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Birny Birnbaum 2 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

The Center for Economic Justice 

CEJ is a non-profit consumer advocacy organization dedrepresenting the interests of low-income and minority coas a class on economic justice issues. Most of our work administrative agencies on insurance, financial services 

issues. 

On the Web: www.cej-online.org 
Birny Birnbaum 3 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

Why CEJ Works on Insurance Issues 

Insurance Products Are Essential Financial Security Individual and Community Economic Development: 

CEJ Works to Ensure Fair Access and Fair Prices for ThEssential Products and Services, particularly for Low- anModerate-Income Consumers. Insurance is the Primary Institution to Promote Loss Prevention and Mitigation, Resiliency and SustainabCEJ Works to Ensure Insurance Institutions Maximize Tin Efforts to Reduce Loss of Life and Property from CataEvents and Promote Resiliency and Sustainability of Ind

Businesses and Communities. 
Birny Birnbaum 4 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

Big Data Defined 

Insurers’ use of Big Data has transformed the way they do mapricing and claims settlement. Big Data means: 

 Massive databases of information about (millions) of indiv

consumers 

 Associated data mining and predictive analytics applied to 

 Scoring models produced from these analytics. 

The scoring models generated by data mining and predanalytics are algorithms. Algorithms are lines of comput

that rapidly execute decisions based on rules set by programmthe case of machine learning, generated from statistical corremassive datasets. With machine learning, the models change automatically.
Birny Birnbaum 5 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

What’s So Big About Big Data? 

1. There has been a revolution in insurance pricing, marketi

claims settlement resulting from insurers’ use of Big Data databases of new insurance and non-insurance personal information with associated data mining and predictive anscoring. 

2. Insurers’ use of Big Data has huge potential to benefit con

and insurers by transforming the insurer-consumer relatioby discovering new insights into loss mitigation. 

3. Insurers’ use of Big Data has huge implications for fairnes

affordability of insurance and for regulators’ ability to keethe changes and protect consumers from unfair practices 
Birny Birnbaum 6 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 4. The current insurance regulatory framework in most state

particularly related to risk classifications and unfair discrim

does not provide regulators with the tools to effectively reinsurers’ use of Big Data. Big Data has massively increasmarket power of insurers versus consumers and versus r

5. Market forces alone – “free-market competition” – cannot 

protect consumers from unfair insurer practices. So-calle“innovation” without some consumer protection and public guardrails will lead to unfair outcomes. 

6. Regulatory reform emphasizing regulatory collection, ana

publication of consumer market outcomes – Regulatory Bwill yield more efficient and effective regulation for consuminsurers and producers, will promote more competitive ma

will foster quicker adoption of innovative technologies thaconsumers and fulfill public policy goals. 
Birny Birnbaum 7 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

Personal Consumer Information in Big Da

 Telematics – Auto, Home, Wearable Devices  Social Media  Shopping Habits/Purchase History  Hobbies and Interests  Demographics/Household Data/Census Data  Government Records/Property Records  Web Tracking  Vehicle Registration and Service Records  Facial Analytics  Mainstream Credit Files: Loans, Credit Cards

 Alternative Credit Data: Telecom, Utility, Rent 
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Center for Economic Justice What’s So Big About Big Data 

Examples of Insurer Big Data Algorithms 

Pricing/Underwriting: 

 Price Optimization/Demand Models  Customer Value Scores,  Telematics,  Credit Scores,  Criminal History Scores,  Vehicle Scores,  FireLine Rating  Accelerated Life Insurance Underwriting 

Claims: 

 Fraud Scores,  Severity Scores  Telematics 
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Center for Economic Justice What’s So Big About Big Data 

Example: Price Optimization/Consumer Demand 

Deloitte Presentation at 2014 CAS Ratemaking S

What is the ultimate goal of price optimization? Increase ProfInsurance pricing can be classified in three levels of sophisticBasic Rating Plans, Underwriting Models, and Market DemanMarket Demand Models: Customer price elasticity to optimize A key advantage of including underwriting components is that insured’s price elasticity and demand behavior is on the finathe policy level and not the coverage and sub-component leOptimizing price on the sub-coverage level creates a gap betwresults and the insured’s price behavior. 
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Center for Economic Justice What’s So Big About Big Data 

Example: Pricing Models 

EagleEye Analytics Real Time Scoring Mode

An insurer testimonial: when our underwriter is sitting at his 

desk, and they’re looking at a renewal or quoting new busines

are two scores that pop up: a frequency score a kind of underquality score and also a pricing score we use to help price renbusiness and new business quoting. The risk scoring goes w

just financial data, it uses all of our characteristics in our data used for rating or not and then we are also able to bring in thidata, Census Bureau data, to supplement that. 
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Center for Economic Justice What’s So Big About Big Data 

EagleEye Analytics Real Time Scoring Mod

Step 3: Build Elasticity Models. The loss ratio model will provindications greater than you will take in one revision. Elasticitprovide the data you need to forecast how your policies wrespond to price change. Talon Elasticity models have msegments, not one curve for the entire portfolio.Step 4: Optimize. Using the Loss Ration Models, Elasticity M

incorporating the aging that understands the new business petime to make rate level decisions. Optimization takes these and applies them specifically to the policies you want. It’pick the perfect rate level and forecast future profitability anStep 5: Filing Machine Learning models is becoming comEagleEye customers have filed and been approved in 45 state

multiple lines of business. . . .As a bonus – once you switch machine learning, your competition will not be able to revengineer your rating plan.
Birny Birnbaum 12 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data 

Example: Pricing Model 

TransUnion Criminal History Score 

“TransUnion recently evaluated the predictive power of court 

violation data (including criminal and traffic violations) “While a court record violation is created during the initial citatstate MVR is updated later and may be delayed depending on consumer’s response to the citation. For example, if someone guilty and pays a ticket immediately, the state MVR will be up

approximately two months. If the ticket is disputed in court, in the state MVR may not be updated for 6–19 months or longer“Also, as court records are created when the initial citatioissued, they provide insight into violations beyond those ultimately end up on the MVR—such as violation dismissviolation downgrades, and pre-adjudicated or open ticket
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Center for Economic Justice What’s So Big About Big Data 

Example: Claim Fraud Scores, Claim Severity S

LexisNexis Claim Tools 

“LexisNexis (LN) seeks to provide a Single Point of Entry for dall of information directly back into a carrier’s system whether marketing standpoint, underwriting process or especially the c

“LN has over 10,000 data sources that feed into its infrastructmonth and has contributed information from the industry. “Claims Data Fill” – deliver data and analytics directly into clain the claims process regarding parties, vehicles and carrier inUsed to verify information provided to insurers and provide indbeyond the data to identify whether a social security number iindicator of fraud or whether an address provided is a good adHas an analytic component at first notice of loss and througho

claim, constantly monitoring the claim looking for fraudulent aReal time data verification and enhancement with fraud scorinattributes 
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Center for Economic Justice What’s So Big About Big Data 

LexisNexis Claim Tools (con’t) 

“Example, insured calls in, rear-ended, all I got was license pl“Claims Data Fill takes that license plate, reach out to DMV to registration to get VIN number, we have policy database and carrier and policy information, take the registered owner, go orecords, pull back their address, date of birth, telephone numbsecurity, wrap that into a package and put it back into our sysof the time done in less than 5 seconds. “Take minimum information provided at first notice of losa fraud score at the initial notice of loss. Daily monitoring every time new information comes in, able to run various fraud scores, severity score.” 
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Center for Economic Justice What’s So Big About Big Data 

Example: Fraud Scores 

LexisNexis: “Severity Focus” 

“Identify claims with the potential to become severe: SeverityFocus utilizes advanced predictive modeling to identif

with the potential to become severe as they develop claims thotherwise would go undetected until much later. “SeverityFocus does not constitute a "consumer report" as thadefined in the federal Fair Credit Reporting Act, 15 USC 1681 

(FCRA).” 
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Center for Economic Justice What’s So Big About Big Data 

Example: Claims Scoring 

StatSoft’s Predictive Claims FlowTM 

“A predictive analytics and reporting solution for property and 

insurance companies can help you reduce loss ratios and impbottom-line profitability, often within a few months of implemeStatSoft’s Predictive Claims FlowTM solution incorporates predmodeling at every stage of an insurance claim. This closed lohas a unique scoring system that rates each claim at its inceppropensity for fraud and then continually rescores the claim athrough each step of a claim’s lifecycle.” 
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Center for Economic Justice What’s So Big About Big Data 

Example: Fraud Scores 

Infosys Social Network Analysis 

“The SNA tool combines a hybrid approach of analytical methhybrid approach includes organizational business rules, statismethods, pattern analysis, and network linkage analysis to relarge of amounts of data to show relationships via links. When for fraud in a link analysis, one looks for clusters and how thelink to other clusters. Public records such as judgments, foreccriminal records, address change frequency and bankruptcies data sources that can be integrated into a model. Using the happroach, the insurer can rate these claims. If the rating is higindicates the claim is fraudulent.” 
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Center for Economic Justice What’s So Big About Big Data 

Example: Fraud Scores 

Infosys: Social Customer Relationship Managem

“Social CRM is neither a platform nor a technology, but ratherIt is important that insurance companies link social media to tSocial CRM . . . gathers data from various social media platfoa “listening” tool to extract data from social chatter,. . . .The redata along with information stored in the CRM is fed into a camanagement system. The case management system then aninformation based on the organization’s business rules and seresponse. The response, from the claim management system whether the claim is fraudulent or not, is then confirmed by invindependently, since the output of the social analytics is just aand should not be taken as the final reason to reject a claim.”
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Center for Economic Justice What’s So Big About Big Data 

Accelerated Underwriting for Life Insurance 

AUW may look like an expanded simplified issue process but 

mortality that aligns more closely with fully underwritten businAUW is often modeled using predictive modeling and complex 

New data sources include 

 Enhanced application with use of behavioral economics  Credit profiles  Applicant Candor  Criminal History  Facial Analytics  Use of Wearable Devices 

AUW is a process that is dynamic in that non-medical and meinformation gathering may be customized to the individual app

Source: Mary Bahna-Nolan presentation to NAIC Life Actuarial Task Force, August 2016 
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Center for Economic Justice What’s So Big About Big Data 

A 20th Century Regulatory Framework for 21st Century Ch

Oversight of Inputs Based on Proposition That Preventing BaWill Prevent Bad Consumer Outcomes Regulatory Oversight of 

 Insurer Data – Licensing/Oversight of Advisory Organizat

Statistical Agents; Promulgation of Statistical Plans 

 Cost-Based Pricing – Rates Not Excessive, Not Inadequa

Unfairly Discriminatory; Oversight of Rate Manuals/Rating Prohibition on Explicit Use of Certain Characteristics of thConsumer. 

 Unfair Discrimination – Treating Individuals of the Same C

Same Hazard Differently. 
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Center for Economic Justice What’s So Big About Big Data 

Big Data and Modeling of Rates/Prices/Claims/Custome

Old Old School Big Data: Advisory Organization Loss Costs. 

of Data, Advisory Organization, Analytic Techniques, Filings, Transparency Old School Big Data: Credit-Based Insurance Scores. LimiteConsumer Protections for Completeness and Accuracy of DaFCRA, Limited Oversight of Modelers and Models, Limited TrNew School Big Data: Predictive Modeling of Any Database oConsumer Information. No Consumer Protections for CompleAccuracy of Data, No Oversight of Modelers and Models, No Transparency to Consumers 
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Center for Economic Justice What’s So Big About Big Data 

The Regulatory Framework Breaks Down in an Era of 

 Insurers now using data not subject to regulatory ov

the consumer protections of the FCRA. Regulators ability to ensure the accuracy or completeness of thedata sets. 

 Concept of unfair discrimination – consumers of sim

and hazard treated differently – becomes meaningleinsurers submit rating plans with millions of rate clas

 New risk classifications can be proxies for protected 

but with no recognition of disparate impact, risk clasthat have the effect of discriminating against protecteare permitted. Big Data amplifies this problem. 
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Center for Economic Justice What’s So Big About Big Data 

Big Data Algorithms Can Reflect and Perpetuate Historical 

Barocas and Selbst: Big Data’s Disparate Impact Advocates of algorithmic techniques like data mining argue th

eliminate human biases from the decision-making process. Balgorithm is only as good as the data it works with. Data minininherit the prejudices of prior decision-makers or reflect the wbiases that persist in society at large. Often, the “patterns” it dare simply preexisting societal patterns of inequality and excluUnthinking reliance on data mining can deny members of vuln

groups full participation in society.
Birny Birnbaum 24 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data New York Times, August 10, 2015: Algorithms and Bias: Q. aCynthia Dwork 

Algorithms have become one of the most powerful arbiter

lives. They make decisions about the news we read, the jthe people we meet, the schools we attend and the ads wthere is growing evidence that algorithms and other types can discriminate. The people who write them incorporate 

biases, and algorithms often learn from human behavior, 

reflect the biases we hold. Q: Some people have argued that algorithms eliminate dibecause they make decisions based on data, free of humOthers say algorithms reflect and perpetuate human biasyou think? A: Algorithms do not automatically eliminate bias. . . .Histbiases in the . . .data will be learned by the algorithm, and discrimination will lead to future discrimination.
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Center for Economic Justice What’s So Big About Big Data 

Fairness means that similar people are treated similarly. A understanding of who should be considered similar fparticular classification task requires knowledge of sattributes, and removing those attributes from considcan introduce unfairness and harm utility. Q: Should computer science education include lessons on 

aware of these issues and the various approaches to addthem? A: Absolutely! First, students should learn that design choalgorithms embody value judgments and therefore bias thsystems operate. They should also learn that these things For example, designing an algorithm for targeted advertisgender neutral is more complicated than simply ensuring is ignored. They need to understand that classification rulby machine learning are not immune from bias, especially historical data incorporates bias.
Birny Birnbaum 26 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data White House Report on Big Data For all of these reasons, the civil rights community is concernsuch algorithmic decisions raise the specter of “redlining” in theconomy—the potential to discriminate against the most vulne

classes of our society under the guise of neutral algorithms. . ability to segment the population and to stratify consumer expseamlessly as to be almost undetectable demands greater reespecially when it comes to the practice of differential pricing potentially discriminatory practices. It will also be important to how algorithmically-driven decisions might exacerbate existineconomic disparities beyond the pricing of goods and servicein education and workforce settings
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Center for Economic Justice What’s So Big About Big Data 

Insurer Use of Big Data Scoring Models Lack Fund

Consumer Protections 

 Accuracy and Completeness of Data  Oversight of Data Bases  Disclosures to Consumer About Data Used, How Us

Privacy Protections 

 Consumer Ability to Challenge False Information  Regulators’ Knowledge Of and Capability to Provide 

meaningful Oversight 

 Prevent discrimination Against Low-Income and Min

Consumers and other protected classes 

 Asymmetric Use of Data  Greater Cybersecurity Danger for Consumers and In
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Center for Economic Justice What’s So Big About Big Data 

21st Century Insurance Regulation 

1. Identify What Insurers are Doing 2. Monitor Market Outcomes 3. Create an NAIC Resource for the States for Big Da

Analytics 

4. Develop a 21st Century Approach to Oversight of R

Classifications, including Oversight of Modelers AcAdvisory Organizations 
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Center for Economic Justice What’s So Big About Big Data 

Identify What Insurers Are Doing 

To a great extent, regulators – and, of course, consumers and makers – do not know what types of information insurers are what they are using the information for and how they are usinA logical first step is to develop a template for states to use, wassistance from the NAIC for collection of requested informatrequest from insurers the sources and uses of data for varioufunctions. For each source of data, the insurer would provide name/description of the data, the source of the data and the uof the data -- pricing (including underwriting), marketing, claimsettlement, antifraud and other. This periodic survey will provide regulators with the basic ovewhat types of information are being used by insurers and whainformation is being used for. This information is essential for to respond to policy makers and to foster public discussion ovpotentially controversial types of data.
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Center for Economic Justice What’s So Big About Big Data 

Monitor Market Outcomes 

The old regulatory model of monitoring everything that goes inmarketing, pricing and claims settlement practices and model

feasible in the era of Big Data. Big Data has massively increamarket power of insurers versus regulators and versus consuRegulatory Big Data is needed. The data needed for a robusanalysis – one that includes the ability to monitor the affordabaccessibility of insurance in underserved areas as well the abperform enhanced market analysis to focus regulatory resourcproblem companies and problem markets – is transaction datpremium quotes, policies issued and claims. Insurance regulother financial regulators in the ability to monitor market outco

the collection of granular data for market monitoring. 
Birny Birnbaum 31 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data Industry’s “Muscular” Resistance to Regulatory Dat“The number of data calls has now reached a level where it is 

and affects insurers’ ability to effectively service their custome“It is very questionable, especially when you consider the add

insurers, what value some of these data calls have to the consome are also duplicated across multiple states.” “Our members see these things as examples of regulatory ovwhich has no connection to what regulators should be concer

Sampson said. “On top of that, these requests are uncoordinaoften duplicated state by state.” “Our members are concerned and have directed us to be morin our approach to pushing back against that trend.” 

1 “PCI will be muscular as we combat regulatory overreach, says CEO Sampson," Intelligent Insurer, October 23, 2016 
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Center for Economic Justice What’s So Big About Big Data 

Why Would Regulators Be Seeking More Data from In

The current regulatory framework is based on oversight of inp

insurer pricing and practices as the way to ensure fair and goconsumer outcomes. With insurers now using many types and sources of non-insupersonal consumer information for marketing, pricing and claisettlement – data sources over which insurance regulators haoversight – regulators must turn to data collection of markoutcomes to monitor markets and insurer performance. It only makes sense that insurers introduce new marketinand claim settlement practices more frequently and quick

ever before, that regulators need to ask for data to monito

action.
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Center for Economic Justice What’s So Big About Big Data 

Improved and Expanded Regulatory Data Collection Can 

for Insurers, Regulators and Consumers. 

Consider the following scenario to look at limitations of curren

regulatory data collection for market monitoring and market ainsurer and producer market performance and of consumer oFrom the Life Market Conduct Annual Statement (MCAS), the analyst sees that an insurer has a higher than average ratio replacements to policies issued for Individual Cash Value 

which include: 

 Variable Life  Universal Life  Variable Universal Life  Term Life with Cash Value  Whole Life  Equity Index Life 
Birny Birnbaum 34 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data What does the market analyst do? Look at other MCAS data? Ratios for Policies Surrendered Under 2 Years from Issuance2 and 5 Years or Between 6 and 10 Years? Problem: No Way to Relate Issue Age Data to Policy ReplaceTiming Data. Limited data for market analysis means contacting insurer to fexplanation – an explanation that more often than not shows or a problem with reporting of highly-summarized data. 
Birny Birnbaum 35 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data Let’s Change this Scenario – What if the insurers were reporegulators were analyzing, more detailed data? The Market Analyst sees the higher-than-average ratio for repfor Cash Value Life Insurance Products. The Analyst then examines the replacement ratios by product that this company’s sales are almost all one type of product acompany’s replacement ratio for this product is consistent witreplacement ratio for this product across the industry. Resultto contact insurer. 
Birny Birnbaum 36 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data Now, let’s say the analyst looks at the data by product line, fininsurer’s sales are predominantly in indexed universal life and the replacement ratio is much higher than the industry averagThe analyst looks to see the average age of policies replaced insurer for this product and finds a high percentage of replacerecently issued policies. Now the analyst looks to see the average issue age for the repolicies and finds a very high percentage of replacements at i85 and above. The analyst continues to work with the data before contacting 

insurer. 
Birny Birnbaum 37 Federal Advisory Com

Center for Economic Justice What’s So Big About Big Data Now the analyst looks to see which producers were selling the replacement policies to the 85 and over consumers and finds agents were doing this. Or the analyst finds that only one agent was responsible for thnumber of replacements for older consumers. Now we can compare the difference between market analysis data and market analysis of granular data: Instead of having to go to the insurer with a single metric – yohigh replacement rate for cash value products – the regulator answer the question without having to contact the insurer or, further analysis to narrow the focus – and inquiry to the insureparticular product, process and/or producer. 
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Center for Economic Justice What’s So Big About Big Data 

More Granular Data Reporting by Insurers and Analy

Regulators Produces Big Improvements in Efficienc

Effectiveness of Market Analysis. 

More granular data reporting allows more refined market anal

More refined market analysis means fewer contacts with insu

explain non-problems. More granular data reporting means a huge reduction in speccalls because regulators already have the data in almost all cMore granular data reporting means more focused regulatory investigations and inquiries. More granular data reporting means identifying insurers with gconsumer market outcomes and leaving them alone. More granular data reporting means regulatory involvement in management policies and procedures if there is a problem, noroutine practice. 
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Center for Economic Justice What’s So Big About Big Data 

This is Not State-of-the-Art Technology – 

It is Technology from the 1990’s State-of-the-Art Technology would be Data Visualizations and 

Analytics of Market Outcome Data by Regulators. There Are Opportunities to Build On Existing Data ReportExamples include: 

 Transaction reporting for Principles-Based Reserving – ev

life insurance, annuities and long-term care. 

 Transaction reporting already in place for workers’ compe

and significant portions of the industry in personal lines prcasualty insurance. 
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Center for Economic Justice What’s So Big About Big Data 

Muscular Cooperation, Not Opposition, Makes Sense Fo

On the one hand, we routinely hear from industry about the coinefficiencies of market regulation – the urban legends about multiple and duplicative market conduct examinations and specalls. But, on the other hand, every time the logical solution to these problems is proposed – more granular data collection for morand effective market analysis – the trades say, “Hell, No!” CEJ urges industry to embrace regulatory big data and work wregulators and consumers for quick implementation. We urge to move expeditiously on regulatory big data – it wouldn’t be tregulators act in the industry’s best interest despite industry o
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Center for Economic Justice What’s So Big About Big Data Benefits of Regulatory Big Data 

 Calibrate inputs/processes to outcomes  Assist financial regulation  More efficient/effective market regulation  Information to evaluate insurer and producer practices  Information to evaluate existing and proposed public polic Information to meaningfully evaluate affordability and ava

competition issues

 Information to empower consumers and promote competi

markets

On the last point, consider how little information consumers h

regarding the market performance of insurers and how publicsuch information – frequency of claim denials, frequency of atinvolvement, average time to settle different types of claims awould empower consumers in the same way that information safety empowers consumers and motivates automakers. 
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Center for Economic Justice What’s So Big About Big Data Create an NAIC Resource for the States for Big Data Just as the NAIC provides resources to assist the states in ottechnical/actuarial capability at the NAIC to assist the states wcollection and compilation of massive amounts of financial stadata to assist with financial analysis, collection and compilatioConduct Annual Statement data to assist states with market aname just a few examples – the NAIC should develop resourcstates in analyzing new big data models used by insurers. The NAIC resource would not be a regulator and would not prregulatory opinions. Rather, the NAIC resource would provide with technical – actuarial and statistical – expertise to answer questions about a big data pricing or claims model. The NAIC would also assist states in accepting and processing large dapart of analysis of a pricing or claims model.
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Center for Economic Justice What’s So Big About Big Data 

Modernize Regulatory Oversight of Risk Classific

Risk classification represents insurers’ – and society’s – 

about how to group consumers for the purpose of assignpremium. Risk classification determines the affordability availability of essential financial security tools – insurancconsumers. In the vast majority of states, the only justifneeded for a risk classification is a correlation. But, in aBig Data and the modeling of rates and risk classificatiotest is arbitrary and opaque. We see this in the huge difin rate impact of the same risk classification across insueven for an insurer across states. There is a need for a 

century approach to oversight of risk classifications – motransparency and more accountability 
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Center for Economic Justice What’s So Big About Big Data 

21st Century Regulation of Insurance Risk Classific

Require More Than A Correlation: 

 Risk-Based Pricing / Avoid Adverse Selection  Promote Loss Mitigation / Degree of Consumer Control  Transparency to Consumers/ Accuracy & Completeness  Honor the Risk-Spreading Purpose and Function of Insur Avoid/Minimize Disparate Impact 

Risk Classifications – whether called underwriting guidelines, 

placement rule or rating factors – should all be approved prior opportunity for stakeholder input. Regulatory Oversight of New Data – update statutes to treat organizations providing advisory organization services as advorganizations. The key activity is collective pricing, not insureownership of the advisory organization. 
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Center for Economic Justice What’s So Big About Big Data Consumer Data Ownership and Protections – Ensure consumgenerated data (telematics and wearable devices) is owned, cand transportable by the consumer. Ensure basic consumer for new data sources – disclosure, consent, access, completeaccuracy, adverse action notice, ability to correct.