessible finan<:ial data ecosystem: 

Sewrity. Realizing the benefits of pennissioned access to consumer financial 

acoount data is dependent on robust security-all participants in the eoosystem are aligned 

that security is a shared goal and a shared responsibility. There are a growing number of 

approaches designed to share securely consumer financial acoount data. We support the 

adoption of industry-wide technical protocols; however, both security challenges and 

technology will continue to evolve. Security standards must not box in any specific 

tecllnology and should allow for technology to be updated, as needed, on an ongoing basis. 

Reliability. MGreover, realizing the benefits of permissioned access to consumer 

financial acoount data is dependent on consumer confidence that the data obtained by the 

permissioned entity is current, accurate and complete. Industry will need to develop common 

expectations around lhe content of data, and should also consider developing a reporting 

and resolution mechanism for inaccurate or incomplete data. Finally, we understand that 

there may be circumstances under which acoount-holding financial insmutions would need to 

discontinue access to consumer financial acoount data. Industry standards should ensure 

that these circumstances would be exceptional and reasonably justified, and that 

pennissioned enmies. would be notified of the interruption and the timeline for resolutioo. 

Consumer Consent. Fundamental components of permissioned access to consumer 

financial acoount data are appropriate consumer consent to such access, transparency 

regarding what data permissioned users may access, and the purposes for which the data 

may be used. Permissioned users should also provide a clear revocation option. 

Industry-Driven Standards. Individual one-off partnerships enable certain consumers 

to benefit from pennissioned access to financial acoount data, but these partnerships cannot 

scale to all acoount-holding institutions or third-party application providers. Industry 

standards would empower a broad dass of consumers to permission access to consumer 

financial acoount data, and would promote innovation. For example, industry standards 

would enable many small financial institutions to facilitate permissioned access to consumer 

financial acoount data, minimizing the need to negotiate bilateral agreements with every 

third-party application provider. Standards should be developed by industry through a multi· 

stakeholder, consensus-based approach. Security standards should be risk based, so that 

security requirements match the risk posed, but do not constrain innovation. The need for 

standards to evolve as technology evolves makes a regulatory approach to setting standards 

for permissioned access to consumer ~nancial account data insufficientiy ftexible. 

In dosing, FIN believes that consumers and small businesses should be empowered to 

penmission access to financial acoount data securely and easily, using whatever secure 

application or technolagy they wish, without charges or restrictions that unreasonably favor 

any one application or technolagy over another. Thank you for your leadership and 

reO()Qnition of the potential benefits of data to improve financial health and access to new 

services. 
184 

LETTER FROM ELECTRONIC PRIVACY INFORMATION CENTER 

SUBMITTED BY SENATOR SHERROD BROWN 


91818121.eps

epic.org 

September 26, 2018 

The Honorable Michael Crapo 

Chainnan 

U.S. Senate Committee on Banking, 

Housing, and Uroan Affairs 

534 Dirksen Senate Office Building 

Washington, DC 20510 

Electronic Privacy Information Center 

1718 Connecticut Avenue NW, Suite 200 

Washington, DC 20009. USA 

The Honorable Sherrod Brown 

Ranking Member 

'1. +1 202 483 1140 

~ +1 202 4831248 

II @lEPICPrivacy 

e https:ll•pic.org 

U.S. Senate Commiuee on Banking, 

Housing, and Uroan Affairs 

534 Dirksen Senate Office Building 

Washington, DC 20510 

Dear Chainnan Crapo and Ranking Member Brown: 

We write to you regarding the upcoming hearing on "Fintoch: Examining Digitization, 

Data, and Technology."' The financial services industry is undergoing tremendous, rapid change, 

and ensuring that consumers remain protected must continue to be a top priority. While financial 

technology ("fintech") may provide consumers with new tools and opportunities, it also raises 

substantial privacy and data security concerns. 

The Equifax breach of 143 million consumer records last year provides a stark reminder 

that Americans' most sensitive data is entrusted to companies who repeatedly fail to protect that 

infom1ation.

2 The Eloctronic Privacy lnfom1ation Center ("EPIC"), founded in 1994, has long 

advocated for greater transparency and cybersecurity safeguards for consumer infonnation held 

by financial and commercial organizations, and has repeatedly urged Congress to ensure that 

financial institutions and fintech companies adequately protect consumer financial data. EPIC 

submined a statement to this Committee for the September 2017 hearing, "Examining the 

Fintech Landscape," and to the House Committee on Energy & Commerce for the June 2017 

hearing, "Improving Consumer's Financial Options With FinTech.'o.l As this Committee 

examines fintech, several security and consumer privacy issues should remain at the foretront. 

1 f.XJJmj!Pjng tire fjmech Landscape. !15th Cong. (2018), S. Comm. Banking, Housing, and Urban AOairs, 

hnpi1Avww.banking.s<na1<.govillearingslfontech _ txamining-digitiution-<lata·and·technology. 

'EPIC, UJ Mmivn US Consumm Soffer u..,;,. Dcro Breach. Equifax ot Fa11lt (Sept. 8. 2017~ 

hllpi1l<pic,orgl20171091143·million·us-ronswners-suffe.html. 1 Stal<ment from Electronic Privacy Information Center {EPIC) to Senators Crapo and Bro•11 (Sept. II, 2017), 

hnps1Avww.epic.orgltestimonylcongress!E PIC.SBH UA -Fin T ech-S<p20 l7.pdf; Sl3t<mem from Electronic Privacy 

lnfOIT!lation Ctnter(EPIC) to Repr<s<ntati\'es Lana and Schako•~ky (June 9, 2017), 

hnpsJA,ww.epic.orgltestimonylcongress/EPIC·HEC·finTech·Jtm2017.pd( 

EPIC Statement to U.S. Senate Fintech: Digitization, Data, and Technology 

Comminee on Banking, Housing, and Urban AfTairs September 18,2018 

Defend Privacy. Support EP IC. 
185 


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Many new fintex:h platforms- and established firms entering the fintech space-rely on 

third-party data aggregators to provide them with consumer financial data.' When aggregators 

cannot legitimately obtain consumer financial information, they often simply take that 

information from consumers by "scraping" --{)r copying--the information when consumers input 

it, often without alerting the consumer.s Fintech platforms may also provide aggregators with 

consumer information lhrough Application Programming Interfaces (AP!s) that allow 

aggregators to simply access financial infomtation in bulk.6 Congress should take a proactive 

role in examining how -consumer financial data is accessed, aggregated, and used. Specifically, 

this Committee should ensure that consumers' financial data is strongly protected no matter who 

holds it. Data aggregators should be held to the same standards as financial institutions, and 

should not be permitted to engage in reckless processing and distribution of consumer financial 

data. 

Consumers must also have confidence that their data is being processed fairly and 

accurately. As EPIC Advisory Board member Professor Frank Pasquale told this committee, 

unmonitored data brokers poke holes in consumer protections by processing data in secret and 

using inaccurate infom1ation.1 Companies using consumer financial data should instead be 

required to register with the Federal Trade Commission or another federal agency and should 

notify consumers whe11 using their financial data.

1 Those consumers should be provided with the 

ability to challenge the use and accuracy of their data, and companies should be held accountable 

for improper use of personal data.' Extending the Fair Credit Reponing Act (FCRA) to data 

aggregators would benefit consumers and help ensure fair and accurate uses of personal 

information. 

Fintex:h and all companies in the financial services industry should be subjex:t to strict 

privacy rules to protect consumers. While the Consumer Financial Protection Bureau provides 

principles on consumer-authorized access and use of consumer fmancial information, companies 

are not required to follow these.10 The rising popularity of banking and finance apps-.:ombined 

with the lack of meaningful oversight and consumer protection-will no doubt lead to another 

breach similar in scale to the Equifa~ breach, with millions of financial records exposed 

simultaneously. Congress should therefore require fintech companies to ensure that data transfers 

o[ consumer financial data are secure and that third panies receiving that data are subject to 

limitations on use and disclosure. 

'U.S. Dep't Trea>ury, A f i11oru:ial S>"'"' tiJaJ Creates Economic 0pt11Jrtunitfes: Nonbank Fb:oncials. Fimedr. and 

lnM>-ation 24 (2018), hUpS:II11ome.lre&SUry.gov/sitcaldefault/filesi2018.(1&/A·Fin:rn<:iai·S}>Iem·that·Crtates· 

Economic.()pponunities-·Nonbank-financials-finle<h·and-lnn0\·ation_O.pdf23-24. 

' Jd. at2S. 6/d. at26. Simibr A Pis were 31 issue in the FattbooldCambridge nalytic~ S<andal, 11-htre Facebook allowtd thirdpany developers to ac= troves of uset infonnation without alening the users thtmsetves. See In re FacebookCambridge Ano6"1ico, EPIC. hnpsJ/epic.orglpri1'11cy/fa«boolJcambridge·analytical. 1 Explorbrg till Finteth l.turdscope: Hearing Before I he Seii/Jie Cmmte on Banking. Hou•fng. and Urbon Affairs. 

t 15th Cong. 6 (2017) (testimony of Frank Pasqualt, Professor of law, University ofM31yland), 

hnps:/ .banking.s<ll.l e.g olmedial od?asquale'/o20Test mony'lo 09 12- .pd . 

*Jd. 3t8. 

' Seeid. 

1

° Consumer Fin. Prot. Bureau, Consumer Protetrion Principles: Coll$unrrr Amhorized Fintmcinl Dora Slurring and 

Aggregotion(20!1), hnps:l/files.consumerfonance.go,•/Odocumentsicfpb_oonsumer·prote<tion·principfes_data· 

aggregation. pdf. 

EPIC Statement to U.S. Senate 2 Fintech: Digitization, Data, and Technology 

Committee on Banking, Housing and Urban AtTairs September 18,2018 
186 


91818123.eps

Thank you for your attention to this critical issue. EPIC looks forward to working with 

the conmlittee to ensure that consumers are protected and informed about their financial 

information. We ask that this letter be entered in the hearing record. 

Sincerely, 

Is/ :Marc 'Roten6erB--

Marc Rotenberg 

EPIC President 

Is/ leffGarv 

Jell Gary 

EPIC Legislative Fellow 

Is/ Caitriona fitzaerali{ 

Caitriona Fitzgerald 

EPIC Policy Director 

EPIC Statement to U.S. Senate 3 Fintech: Digitization, Data, and Technology 

Committee on Banking, Housing and Urban AITairs September 18,2018 
187 

STATEMENT SUBMITTED BY INDEPENDENT COMMUNITY BANKERS OF 

AMERICA 


91818124.eps

'(f;A September 18,2018 

l\ 1111 n, 1 ( 0,

1

,n ,111 Fintech: The Community Bank Perspecthe 

BmtR"'/ \IIIRit 1 

On behalfofthe more than 5,700 community banks represented by ICBA, we thank Chainnan Crapo, Ranking 

Member Bro1111, and members of the Senate Banking Committee for convening today's hearing on "Fintech: 

Examining Digitalization, Data, and Technology." We appreciate you raising the profile of a critical issue for the 

future of credit, payments, and American prosperity. As outlined below, ICBA believes that fintech is a promising 

de1•elopment for consumers, businesses, and community banks. To achieve the full potential of fintech, policymakers 

must ensure that it does not jeopardize safety and soundness and consumer protection. In particular, the Office of the 

Comptroller of the Currency's (OCC's) special purpose national bank charter fails to address these concerns and 

dcsc.rYCS closer scrutiny by Congres-s. 

The promise of fintech 

Technological innovation and deployment continue to alter the way that consumers and businesses conduct banking 

and commerce. Communi!)' bankers are embracing innovative fimech solutions to simplify the banking experience 

for consumers and facilitate and speed transactions. Fintech oflers a wealth of opportunities for community banks. 

These include: simplif)ing the banking experience for consumers; providing a more detailed and sophisticated 

understanding of customers and targeting products and services to the market segments where they are most valued; 

creating innovative uses of data to ease and speed decision making and providing access to the cloud infrastructure to 

lower costs. 

Many community banks have partnered with fintech companies to access the opportunities described above. The 

challenge facing regulators is to encourage technological innovation that doesn't unfairly disadvantage existing 

market participants and doesn't put the financial system or consumers at risk. 

OCC special purpose charter fails to address these concerns 

In August, the OCC announced that it would accept applications for a new special purpose charter for non-depository 

online marketplace lenders, other fin tech companies, and any other company that the OCC considers to be in the 

''business of banking." While the special purpose charter will subject online lenders and fintech companies to more 

oversight and regulation than they have had, it tails to address the essential questions concerning the regulatory 

framework that will govern the supervision of these finns. 

For instance, while the OCC Policy Statement on Financial Companies' Eligibility to Apply for National Bank 

Charters says that "grant(ing) special purpose charters does not alter existing barriers separating banking and 

commerce," it is unclear whether the owners or alliliates of an OCC chartered fintech company would be regulated in 

the same way that the Bank Holding Company Act restricts the commercial actil'ities of a bank holding company. 

Allo11ing corporate conglomerates like Google or Amazon to own banks violates the U.S. policy of maintaining the 

separation of banking and commerce, jeopardizes the impartial allocation of credit, creates conflicts of interest, and 

www icba.org/advocacy 
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1\ll~\1>1" ( 0\1\ll "1\ 

B\\li.tR".tl/ \\IHtl( \ 

unwisely extends the federal safety net to commercial interests. If the OCC truly wants to separate banking and 

commerce, the agency should issue a rule that states that any s~ial purpose national bank charter and/or its owners 

or afiiliates will be subject to the same restrictions as those that apply under the Bank Holding Company Act. 

ICBA supports the development of a fintech regulatory framework that is no less stringent than that which applies to 

insured depository institutions. The OCC should publish transparent capital and liquidity requirements for these finns 

that specifically address minimum levels considered appropriate for a fintech finn to be well capitalized. Fintech 

capital and liquidity requirements should be no less rigorous than those that apply to insured depository institutions. 

Such a framework would promote a fair regulatory S)~tem, protect consumers, maintain the separation of banking 

and commerce, and support safety and soundness at these companies. 

Fintech charter should have statutory authority 

Because the scope of the chartering authority under the 150-year old National Bank Act is unclear, ICBA urged the 

OCC to obtain specific legal authority from Congress before creating a special purpose charter for fin tech companies, 

a step that could ftmdamentally change the financial market place, put safety and soundness at risk, and jeopardize 

consumers. Furthennore, the OCC should issue mles, subject to notice and comment, which would prescribe the 

scope and requirements of the new special purpose national bank charter. 

A full bank charter is the best point of entry for fin tech companies 

Varo Money (which has rebranded itself as Varo Bank) recently received preliminary approval from the OCC for a 

full bank charter that will allow it 10 accept FDIC-insured deposits. Varo Money provides mobile payments and 

accounts sef\•ices. This, in our view, is how fintech companies that want bank charters should enter banking. Varo 

Bank will be subject to full arra)' of national bank regulation and supervision, and if it creates or is acquired by a 

holding company, the holding company will be subject to consolidated Federal Reserve examination and oversight, 

as would any other de novo national bank. 

Online marketplace lender performance raises serious concerns 

The recent problems some online marketplace lenders have experienced with liquidity and earnings, as well as with 

compliance, make it important thai these lenders be subject to safety and soundness supervision and regulation. Il!ll 

could become a source of svstemic risk. lltese companies have not experienced a serious economic downturn yet and 

already they have been subject to serious funding and capital issues. 

Congress should close the industrial loan company loophole 

A loophole in the Ba•lk Holding Company Act allows fintech companies and commercial holding companies to 

acquire industrial loan companies (ILCs) without being subject to federal consolidated supe"•ision. In recent months, 

www.icba.org/advocacy 2 
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1\ll~\1>1" ( 0\1\11 "" 

B\\!i.tR ... II/ \\IHtl( \ 

Square, SoFi Bank, and most recently, Nelnet Bank, have applied for lLC charters. All of these companies have 

holding companies and affiliates that engage in diverse, non-financial, commercial activities. 

Expansion of ILCs through fintecl!s and commercial companies would put the federal safety net, and ultimately the 

American taxpayer, a1 risk. ILCs are 1he functional equi1•alem of lull-service banks. Commercial holding company 

ownership of llCs will eiTeclively combine banking and commerce, contrnry to long·slanding American economic 

policy. 

ICBA supports slatulory closure oflhe ILC loophole and urges I he FDIC 10 impose an immediate lwo-year 

moratorium on !he approval of deposil insurance for ILCs. 

Historically, limited purpose charters ha\'e evolved far beyond their original purpose and intent 

The indus1rialloan company charter should provide a caUiionary example for financial regulators. Special purpose 

bank charters have the po1en1ial 10 evolve be)·ond their original purpose and intenl and end up having all of the 

advantages and benefils of a full-service bank charter wilh limited supervision and regulation. 

Closing 

Thank you again for convening today's hearing.ICBA urges Congress 10 exercise lhoughtful and vigorous oversighl 

of <he emergence of fintech and i1s implications for consumers, businesses, and the broader economy. We are pleased 

10 have the opportunity to oOer the community bank perspective and look forward to working with this Committee as 

consideration of this important issve unfolds. 

www.icba.org/advocacy 3