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The Role of Data Science in Enterprise Risk Management, Presented by John Liu

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Originally presented at the 2014 Nashville Analytics Summit

Published in: Data & Analytics

The Role of Data Science in Enterprise Risk Management, Presented by John Liu

  1. 1. The Role of Data Science in Enterprise Risk Management By John Liu, PhD, CFA
  2. 2. Question of the Day ¡ How do you tell the difference between a Bayesian Statistician and Data Scientist? ¡ Answer: What’s the p-value?
  3. 3. Big Data: Big Risks ¡ Healthcare ¡ Financial Services ¡ Insurance ¡ Transportation ¡ National Security ¡ Dating
  4. 4. Key Takeaways ¡ What is Enterprise Risk Management (ERM)? ¡ What is the Role of Data Science in ERM? ¡ What Data Analytics are used for ERM?
  5. 5. What is Enterprise Risk Management?
  6. 6. What is Risk Management? ¡ A structured approach to manage uncertainty ¡ Management strategies: Risk Avoidance Risk Transfer Risk Mitigation
  7. 7. Risk Management - Defense Insurance Approach Reward Do Nothing Probability of Success
  8. 8. Risk Management - Offense Opportunistic Approach Reward Carpe Diem Do Nothing Probability of Success
  9. 9. What is ERM? ¡ Risk-based approach to managing an enterprise ¡ Risk-aware: every major tangible and intangible factor contributing toward failure in every process at every level of the enterprise ¡ Enterprise value maximized with optimal balance between profitability/growth and related risks ¡ Management better prepared to seize opportunities for growth and value creation
  10. 10. ERM Components Identify Quantify Respond Monitor & Report Effectiveness Monitor Comprehensive Approach To Managing Uncertainty Identify/Assess Internal and External Risks Risk Scoring & Modeling Respond and Control
  11. 11. ERM Goals ¡ Provide holistic view across an organization leveraging firm experience and knowledge ¡ Provide greater transparency to factors that can impair value preservation and business profitability ¡ Understand & test assumptions & interpretations in business decision-making
  12. 12. ERM Risk Types ¡ • Resource Capital Management • Business Disruption, IT Operational • Credit Exposure • Exchange Rate, Cash flow, Funding Financial • Privacy, Security, Safety • Regulatory and Statutory Compliance • Financial Reporting • Regulatory Reporting Reporting • Natural Catastrophe • Market Panics Hazard • Business Planning • Marketing, Reputation Strategic
  13. 13. RM vs ERM HQ: EUR exposure Subsidiary: USD exposure Sells EUR, Buys USD Sells USD, Buys EUR RM: Subsidiaries/Business Units manage risks separately ERM: Manage NET exposure across entire enterprise
  14. 14. Data Science and ERM
  15. 15. ERM Framework Enterprise Structure, Risks Objectives & Components Compliance Financial Compliance Reporting Hazard Strategic Entity Wide Division Business Unit Comprehensive Approach Leverage Data & Analytic Resources Predictive Modeling
  16. 16. Common Challenges ¡ Data warehousing & sharing across entity ¡ Prioritization methodology ¡ Consolidated reporting ¡ Timeliness ¡ Data security ¡ The risk management process itself!
  17. 17. Role of Data Science ¡ Data science methods provide: ¡ Enterprise Data Management ¡ Comprehensive warehousing ¡ Data quality and abundance ¡ Risk Analytics ¡ Predictive Modeling ¡ Loss Distributions ¡ Reporting ¡ Real-time visualization, dashboards ¡ Regulatory requirements Reporting
  18. 18. Typical Corporate EDW ¡ Big data warehouse ≠ useful data (quite the opposite)
  19. 19. Data Management ¡ Comprehensive data warehouse ¡ Coherent data collection (maybe) ¡ Facilitate data sharing across entity ¡ No useful analytics without abundant, high quality data Data Big Data Excel BigTable PostgreSQL Cassandra, HIVE, HBase MongoDB Vertica, KDB
  20. 20. Risk Analytics ¡ Benefits beyond Business Intelligence Descriptive Analytics Predictive Analytics Prescriptive Analytics What happened? What’s likely to occur? Why would it occur? Hindsight Foresight Insight Summary Statistics Data mining Heuristic Optimization web analytics, BI, credit scoring, trend operations planning, inventory reporting analysis, sentiment stochastic methods ¡ Newest: cognitive analytics = What is the best answer?
  21. 21. Rich Set of Visualization & Reporting Tools Aggregate Risk Dashboards Continuous & Comprehensive Risk Monitors Source: IBM Cognos
  22. 22. Data Analytics Applications for ERM ¡ • Scenario Analysis Operational & Stress Testing Financial • Credit Scoring Compliance • IT Security Anomaly Detection Reporting • Risk Dashboard Hazard • Catastrophe & Market Risk Hedging Strategic • Marketing Analytics
  23. 23. Data Analytics for ERM
  24. 24. Definition of Risk ¡ Risk = Frequency of Loss x Severity of Loss ¡ Loss Distribution Unexpected Loss
  25. 25. Traditional ERM ¡ Analytic Methods ¡ Closed-form solutions (…just like most things in life) ¡ Historical ¡ Estimate risk using internal and external loss data ¡ Monte Carlo ¡ Estimate distribution parameters from real data ¡ Monte-Carlo sample distribution ¡ Calculate ensemble measures to estimate overall risk ¡ Simple to implement, aggregate across entity, but make complex assumptions, not robust to outliers
  26. 26. Modern ERM ¡ Data analytics driven ¡ Inference based methods ¡ KRI scoring ¡ Parallelization ¡ Natural applications ¡ credit risk scoring ¡ Anti-money laundering ¡ Fraud
  27. 27. Prediction Methods Methods Transduction Tail Bayesian Frequentist Extreme-Value Expected Deficit Naïve Bayes HMMs Bayes Nets Regression, Decision Trees SVM Ensemble Methods Bagging, Boosting, Voting
  28. 28. Outliers, Inliers, and Just Plain Liars ¡ Prediction problems fall in two classes: Inliers Outliers Inherently different problems with different quirks
  29. 29. Main Problems with Inlier Prediction ¡ Parametric model choice ¡ Estimation error for lower moments (mean, s.d.) ¡ Incorrectly conjugating priors ¡ Normal/Gaussian distributions don’t really occur in real life ¡ I.I.D.? Really?
  30. 30. Main Problem with Outlier Prediction ¡ Data Quality and Abundance ¡ To estimate low probability events, big data may not be big enough Data: 150 years of daily data Predictor: 100 year flood severity Relevant Data: 1 or 2 data points
  31. 31. Value-at-Risk (VaR) ¡ Loss severity measure for a given probability and time horizon • Estimate potential losses (or historical losses) • Rank losses based on severity • 95% Value-at-Risk is equal to the 95th percentile loss • Interpretation = Losses won’t exceed 65.2m 95% of time • Underestimates losses during the other 5% of time Rank Loss 1 -­‐0.1 2 -­‐0.1 3 -­‐0.3 4 -­‐0.6 5 -­‐0.7 6 -­‐0.9 7 -­‐1.1 … … 91 -­‐59.5 92 -­‐63.2 93 -­‐64.9 94 -­‐65.0 95 -­‐65.2 96 -­‐66.5 97 -­‐67.8 98 -­‐93.9 99 -­‐110.0 100 -­‐273.1 VaR
  32. 32. Value-at-Risk ¡ Loss severity measure for a given probability and time horizon 1-day 95% VaR of $1m Expect to lose no more than $1m in 95 out of every 100 days Says nothing about the other 5 days out of 100. Not very reassuring, is it?
  33. 33. Tail Value-at-Risk (TVaR) ¡ Loss severity measure for a given probability and time horizon • Estimate potential losses (or historical losses) • Rank losses based on severity • 95% Tail Value-at-Risk is equal to average of all losses beyond 95th percentile loss • Expect to lose on average $122m if losses exceed the 95th percentile Rank Loss 1 -­‐0.1 2 -­‐0.1 3 -­‐0.3 4 -­‐0.6 5 -­‐0.7 6 -­‐0.9 7 -­‐1.1 … … 91 -­‐59.5 92 -­‐63.2 93 -­‐64.9 94 -­‐65.0 95 -­‐65.2 96 -­‐66.5 97 -­‐67.8 98 -­‐93.9 99 -­‐110.0 100 -­‐273.1 TVaR
  34. 34. Tail Value-at-Risk (TVaR) ¡ Loss severity measure for a given probability and time horizon 1-day 95% TVaR of $122m Better Measure of Risk Also known as Expected Shortfall, CVaR
  35. 35. Application: Operational Risk Management ¡ Definition: The risk of direct and indirect loss resulting from inadequate or failed: ¡ Internal processes ¡ People ¡ IT systems ¡ External events Source: NYFed Operational Risk External Criminal Activity Information security failure Internal Criminal Unauthorized Activity Activity Processing Failure System Failure Control Failure Business Disruption Workplace Safety Malpractice
  36. 36. Managing OpRisk ¡ One Approach Source: NYFed Assess Scorecard Internal Loss Data Identify Weakness Risk Scenarios Risk Model OpVar Risk Capital
  37. 37. Methods ¡ Scorecard 3 5 9 ¡ KRI scoring models 2 3 5 ¡ Useful where no severity data exists 1 2 3 Loss Distribution Impact ¡ ¡ Estimation of severity distribution parameters ¡ MLE Not robust – data not i.i.d., biased upwards, subject to Probability data paucity & sparsity ¡ Leads to biased loss exposures and correlation assumptions ¡ Huge opportunity for inference-based analytics
  38. 38. Looking Forward
  39. 39. ERM Trends Source: NCSU ¡ Increasing adoption of ERM
  40. 40. Forensic Data Analytics Fraud Detection Top Concern But Low Adoption. Source: Ernst & Young
  41. 41. Promise of Data Analytics ¡ EDW remains a huge issue for most corporations ¡ Legacy zombie systems ¡ IT reporting lines ¡ Increased understanding by senior managers and C-suite ¡ Analytics as a Service: growing competition within consulting industry ¡ Talent Gap – same for anything Data Science
  42. 42. Thank you

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