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Federal Reserve/OCC Model Risk Management Guidance SR11-7/OCC 2011-12
Regulators & Wall Street CEOs, CFOs & CROs Catch Up with Model Risk Management Research "Dr. Yogesh Malhotra's research published years before the Global Financial Crisis of 2008 underscored imperative need for preempting and preventing systemic failures through 'anticipation of surprise' by effective challenge of Information Systems based 'predictive' risk management models. In the aftermath of the Financial Crisis, more than a decade later, top Wall Street investment banks and their financial regulators are increasingly cognizant about the importance of 'anticipation of surprise' by effective challenge of Information Systems based 'predictive' risk management models such as underscored in Federal Reserve/OCC Model Risk Guidance SR11-7/OCC 2011-12."
The 'Anticipation of Surprise' Framework: Anticipatory Risk Analytics
20 Years of Computational Quant Risk Analytics Leading Industry Leaders.
Download Research: Model Risk Management (MRM) Research Program.
Federal Reserve/OCC Model Risk Management Guidance SR11-7/OCC 2011-12.
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Over 20 Years of Computational Quantitative Anticipatory Risk Analytics…
Risk Models: Statistics, Finance, Econometrics, IT, OR, Computer Sc., Telecom, …
Risk Management & Controls: ERM, MRM, Assets, Markets, Exchanges, Networks, …
This site should help you parse perhaps the most important standard that your organization needs to comply with as required by the US Federal Reserve and OCC. The specific guidance is about Model Risk Management (MRM). It is often called SR11-7 and OCC 2011-12. Click on respective hyperlinks to see the specific standards.
Wall Street CEOs, CFOs, and CROs are recently saying: "We must anticipate risk!"
They are also expected to establish a process of "effective challenge of models"?
What is exactly 'anticipation of risk'? How do CEOs, CFOs, and CROs really 'do' so?
What exactly is 'effective challenge of models'? How do CxOs really 'do' so?
When top Finance CEOs and OCC say 'models are backward-looking' and 'future isnít the past'? What does that really mean? What are its implications for your enterprise? When top Finance CFOs say that their metric of success is 'no surprises', what do they really mean? When Basel guidance says that models stress testing requires 'anticipating risk' what does it really mean? When Fed and OCC Model Risk guidance emphasize 'corporate culture', 'incentives', and 'influence' for appropriate "effective challenge" of models, what does that really mean? How can CxOs really 'do' so?
What is the ‘Human Factor’ often attributed for large scale systems failures such as after the Global Financial Crisis and 80%-90% failure rates of BPR? Devoid of the Human Factor, BPR was characterized as 'the fad that forgot people'! Recent observations of world's most respected Finance Quants and Model Risk Management Pioneers such as Emanuel Derman and Paul Wilmott seem to rhyme with what we saw with BPR in the early-1990s.
How do you understand MRM (Model Risk Management) in the context of ERM (Enterprise Risk Management), SRM (Strategic Risk Management), and SyRM (Systemic Risk Management)? How do you integrate your Quantitative Analytics, Modeling, Risk Management, and other business processes within a coherent Dynamic Information Strategy, Risk Management & Controls Framework, and, IT Enterprise Architecture? How do People, Processes, and, Technologies interact to influence and determine Business Performance?
Now all those answers that you always sought but could not find are available in one coherent framework: here! Learn from world's greatest national and corporate leaders who have been doing so for a long time... We have learned from them and they have learnt from us. Not only have they applied what they learned very successfully, they have also shared about our research with worldwide audiences in their national constitutions, world policy documents, corporate strategy papers, industry interviews and keynotes, and, best-selling books.
Now read on the text below and the links interspersed in related text to see how we have been parsing Model Risk Management (MRM) standards SR11-7 and OCC 2011-12 coming from 20 years of Quantitative Analytics research focus originally grounded in Quantitative Risk Management and Controls Systems & Models. The linked documents are illustrative in nature. For greater grasp for execution of the 'anticipation of risk', 'effective challenge of models', and myriad of other activities related to Risk Management and Controls Systems & Models, you are welcome to apply our Model Risk Management Research Program maintained in public domain as a service to the worldwide communities of researchers and practitioners.
"Models by their nature are simplifications of reality, and real-world events may prove those simplifications inappropriate. This is even more of a concern if a model is used outside the environment for which it was designed. Banks may do this intentionally as they apply existing models to new products or markets, or inadvertently as market conditions or customer behavior changes. Decision makers need to understand the limitations of a model to avoid using it in ways that are not consistent with the original intent."
-- SR11-7/OCC 2011-12: Supervisory Guidance On Model Risk Management: Board of Governors of the Federal Reserve System & Office of the Comptroller of the Currency, April 4, 2011
"The new business model of the Information Age, however, is marked by fundamental, not incremental, change. Businesses can't plan long-term; instead, they must shift to a more flexible "anticipation-of-surprise" model."
-- Yogesh Malhotra in CIO Magazine interview, September 15, 1999.
"Bank losses in the recent financial crisis exceed levels observed in recent history! This illustrates the inherent limitations of backward looking models - we must anticipate risk."
-- Jacobs, M. Stress Testing Credit Risk Portfolios, U.S. Office of the Comptroller of the Currency, March 2012.
"A guiding principle for managing model risk is "effective challenge" of models, that is, critical analysis by objective, informed parties who can identify model limitations and assumptions and produce appropriate changes. Effective challenge depends on a combination of incentives, competence, and influence. Incentives to provide effective challenge to models are stronger when there is greater separation of that challenge from the model development process and when challenge is supported by well-designed compensation practices and corporate culture." MORE...
-- OCC 2011-12: Supervisory Guidance On Model Risk Management: Board of Governors of the Federal Reserve System & Office of the Comptroller of the Currency, April 4, 2011
"The very essence of what various IT systems can do in the context of KM begins and ends with people and processes. In absence of motivation and commitment on the part of the users, such systems cannot function."
-- Yogesh Malhotra in CIO Insight interview, July 1, 2004. MORE...
"Our metric of success is ‘no surprises’: no surprises in terms of the impact on the firm of any individual behaviour or outside event."
-- Barry Zubrow, Chief Risk Officer at JP Morgan Chase,quoted in: Bank Risk Manager of the Year: JP Morgan, Risk Magazine, 10 Jan 2012.
"Stress testing means (regular) expeditions into an unknown, but important territory: the land of unexpected events and losses. It requires anticipating risk which could, but need not arise in the future and results in the determination of possible unexpected losses."
-- Engelmann, B. and Rauhmeier, R., The Basel II Risk Parameters: Estimation, Validation, Stress Testing - with Applications to Loan Risk Management, Springer, 2011.
"A guiding principle throughout the guidance is that managing model risk involves "effective challenge" of models: critical analysis by objective, informed parties that can identify model limitations and produce appropriate changes."
-- SR 11-7, Supervisory Guidance on Model Risk Management, U.S. Board Of Governors of the Federal Reserve System & Office of the Comptroller of the Currency, April 4, 2011
"We at the NAIC, along with our fellow financial regulators at the federal and global levels, must evolve and improve the way we supervise our markets. We must continue our ongoing efforts to develop better structures and tools to help us anticipate risk."
-- National Association of Insurance Commissioners. Examining the Impact of the Proposed Rules To Implement Basel III Capital Standards, National Association of Insurance Commissioners Testimony to the U.S. House of Representatives, November 29, 2012
2015-2018: 63 SSRN Top-10 Research Rankings: Top-2% SSRN Authors:
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Recent Research Presentations and Research Reports
2018 Princeton FinTech & Quant Conference: Invited Research Presentation: AI-Machine Learning-Deep Learning MRM.
2018 MIT Sloan-Computer Sc. & AI Lab AI-Machine Learning Executive Guide including RPA & Cognitive Automation
2018 Journal of Operational Risk, March: Toward 'Cyber-Finance’ Cyber Risk Management Frameworks of Practice.
2017 National Association of Insurance Commissioners, June , Advancing Cyber Risk Insurance beyond VaR Models.
2017 IUP Journal of Computer Sciences, April, Quantitative Modeling of Trust and Trust Management Protocols.
Stress Testing for Cyber Risks: Cyber Risk Insurance Modeling beyond Value-at-Risk: Risk, Uncertainty, & Profit.
Toward Integrated Enterprise Risk Management, Model Risk Management & Cyber-Finance Risk Management.
Bridging Networks, Systems and Controls Frameworks for Cybersecurity Curricula & Standards Development.
Advancing Cognitive Analytics Using Quantum Computing for Next Generation Encryption.
Invited Princeton Quant Trading Presentations: 'Rethinking Finance' for the Era of Global Networked Digital Finance.
2016 Princeton Quant Trading Presentation: Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era.
2015 Princeton Quant Trading Presentation: Future of Finance Beyond 'Flash Boys': Managing Uncertainty.
Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to MRM.
A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks VoIP Protocols.
CyberFinance: Why Cybersecurity Risk Analytics Must Evolve to Survive 90% of Emerging Cyber Financial Threats.
Beyond 'Bayesian vs. VaR' Dilemma: How to Manage Risk (After Risk Management Has Failed) for Hedge Funds.
Measuring & Managing Financial Risks with Improved Alternatives Beyond Value-at-Risk (VaR).
Markov Chain Monte Carlo Models for High-Dimensionality Complex Stochastic Problems in Network Security.
Risk, Uncertainty, & Profit for the Cyber Era: 'Knight Reconsidered': Model Risk Management in Cyber Risk Insurance.
Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to Model Risk Management.
Number Field Sieve Cryptanalytic Algorithms for Most Efficient Prime Factorization on Composites: Beyond RSA 1024.
Future of Bitcoin & Statistical Probabilistic Quant Methods: Global Financial Regulation: Hong Kong Institute of CPAs.
Bitcoin Protocol & Bitcoin Block Chain: Model of 'Cryptographic Proof' based Global Crypto-Currency Payment Systems.
2015-2018 63 SSRN Top-10 Rankings: Computational Quant & Risk Analytics Algorithms Machine Learning Research.
2008 AACSB International Impact of Research Report: Among Black-Scholes, Markowitz, Sharpe, Modigliani & Miller.
Top Wall Street Investment Banks Quantitative Finance Projects & FinTech Ventures
• Princeton: Future of Finance: 'Rethinking Finance' for Era of Global Networked Digital Finance
• 2017 National Association of Insurance Commissioners: Expert Paper: Cyber Risk Insurance Modeling
• 2016 Princeton Quant Trading Conference: Invited Research Presentation: Model Risk Arbitrage
• 2015 Princeton Quant Trading Conference: Invited Research Presentations: Future of Finance
• Quantitative Finance Risk Analytics Modeling Wall Street Investment Banks & VC Projects
• Model Risk Management: Risk Management Analytics from 'Prediction' to 'Anticipation of Risk'
• Quantitative Finance Risk Analytics, Econometric Analytics, Numerical Programming Models
• Quantitative Finance Model Risk Management for Systemic-Tail Risks in Cyber Risk Insurance
• JP Morgan Portfolio Optimization, VaR & Stress Testing: 17-Asset Class Portfolio
• JP Morgan Portfolio Liquidity Risk Modeling Framework for $500-600Bn Portfolio
• Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis
• Goldman Sachs Alumnus Asset Manager Large-Scale Data High Freq Econometric Models
• Quantitative Finance, Risk Modeling, Econometric Modeling, Numerical Programming
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Named among FinTech Finance & IT Nobel laureates for Real World Impact of Research
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• AACSB International Reports Impact of Research among Black-Scholes, Markowitz, Sharpe
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• 63 SSRN Top-10 Rankings: AI, Algorithms & Machine Learning; Cybersecurity; Computational Quant Finance
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• Thesis on Ongoing Convergence of Financial Risk Management & Cyber Risk Management
• U.S. Federal Reserve & Office of the Comptroller of the Currency Model Risk Guidance
• Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis
• Markov Chain Monte Carlo Models & Algorithms to Enable Bayesian Inference Modeling
• OCC Notes Cybersecurity Risk & Cyber Attacks as Key Contributor to Banks' Financial Risk
• Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Global Financial Regulation
• Models Validation Expert Panels: IT, Operations Research, Economics, Computer Science
Global, National, & Enterprise CxO Level FinTech-Cyber-Risk Analytics Ventures
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• CxO Keynotes: Conference Board, Silicon Valley, UN, World Economy: Uncertainty & Risk
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• The Griffiss Cyberspace Cybersecurity Venture Spans Wall Street and Hi-Tech Research
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