NUS-ISS Learning Day 2019- Risk Analytics in Banking: Past, Present and (a bit of) Future
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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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
