Risk management in banking has evolved over the past century due to ideas, technology, and regulation. Analytical techniques are now used throughout the customer lifecycle to assess various risks like credit, market, and operational risk. Models help with tasks like calculating the probability of default, exposure at default, and loss given default. Analytics are also used for monitoring portfolios, performing stress tests and simulations, segmenting customers, and optimizing risk strategies. Emerging areas include using chatbots, biometrics, and regulatory technology to further improve risk management practices.

1. Risk Analytics in Banking
Past, Present and (a bit of) Future
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Mr. Sougata Deb
2 Aug 2019

2. Content
• Introduction
• A History of Thought
• The What Happeneds
• Where Analytics Fits
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3. Introduction
“Business people need to understand the psychology of risk more than the
mathematics of risk.”
- Paul Gibbons
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4. What’s Different in Banking
• The regular IPO process for any business
• The IPO process for Banking
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5. What’s Risk?
• Risk is the possibility of losing something of value.
• Risk may result from a given action or inaction
• More formally, in modern portfolio theory, the variance of a portfolio
is used as the definition of risk
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Deposit Lending
?

6. Overview
Risk management has evolved
continuously over the past century
• Financial institutions played a
defining role in world economic
growth
• Frequent disruptions in the form
of recessions and slowdowns
• Organizations learnt to identify,
measure, monitor and control their
borrowing and lending practices
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7. Key Drivers
Ideas, Technology and Regulation have
been the three major drivers of this
evolution
• Technological advancements to offer
more products
• Competition necessitated product
innovation
• Some innovations succeeded, some
did not - and at times triggered crisis
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8. A History of Thought
“Faced with the choice between changing one’s mind and proving that there is no
need to do so, almost everyone gets busy on the proof.”
- John Kenneth Galbraith
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9. 1900-1925 : Defining
Separation of Uncertainty and Risk, and the importance of judgment
• Frank Knight’s book Risk, Uncertainty and Profit, differentiated
between uncertainty (not measurable) from risk (measurable)
• John Maynard Keynes stressed the importance of relative perception
and judgment when determining probabilities in his book, A Treatise on
Probability
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10. 1926-1960 : Forming
The goal of not losing is superior to that of winning
• John von Neumann presented his first paper on
a theory of games
Relationship between Return and Variance
• Harry Markowitz proposed Modern Portfolio
Theory
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11. 1961-1990 : Storming / Norming
Enterprise Risk Management was conceptualized
• Gustav Hamilton created a Risk Management Circle
describing the interaction of elements like -
assessment, control, financing and communication
Fortune magazine published The Risk Management
Revolution
• Acknowledged the need for coordination of formerly
unconnected risk management functions
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12. 1991-2019 : Norming / Performing
Value-at-Risk was conceptualized
• The crash of 1987 showed fragility of
standard statistical models
Chief Risk Officer was born
• The title was first used by James Lam,
at GE Capital
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13. The What Happeneds
“All courses of action are risky, so prudence is not in avoiding danger. … Develop the
strength to do bold things, not the strength to suffer.”
- Niccolo Machiavelli
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14. 1900-1940
• 1920 : British Petroleum forms first captive
insurance company Tanker Insurance
• 1929 : The first great depression
• 1933 : The U.S. Congress passes the
Glass-Steagall Act
• 1933 : Securities and Exchange
Commission set up
• 1936 : Social Security Number (SSN) was
introduced
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15. 1941-1980
• 1950 : Credit Cards came into existence
• 1965 : The birth of consumer movement
• 1968 : Fair Housing Act introduced to
eliminate discrimination
• 1974 : Basel committee set up
• 1978 : Computerized Fair Lending data
collection systems introduced
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16. 1981-2019
• 1987 : Black Monday and the 2nd
Great Depression
• 1988 : Basel I Accord and Minimum
Capital Requirement (8%)
• 2004 : Basel II Accord – tiered capital
and risk weighted assets
• 2005 : New Bankruptcy Law introduced
• 2007 : Subprime Crisis
• 2010 : Basel III Accords were released
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17. Where Analytics Fits
“Every model is wrong, but some are useful.”
- George Box
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18. Major Types of Risk
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Financial Risks
Liquidity RiskCredit Risk Operational Risk Market Risk
Underwriting
Transaction
Collections
Other Risks
Fraud
People
Model
Legal
Natural Hazard
Terrorism
Weather
Infectious Disease

19. The Customer Lifecycle
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Source: https://www.slideshare.net/albahnsen/2011-advanced-analytics-through-the-credit-cycle

20. Analytics Areas
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Risk Management
Risk Modeling Risk Strategy
Regulatory Portfolio Segmentation Optimization Simulation A/B Testing
• Probability of
Default (PD)
• Exposure at
Default (EAD)
• Loss Given
Default (LGD)
• Application Score
• Behavior Score
• Fraud Score
• Bankruptcy Score
• Payment Score
• Collections Score
• Recovery Score
Monitoring & Validation

21. Delving Deeper into Scoring
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• Application Score
• Behavior Score
• Payment Score
• Collections Score
• Recovery Score
• Fraud Score
• Bankruptcy Score
Behavior Based Event Based
MoreInformation
Performance
Internal
External

22. Other (Semi-)Futuristic Use Cases
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• Making Chat-bots mainstream in customer interactions
• Digital Persons or virtual video-based assistants
• Seamless authentication using Biometrics
• RegTechs offering text-mining based regulatory compliance

23. Key Takeaways
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• Risk management is continuously evolving
• Complex interdependence among agencies drives the practices
• Analytics is used pretty much at every stage of customer lifecycle
• Name any type of analytics and you can find a use-case

24. Addendum: Must-have’s for an Analyst
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• Data analytics best practices: data cleaning and pre-processing; data
warehousing fundamentals
• Analytical modeling process: feature engineering; feature reduction
and selection techniques; model validation metrics & methodologies
• Data visualization and storytelling
• Statistics fundamentals: Hypothesis testing; Linear, Logistic and
common Generalized Linear Modeling techniques
• Other Statistical techniques: decision trees and random forest; design
of experiments

25. Thank You!
deb.sougata@u.nus.edu
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