Leuven
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  • the banking industry is a highly regulated industry with detailed and focused regulators Fast, fully adaptable, performance and accuracy Commercial Benefits Cost Reduction Investor Scale Negócio que irá permanecer com alta procura ROI Of the team An experienced team, where the whole is far greater than the sum of its parts
  • Boosting the accuracy of credit risk methodologies used by banks and financial institutions may lead to considerable gains. Default rate in Portugal has more than double in the past 5 years Similary in Europe NPL increase by over 25%, many as much as 50% 620 billion euros in 2009 For example, improving the accuracy of credit risk assessment models by only 1% may lead to a gain in banking sector of about 50 million Euros - in Portugal alone

Leuven Presentation Transcript

  • 1. Credit Risk with AI toolsThe old, the new and the unexpected ARMANDO VIEIRA Armandosvieira.wordpress.com
  • 2. RISK Customer fails to pay Change inLosing money marketWrong Strategy prices Processing failures and frauds Regulatory compliance
  • 3. Importance of Credit Risk
  • 4. What is Credit Scoring?A statistical means of providing a quantifiable risk factor for a given applicant.Credit scoring is a process whereby information provided is converted into numbers to arrive at a score.The objective is to forecast future performance from past behavior of clients (SME or individuals).Credit scoring are used in many areas of industries: Banking Decision Models Finance Insurance Retail Telecommunications
  • 5. Bankruptcy prediction problem• Predict financial distress of private companies one year ahead based on account balance sheet from previous years.• Enventualy the probability to become so.• Obtain reliable data from up to 5 previous years before failure• Classify and release warning signs
  • 6. The curse of dimensionalityProblems• Sparness of the search space• Presence of Irrelevant Features• Poor generalization of Learning Machine• Exceptions difficult to identifySolutions• Dimensionality reduction: feature selection• Constrain the complexity of the Learning Machine
  • 7. The Diane Database• Financial statements of French companies, initially of 60,000 industrial French companies, for the years of 2002 to 2006, with at least 10 employees• 3,000 were declared bankrupted in 2007 or presented a• restructuring plan 30 financial ratios which allow the description of firms in terms of the financial strength, liquidity, solvability, productivity of labor and capital, margins, net profitability and return on investment
  • 8. The inputsNumber of employees Net Current Assets/Turnover (days)Financial Debt / Capital Employed (%) Working Capital Needs / Turnover (%)Capital Employed / Fixed Assets Export (%)Depreciation of Tangible Assets (%) Value added per employeeWorking capital / current assets Total Assets / TurnoverCurrent ratio Operating Profit Margin (%)Liquidity ratio Net Profit Margin (%)Stock Turnover days Added Value Margin (%)Collection period Part of Employees (%)Credit Period Return on Capital Employed (%)Turnover per Employee Return on Total Assets (%)Interest / Turnover EBIT Margin (%)Debt Period (days) EBITDA Margin (%)Financial Debt / Equity (%) Cashflow / Turnover (%)Financial Debt / Cashflow Working Capital / Turnover (days)
  • 9. 6 Hard problem Class 0 Class 1 42λ 2 0 3 4 5 6 7 λ 1 First two principal component from PCA
  • 10. How HLVQ-C works1.5 After ? Class 0 Class 1 Before d21.0 Y d10.5 X 0 0 0.5 1.0 1.5
  • 11. DIANE 1 (error %) Model Error I Error II TotalMDA 26.4 21.0 23.7SVM 17.6 12.2 14.8MLP 25.7 13.1 19.4HLVQ-C 11.1 10.6 10.8
  • 12. DIANE 1 - HLVQC Results Classification Method Weighted Efficiency (%) Z-score (Altman) 62.7 Best Discriminant 66.1 MLP 71.4 Our Method 84.1Source: Vieira, A.S., Neves, J.C.: Improving Bankruptcy Prediction with Hidden LayerLearning. Vector Quantization. European Accounting Review, 15 (2), 253-271 (2006).
  • 13. Personal credit
  • 14. Results I – 30 days into arrears GClassifier Accuracy (%) Type I Type II 54.8Logistic 66.3 27.3 40.1 61.1MLP 67.5 8.1 57.1 52.3SVM 64.9 35.6 34.6 55.7AdaboostM1 69.0 12.6 49.4 52.3HLVQ-C 72.6 5.3 49.5
  • 15. Results I – 60 days into arrears GClassifier Accuracy Type I Type II 21.2Logistic 81.2 48.2 11.0 20.1MLP 82.3 57.4 9.1 19.3SVM 83.3 38.1 12.4 14.7AdaboostM1 84.1 45.7 8.0 11.9HLVQ-C 86.5 48.3 6.2
  • 16. DIANE II (2002 – 2007)• More data• Longer history• More features
  • 17. Results Classifier Accuracy Type I Type II Logistic 91.25 6.33 11.17Year2006 MLP 91.17 6.33 11.33 C-SVM 92.42 5.16 10.00 AdaboostM1 89.75 8.16 12.33 Classifier Accuracy Type I Type II Logistic 79.92 19.50 20.67Year MLP 75.83 24.50 23.832005 C-SVM 80.00 21.17 18.83 AdaboostM1 78.17 20.50 23.17
  • 18. How useful?η = NV [ x(1 − eI ) − (1 − x)eII m] x  eII  > mG > m 1− e   1− x  I 
  • 19. The Rating System
  • 20. French market - 2006
  • 21. Score (EBIT, Current ratio) 1 0.5 0 -0.5 -1 -1.5 2 1 0 -1 2 1 0 eb -2 -1 -2 cr
  • 22. MOGAMultiobjective Genetic Algorithms
  • 23. MOGA – feature selection
  • 24. S-ISOMAP – manifold learning
  • 25. The idea behind it
  • 26. Other approaches• SVM+ - domain knowledge SVMs• RVM – probabilistic SVMs• NMF – Non-negative Matrix Factorization• Genetic Programming•…
  • 27. The Power of Social Network Analysis
  • 28. Bad Rank Algorithm
  • 29. Where are the bad guys?
  • 30. Bad Rank for Fraud Detection
  • 31. Results with Semi-supervised Learning
  • 32. Networks Analysis A world of possibilities• Identify critical nodes / links / clusters• Detailed information of dynamics• Stability / robustness of system• Information / crisis Propagation• Stress tests
  • 33. Team Business Director of IT Researcher Marketing Director ResearchJoão Carvalho das Neves Armando Vieira Bernardete Ribeiro Tiago Marques Professor of Professor of Physics, & Associate Professor Marketing and Management, ISEG. entrepreneur. Ph.D. in of Computer Business Ph.D. in Business Physics and researcher Science, University Consultant, Administration, in Artificial Intelligence Coimbra, E-Business Manchester Business researcher at Specialist, School CISUC. 10+ years experience in AI 25 years experience in Credit Risk & Financial Analysis 15 years of marketing experience
  • 34. W do banks need in credit hat management?Efficiency Accuracy Savings of Capital – Basel requirementsThis is a highly regulated industry with detailed and focused regulators
  • 35. W do they get? hat Non-performing loans - Europe % Corporate Debt Default - 250 Portugal 4.5 4 2008 200 2009 3.5 3 Billions of EUR 150 NPL (%) 2.5 2 100 1.5 50 1 0.5 0 0 Germany UK Spain It aly Russia Greece 2005 2006 2007 2008 2009Source: Issue 2 of NP E L urope, a publication overing non-performing loan(NPL) markets in Europe and the United Kingdom (UK)., Source: Bank of PortugalPriceWaterhouseCoopers Boosting the accuracy of credit risk methodologies will lead to considerable gains for banks
  • 36. AIRES Solution
  • 37. AIRES.dei.uc.pt