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Credit Scoring Development and Methods James Marinopoulos Head of Retail Decision Model
Alan Greenspan: President, Federal Reserve Board May 1996 ,[object Object]
Risk Families We are managing different groups of Risk
Retail Decision Models Responsibilities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RDM Structure and Responsibilities Relationship Developments Change Requests Systems Ongoing Validations Monitoring Data Analysis
Presentation Topics Scorecard Modelling Business Objectives World Banks Monitoring Future Direction Overview of scoring
What is credit scoring? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Application Scoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Credit Decision
Behavioural Scoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Risk Grading Debit $1344. 12 Debit $234. 01 Debit $987.56 Debit $6543.22 Debit $32423.11 Total $2556.00 Debit $1344. 12 Debit $234. 01 Debit $987.56 Debit $6543.22 Debit $32423.11 Total $2556.00 Debit $1344. 12 Debit $234. 01 Debit $987.56 Debit $6543.22 Debit $32423.11 Total $2556.00
Sample scorecard characteristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scorecard points (example) Residential status Owner Renter LWP/Other +25 -30 +10 Time in employment (years) <2 3-4 5-6 7+ 2 10 15 25 Total monthly income 0 <$500 <$1000 <$1500 <$2000 <$3000 >$3000 0 15 25 31 37 43 48 Total defaults No Defaults 1 2+ 0 -70 -250
Other Types of Scoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Topics Overview of scoring Business Objectives World Banks Monitoring Future Direction Scorecard Modelling
Good/Bad Odds ,[object Object],[object Object],[object Object],[object Object],[object Object]
'Good/Bad' Discrimination ,[object Object],[object Object],[object Object],[object Object]
Performance Charts ,[object Object],0 40 80 120 160 200 240 280 320 360 400 440 480 520 560 600 640 680 720 760 800 Score Number Of Clients Goods Bads 8 1 Graph 2 - Log Odds Performance Chart 0 5 25 128 645 3250 16400 0 40 80 120 160 200 240 280 320 360 400 440 480 520 560 600 640 680 720 760 800 Good/Bad Odds 0 2 4 6 8 10 12 14 Log GBOs (Base 2) 8 to 1 2 to 1 3
Application Scorecard Construction Flow Chart ,[object Object],[object Object],[object Object],Statistical Analysis Customised Scorecard ,[object Object],[object Object],[object Object],[object Object],Data Integrity Set cut-off Score Implementation Validation Generic Scorecard ,[object Object],[object Object],Outsourcing Scorecard Monitoring
Model Build ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Models ,[object Object],[object Object],[object Object],[object Object],[object Object]
Model Build ,[object Object]
Logistic Regression ,[object Object]
Reject Inference and Validation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measures of discrimination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Gini. xls
Measures of discrimination – (I) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Gini=0.62%
Measures of discrimination –(II) ,[object Object],[object Object],[object Object],Based on a book by Solomon Kullback “ Information Theory and Statistics”
Issues for Successful Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Topics Overview of scoring Scorecard Modelling World Banks Monitoring Future Direction Business Objectives
Business Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Business Objectives (cont) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Topics Overview of scoring Scorecard Modelling Business Objectives Monitoring Future Direction World Banks
World Banks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
World Banks UK Banks AUS Banks
Bureaus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Credit Scoring & Bureaus Around the World “We are not alone!” B B B B B B B
BASEL - The New Accord ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Paul%20Russell%2013a[1] The New Basel Capital Accord Pillar 1 : Minimum capital requirement Pillar 2 : Supervisory review process Pillar 3 : Market discipline
Pillar 1 : credit risk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future direction of scoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Topics Overview of scoring Scorecard Modelling Business Objectives World Banks Future Direction Monitoring
Monitoring Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Loan Approval/Declines by Score Approva/Declinal Rates by Score 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <=500 501-550 551-600 601-650 651-700 701-750 751-800 801-850 851-900 901-950 951-1000 >1000 Score Bands Percentages Auto Declined Manually Declined Manually Approved Auto Approved
Population Stability ,[object Object],[object Object],[object Object],[object Object],[object Object],NO YES Dec-96 25% 75% Mar-97 23% 77% Jun-97 24% 76% Sep-97 22% 78% Dec-97 21% 79% Mar-98 19% 81% Jun-98 19% 81% Sep-98 22% 78% Dec-98 20% 80% Mar-99 20% 80% Jun-99 18% 82% Sep-99 18% 82% Dec-99 17% 83% Benchmarks 29% 71% Population Stability 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% NO YES Dec-96 Mar-97 Jun-97 Sep-97 Dec-97 Mar-98 Jun-98 Sep-98 Dec-98 Mar-99 Jun-99 Sep-99 Dec-99
Monitoring Requirements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Loans - Approval & Delinquency Rates ,[object Object],Loans Approval & Delinquency Rates 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1-300 301- 350 351- 400 401- 450 451- 500 501- 550 551- 600 601- 650 651- 700 701- 750 751- 800 >800 Score Approval Rates 0% 5% 10% 15% 20% 25% Delinquency Rates % Approved (LHS) Delinquency Rates (RHS)
Scorecard Performance ,[object Object],[object Object],Score Distribution & G/B Odds 0 500 1000 1500 2000 2500 3000 3500 4000 <=500 501-550 551-600 601-650 651-700 701-750 751-800 801-850 851-900 901-950 951-1000 >1000 Score 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 Non Delinq Delinq HL GB Odds
Presentation Topics Overview of scoring Scorecard Modelling Business Objectives World Banks Monitoring Future Direction
Future Direction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conferences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Three Portfolio Dimensions: Volume, Loss, and Profit low high high E [ Profit ] E [ Volume ] E [ Losses ] Low cutoffs High cutoffs
Efficient Frontiers in two dimensions OP High Cutoffs E[Volume] E[Loss] Low Cutoffs 0.6 0.0 0.2 Low Cutoffs High Cutoffs E[Profit] E[Loss] OP 0.9 0.6 0.0 0.2 0.6 High Cutoffs Low Cutoffs OP E[Volume] E[Profit] 0.6 0.2 0.2 0.9 Efficient Frontier
Improved portfolio performance OP High Cutoffs E[Volume] E[Loss] Low Cutoffs 0.6 0.0 0.2 Low Cutoffs High Cutoffs E[Profit] E[Loss] OP 0.9 0.6 0.0 0.2 0.6 High Cutoffs Low Cutoffs OP E[Volume] E[Profit] 0.6 0.2 0.2 0.9 Single Score Combined Scores Single Score Combined Scores Single Score Combined Scores Efficient Frontier
Best Practices ,[object Object],Reject set with combined scores Accept set with combined scores s t Equal- odds line  c ( s, t )
Other Techniques ,[object Object],[object Object],[object Object],Proportional Hazards.ppt Measuring Customer Quality.doc

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Creditscore

  • 1. Credit Scoring Development and Methods James Marinopoulos Head of Retail Decision Model
  • 2.
  • 3. Risk Families We are managing different groups of Risk
  • 4.
  • 5. RDM Structure and Responsibilities Relationship Developments Change Requests Systems Ongoing Validations Monitoring Data Analysis
  • 6. Presentation Topics Scorecard Modelling Business Objectives World Banks Monitoring Future Direction Overview of scoring
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. Scorecard points (example) Residential status Owner Renter LWP/Other +25 -30 +10 Time in employment (years) <2 3-4 5-6 7+ 2 10 15 25 Total monthly income 0 <$500 <$1000 <$1500 <$2000 <$3000 >$3000 0 15 25 31 37 43 48 Total defaults No Defaults 1 2+ 0 -70 -250
  • 12.
  • 13. Presentation Topics Overview of scoring Business Objectives World Banks Monitoring Future Direction Scorecard Modelling
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27. Presentation Topics Overview of scoring Scorecard Modelling World Banks Monitoring Future Direction Business Objectives
  • 28.
  • 29.
  • 30. Presentation Topics Overview of scoring Scorecard Modelling Business Objectives Monitoring Future Direction World Banks
  • 31.
  • 32. World Banks UK Banks AUS Banks
  • 33.
  • 34. Credit Scoring & Bureaus Around the World “We are not alone!” B B B B B B B
  • 35.
  • 36.
  • 37.
  • 38. Presentation Topics Overview of scoring Scorecard Modelling Business Objectives World Banks Future Direction Monitoring
  • 39.
  • 40. Loan Approval/Declines by Score Approva/Declinal Rates by Score 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% <=500 501-550 551-600 601-650 651-700 701-750 751-800 801-850 851-900 901-950 951-1000 >1000 Score Bands Percentages Auto Declined Manually Declined Manually Approved Auto Approved
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. Presentation Topics Overview of scoring Scorecard Modelling Business Objectives World Banks Monitoring Future Direction
  • 46.
  • 47.
  • 48. Three Portfolio Dimensions: Volume, Loss, and Profit low high high E [ Profit ] E [ Volume ] E [ Losses ] Low cutoffs High cutoffs
  • 49. Efficient Frontiers in two dimensions OP High Cutoffs E[Volume] E[Loss] Low Cutoffs 0.6 0.0 0.2 Low Cutoffs High Cutoffs E[Profit] E[Loss] OP 0.9 0.6 0.0 0.2 0.6 High Cutoffs Low Cutoffs OP E[Volume] E[Profit] 0.6 0.2 0.2 0.9 Efficient Frontier
  • 50. Improved portfolio performance OP High Cutoffs E[Volume] E[Loss] Low Cutoffs 0.6 0.0 0.2 Low Cutoffs High Cutoffs E[Profit] E[Loss] OP 0.9 0.6 0.0 0.2 0.6 High Cutoffs Low Cutoffs OP E[Volume] E[Profit] 0.6 0.2 0.2 0.9 Single Score Combined Scores Single Score Combined Scores Single Score Combined Scores Efficient Frontier
  • 51.
  • 52.