WHITE PAPER

Using deep learningmodels to conquer
risk in the universalbank

1PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

Beyond data-driven, the bank of the future issignal-oriented
In “The signal-oriented bank of the future” paper, we detailed
how data-driven organizations are evolving to become signaloriented, where signal is an actionable insight generated from
customer data by a predictive model. Immensely valuable,
unstructured customer data in the form of speech and text from
dialogue and customer behavior can inform business processes
and drive beneficial economic outcomes.
The salient predictive models best suited to take advantage ofthis customer data are those in deep learning, which encompass
advanced artificial neural network models in the domains of
natural language processing, user content recommendations,and computer vision.
The impact that the adoption of deep learning models
can have in supporting the broad lines of businesswithin the universal bank is significant, especiallywhen we consider domain-specific foundation models
that are pre-trained for production readydeployments. We will trace the evolution of banking
through the ways in which risk was successfully
mitigated by the application of analytical techniques
enabled by technology. It’s an incredible story!

2PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANKThe story of banking
is the story of conquering risk
Banking’s modern origins can be traced back to Venice, where goldsmiths at their work table or “banca” - loaned coins to merchants on the personal promise of repayment. Since thebeginning, banking has always been focused on the assessment and management of risk.
Since the Renaissance, private banks have performed theclassic financial intermediation function of lending the surplus
funds held on deposit by one customer to another customer,at various maturities secured by various forms of collateral.
The burden of liquidating debts with specie, or governmentissued coins, evolved into a system where bank drafts
became accepted as “currency” to make payments andsettle debts.
The codification of fractional reserve banking in London allowed banks to provide the necessary
credit for merchants to support commerce and international trade. When the Bank of Englandon Threadneedle Street made a market for government debt - or “gilts” - the security of gilts
undergirded the development of the short-term money market on Lombard Street. The trading of
the stock in corporations in the coffeehouses of the Merchants Exchange across Threadneedle
Street evolved into the formalized London Stock exchange, with turnover enhanced by banklending on stocks as collateral.
The 18th century boom in seaborne international commerce led to a demand for marine
insurance against the peril of ships lost at sea, formalized by Lloyds of London. Both fire and life
insurance were originally provided by mutual aid societies and the development of actuarial
methods facilitated the development of hazard insurance by capitalists.
The development of financial capitalism in London in the 18th century illustrates that the majorityof what we know today as financial services has had a long life. What is new is how thesefinancial services are packaged, priced and delivered to an ever-wider audience. While humanneeds are constant, innovation in banking is driven by new technologies – especially those thatallow an enhanced ability to dimension and manage risk. The story of banking is the story of
conquering risk, whether it is risk born of credit repayment, illiquidity in times of crisis, mispriced or
misallocated investments, fraudulent behavior or the natural hazards of disasters or premature
death.
The breadth of risks mitigated and the reduction in the cost to insulate against them in banking,as expressed as rate of interest, goes hand-in-glove with the development of more accurate andreliable methods to assess and price these risks.

3PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#1Universal banking
The demand for financial services falls cleanly into two distinct sets of customers: commercial
and retail. Commercial banking serves institutions such as governments and businesses who canissue securities such as bonds or equities that are easily traded in regulated markets. Retailbanking is the domain of small and medium-sized enterprises as well as households andindividual consumers.
Universal banking refers to the extent that a single financial institution provides a broad set ofservices, including deposit-taking, lending, investment management, capital markets, wealthmanagement and insurance.
A key consideration for any financial institution is the degree to which the government is directly
or indirectly involved in the control of the bank’s decision-making and the degree of regulation
regarding the privileges of the charter. There are clear benefits to a banking license - notably theavailability of government-sponsored deposit insurance, a public good as it promotes
confidence that payments will clear and bank runs will be eliminated. With the benefit of deposit
insurance, however, comes the burden of government regulation. To ensure banking system“safety and soundness” and to promote other benefits to the public, such as crime reduction,banks are subject to a proliferating alphabet of regulations including KYC (know-your-customer),
STM (suspicious transaction monitoring) and AML (anti-money laundering). Government
regulation has introduced a new risk for a bank – the risk of sanctions or even charter suspension.

Historically, most banks have responded to the expansion ofregulatory oversight with expensive manual processes ordeveloped brittle rule-based systems to flag potential issues.
With natural language processing, onboarding newcustomers and addressing KYC (know-your-customer)
regulatory requirements can be made more efficient. Once
NLP distills information from documentation, deep learning
graph neural networks can then provide a deeper
understanding of the entities involved and automatically
identify and address potentially illicit transactions.

4PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#2
Payments
Banks compete on rate, terms and, increasingly, customer
experience. The vast majority of funds on deposit pay no
explicit rate of interest yet banks compensate deposit-holders
via the facilitation of payments and the ease of access to theiraccounts via banking channels.

Banks are at the epicenter of the payments system and thebenefits and risks therein. The medium of payment has
evolved from promissory notes to bank drafts to real-time
inter-bank funds transfers. The fundamental risk for the bankremains the same — are the payer and payee known andtrustworthy entities? As the latency in inter-bank payments is
reduced, the risk of fraudulent payments increases as does
the necessity for sophisticated risk mitigation algorithms. The
current decision tree learning approach to payments fraud
risk and credit repayment risks are yielding to more effective
deep learning modeling techniques such as recurrent neuralnetworks (RNN), which allow a higher-order understanding of
temporal payment patterns and cross-sectional payment
features such as merchant type and payment instrument.

As articulated by Nobel prize-winning economist Douglas
Diamond, the role of traditional banks in deposit taking andpayments processing provides these banks a competitive
advantage in credit underwriting through the “delegatedmonitoring” of customer funds inflows and outflows. The nexusof payer and payee relationships can be revealed frompayments flow data leveraging a payments network graphand the hidden patterns revealed by graph neural networks(GNN) deep learning models.

5

PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#3
Loans
From the earliest days of financing trade or providing working capital or funds for expansion,banks have developed an expertise in evaluating the creditworthiness of potential borrowers
through detailed knowledge of the people involved, complemented by sophisticated analysis of
the potential borrower’s account history and financial statements. Trade finance acrossinternational borders necessitated expertise in managing the risk of foreign currency
depreciation as well as counterparty risk in areas beyond legal recourse. Today, asset-based
lending requires enhanced analytical methods to understand the current and future value of thereal assets that collateralize the loan. Due to the complexity and dynamic nature of international
currency valuation, sophisticated deep learning models are on the frontier of enhanced
predictive power.
Commercial banking has a very long history, while retail banking as we know it today only took
shape in the early 20th century. In the 19th century, mortgages to purchase residential realestate were provided by extended family networks or were funded by non-profit mutual
associations like savings banks. Today in the US, mortgages are originated by banks and sold togovernment-sponsored-entities (i.e. FNMA & FHMLC) which package the cash flows of theindividual mortgages into mortgage-backed securities. Once again, the valuation of thesesecurities requires sophisticated modeling of interest-rate and prepayment risks and traditional
statistical models are being displaced by deep learning models, such as recurrent neuralnetworks (RNN).
Commercial Banks first began to make direct loans to
consumers at scale in the 1920s, stimulated by the necessity
to finance automobile purchases through multi-year
installment purchase plans. Trying to assess thecreditworthiness of individual consumers was very difficult,even with the development of “credit bureaus” who compiled
records of consumers creditworthiness which banks
contributed to and drew upon.
By the end of the second world war, a bank’s decision to make a loan to an individual was
based on a scorecard which assigned points to information that the consumer provided on theirapplication, including a character assessment by the banker. With the goal of reducing the costand improving the reliability of credit decisions, two Stanford engineering professors developed astatistical algorithm to evaluate consumer loan applications and the Fair-Isaac company wasborn; the algorithm evolved to ingest credit bureau data via a logistic regression model andprovided a binary scaled score we know as the “FICO score.”
The current decision tree-based approaches to credit repayment risks are beginning to yield todeep learning modeling techniques such as recurrent neural networks (RNN) which allow ahigher-order understanding of temporal payment patterns and cross-sectional features such as
merchant and payment instrument. In addition, to assess the creditworthiness of a broader
audience and to leverage novel payments data not captured by traditional credit bureaus,“alternative data” such as rental and utility payments is under consideration.

6

PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#4Investments
During the US Civil War, the Federal government was unable to meet the costs of the war effort
using tax revenues alone. Given the immaturity of the banking system, the Federal government
was forced to issue debt directly to individuals to pay for war materiel and soldiers’ wages with“greenbacks” which circulated as currency in the north as a supplement to bank notes andspecie. The huge cost of the war led the US government to sell war bonds directly to the public
for the first time and enlisted Jay Cooke and his army of commission sales agents spread acrossthe country.
The huge requirements of capital to build the railroad networks in Europe, North America, andSouth America led to the widespread distribution of corporate debentures, or unsecured bonds.
While joint stock banks were prevalent in England and America in the early 19th century,changes in corporation law led to the first widespread sale of corporate stocks directly to
consumers in the 1870s. The excess capitalization of railroad stocks allowed market operators like
Jay Gould to make the first Wall Street fortunes. The large flotation of corporate stocks led toformalization of stock jobbing via the New York Stock Exchange on Wall Street and the beginningof “mergers and acquisition” mania on a national scale. The invention of the corporate holding
company, or Trust, led to the development of what we know today as Investment Banking,exemplified by J.P. Morgan’s ability to roll up a dozen small foundries into the mammoth US Steelcorporation in 1910.
The development of Investment Banking in order tounderwrite bonds & stocks, as well as to develop and market
structured financial products, such as mortgage-backed
securities, was facilitated by innovations in computer
technology and valuation models.
The development of broad and deep securities markets, notably the New York Stock Exchange,
led to competition to identify investment and trading strategies that provide above market
returns, as defined by the “efficient markets” hypothesis of Eugene Fama and Kenneth French.
Institutional money managers encounter a wide range of risks to maximize performance, theymust determine the allocation of funds under management across asset types, positions within
assets and consider the tax implications of asset location, while minimizing trading costs.
The search for yield has stimulated the pursuant of increasingly sophisticated asset pricing
models, including those leveraging deep learning. While asset pricing models draw on the richhistory of security trades, the search for yield has led to the pursuit of “alternative data” sources,
such as interrogating published financial statements with natural language processing and evenleveraging computer vision to evaluate satellite images of store parking lots. Increasingly, thesearch for investment value is moving beyond just the evaluation of security price histories andeconomic statistical data.

7

PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#5
Wealth management
Until the late nineteenth century, wealth meant land holdings, private partnerships, and goldcoins. The development of government and corporate bond markets in the late nineteenth
century and widespread corporate stock holding by households in the early twentieth century
allowed wealth to be increasingly held as financial claims -- more liquid but with their value moresensitive to the state of the overall national economy. The failure of master promoter Jay Cooke
to keep the Northwestern Pacific Railroad afloat in 1873 led to the first finance-driven economic
depression in US history, the severity of which was only surpassed during the depression of the
1930s.
The development of institutional investment management waspioneered in the US in the early 20th century by trust
companies for the affluent and life insurance companies suchas New York Life for small investors. Stockbrokers advised
wealthy families on how to maximize financial returns with thefirst crude notions of asset allocation: government bonds forsafety and corporate stocks for return.

The collapse of the banking system during the depression andthe 25-year bear market (US stocks returned to 1928 values only
in 1953) led to a flight to the safety of “blue chip” stocks andgovernment bonds.
By the late 1960s, “money managers” began to construct
growth stock portfolios and ushered in the first econometricanalysis of securities prices and the notion of the meanvariance tradeoff in the allocation of funds across assets andsecurities within asset classes, as pioneered by Harry Markowitz,
to meet long-term financial goals. In the 1980s, the eliminationof fixed-price commissions for stock trades and the rise of
discount stockbrokers and the proliferation of mutual funds led
to a more scientific approach to saving for retirement. Thus,“wealth management” was born.
Today, wealth management is not only concerned withconstructing investment portfolios that generate excess “riskadjusted returns” but also to integrate across multiple domains
to preserve wealth, including insurance and tax. Wealthmanagers also need to effectively understand the goals andconcerns of their customers and are increasingly leveraging
techniques such as natural language processing to gauge theconcerns and sentiment of their clients.

8PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#6Technology and customer service
The adoption of communications and information processing
technology has transformed how banking services aredelivered while core banking needs, in line with human nature,remain constant.
The telephone reduced the need for in-person
communications at the bank building just as ATMs eliminatedthe need for a teller to disburse or accept funds. The
deployment of internet banking has allowed fully remotebanking service delivery for everything except currency. The
adoption of new technologies allows banking services to bedelivered more readily at a radically reduced cost and banks
are no longer constrained by physical proximity to theircustomers.
The radical decline in the marginal cost of providing banking
services and the surmounting of geographic barriers led to therapid consolidation of banks in the US and Europe, increasing
the size of the customer base and the scope of the services
provided.
In a sense, money is information, as it is only currency whenboth parties agree to its value.
By the beginning of the 21st century, banks had transformed
their ability to process information with a focus on technology
adoption and deployment. An indirect result of the adoptionof internet banking was the demand by consumers to export
their banking transactional data into formats that they canreview themselves, a process now largely accommodated byAPIs via vendors such as Plaid.

Consider that speech and text data from customer service
interactions is one of the only times a bank can hear exactly
what a customer wants and needs, directly from thecustomer. Leveraging this unstructured data through deeplearning and large language models offers a bank an
unprecedented opportunity to understand customer intent
and resolve issues more quickly, thus driving retention andexpansion from well-serviced customers.

9

PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

#7
MarketingIn the branch banking era, marketing meant advertising, and applications for new products
were solicited and taken at the branch by the branch manager. In the 1960s, the developmentof the travel and entertainment charge card led Bank of America in California to develop thebank credit card, which morphed into VISA. Merchants were quick to accept the new bankcharge card to reduce the necessity to handle currency and to deal with bad checks.
Moreover, merchants discovered that purchases with a bank credit card were for a higher dollar
amount than cash, a quirk of consumer behavior. Bank of America developed a strategy to
expand credit card holding within its large customer base and borrowed techniques for direct
mail solicitations from book publishers. The mass solicitation of bank credit cards was widelysuccessful - although new types of fraud surfaced via cards intercepted in the mail - and direct
response marketing continued to evolve. By the 1990s, monoline banks such as Capital One
were able to build very profitable operations through the innovative use of data and analytics toidentify consumers who were prone to “revolve” credit card balances at high interest rates.
The development of the enterprise data warehouse in the 1980s enabled the first customer
relationship management software and by the 1990s, banks developed product propensity
models that were used to generate leads for branch bankers and call center agents, as well as
to drive direct mail solicitations. At the turn of the 21st century, internet banking introduced theability to market directly to bank customers during routine banking activity as well as the ability tooriginate new accounts via applications in the digital banking channel. The product propensity,
service channel preference and sales channel response models developed by banks focused on
identifying the “next best action” to increase customer engagement and value.
Inspired by the sophisticated approaches taken within social media platforms like Facebook,banks can now develop and deploy their own deep learning recommendation models. The
ability to leverage a broader possible set of behavioral information about their customers allows
banks to pursue “hyper-personalization” and to realize the long-held vision of “one-to-one
marketing.”

10

PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

Speech & text
Throughout history, banking has always been primarily about
credit - the reliability of a man’s character where his “word ishis bond.” The deployment of the telegraph in the 1840s
radically accelerated financial innovation and the firsttransatlantic cables in the 1870s resulted in “arbitrage”
operations in New York and London to exploit information
asymmetries. The realization of the financial returns to thedisruption of banking by technology is not new; stock market
operator Jay Gould acted quickly to consolidate telegraphcompanies into what is now Western Union and control thedissemination of securities prices on Wall Street, akin to theposition of the Bloomberg terminal today.
Beginning at the end of the 20th century and accelerating in
the first three decades of the 21st century, retail customers are
no longer primarily served in the traditional branch channel but
rather via self-service technologies such as inbound phone
calls, email and SMS, and online banking, both desktop andmobile. Unlike in the past, customers don’t share their concerns
directly with branch bankers but rather identify their banking
tasks, concerns and aspirations via phone calls, emails, andtexts; this unstructured data is the most difficult for banks toprocess but contains the most valuable information –especially when conjoined with channel interactions data suchas digital banking weblogs.

Automated speech recognition (ASR) automaticallygenerates transcripts from inbound calls with astonishingvalidity. Natural language processing (NLP) can unlock thevalue of unstructured customer-generated speech and text.
Recent innovations in deep learning large language models
have radically improved the ability to generate actionableinsights from speech and text. SambaNova is working withbanking and financial services enterprises to support this typeof ‘language-as-a-service’ approach that leverages today’s
most innovative deep learning capabilities to unlock
previously unattainable value from the huge morasses of
unstructured data that are stored within banking enterprises.

11PART TWO: USING DEEP LEARNING MODELS TO CONQUER RISK IN THE UNIVERSAL BANK

Conclusion
The evolution of banking is tied up in the development of novel
methods to quantify and manage risk. Advances incommunication and data processing technologies have been
instrumental in surmounting the risks of banking and the need todevelop novel methods to interact with customers and satisfy
their banking needs. Today, we are witnessing the rapid
evolution from statistical models that require hand-curation ofdata to pretrained, domain-specific foundation models thatingest a broad and deep set of behavioral data. These deeplearning models more accurately identify the risks inherent inbanking (fraud, credit, operational) and propagate these
insights as signals that the bank can operationalize indownstream workflows such as credit risk assessment and pricing,fraud mitigation strategies, customer relationship management
and every dimension of investment securities selection andtrading.
The reality of banking in real-time across borders haschanged how consumers understand and use their financial
assets. It is incumbent on financial institutions to adopt anddeploy modern technology solutions to keep pace with
consumer expectations for speed and safety of digitaltransactions. By aligning with the expertise of subject matter
experts and technology leaders, banks can demonstrate not
only trustworthiness but insight and forward thinking as they
employ deep learning foundation models andrecommendation engines to deliver better experiences thanever before.

12

Next steps
To learn more, please visit SambaNova’s
Banking and Financial Services
solutions web page.

Learn more at SambaNova.ai
linkedin.com/company/sambanova@SambaNovaAI
info@sambanova.ai

Customers turn to SambaNova to quickly deploy state-of-the-art AI capabilities to meet the demands of the AI-enabled world. Our
purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing. We enable
customers to unlock the valuable business insights trapped in their data. Our flagship offering, Dataflow-as-a-ServiceTM, overcomesthe limitations of legacy technology to power the large complex foundation models that enable customers to discover new services
and revenue streams, and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems wa s foundedin 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include
SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline
Capital, Atlantic Bridge Ventures, Celesta, and several others.
Copyright © 2022 SambaNova Systems, Inc. All rights reserved.

13 13