Credit Scoring. from Concept to Reality. Credit & Collections Conference Boston: June 11 th, 2007

Transcription

1 Credit Scoring from Concept to Reality Credit & Collections Conference Boston: June 11 th, 2007

2 2 Agenda 1) Developing & Launching the Credit Scoring Plan Tom Kritzer Navistar Financial Corporation 2) Crunching the Numbers Tom Ware PayNet Analytical Services 3) Making It a Reality Bill Gillin GE Capital Solutions

3 3 Developing & Launching the Credit Scoring Plan Tom Kritzer Navistar Financial Corporation

4 The Vision 4 - Understanding the Portfolio! - Segmentation, Performance, SG&A, etc. - Automation: the end game - Leverage Staff / Better Control of Buying Practices - Preparation for the Future - Transparency, Regulation, Compliance

5 Pooled vs. Custom vs. Hybrid Models 5 Pooled-Data Scorecard Custom Scorecard Hybrid "Custom-Pooled" Scorecard Size of Data Sample for Model Building Applicability to Your Unique Situation Ability to Benefit from Types of Data Only You Have Very Large Varies from Good to Fair No Direct Method (Can Build Rules and/or Matrix) Varies from Medium to Small Excellent Excellent Very Large (in most respects) Excellent Excellent Cost Low Medium - High Medium - Low PREDICITIVENESS COST Off-the-rack suit, unaltered Custom-tailored suit (but lacking benefits of large scale production) Off-the-rack suit, altered by a tailor for optimum fit

6 Pooled vs. Custom vs. Hybrid Models (blue/green) (yellow/green) (all colors) 6 Breadth of Data Types Data Unique to Your Institution (e.g."program") Your Institution Breadth of Lending Institutions

7 Choosing Your Partners 3 Rules 7 Model development firm should: 1) Know your industry, having done prior models for your segments and equipment types Credit score development is both Art & Science, the modeler who knows more about the industry will be better able to find meaningful and predictive segmentations and variables: - And know to look for things like seasonal patterns in construction lending but only in Northern states - Or that in truck lending, more trucks is better, except that 2 or 3 is often worse than 1 (unless it s s medium duty)

8 Choosing Your Partners 3 Rules 8 Model development firm should: 2) Have an Interactive Style taking extensive time to collaborate with your Subject Matter Experts not just take your data file and then present you with a finished model Your staff knows your business, and can help guide the modeler to find the most meaningful and predictive nuances if the modeler cares to listen Fundamentally credit scoring is all about systematically capturing and structuring knowledge, and while the expected relationships need to be quantitatively verified, they need to first be identified to be tested

9 Choosing Your Partners 3 Rules 9 Model development firm should: 3) Be willing and able to provide a full range of score-related related services and consulting to help you implement and manage your scoring, both upfront and on an on-going basis The credit score is great, but where should the score cut-offs be? Up to what dollar amount? What review rules should be in place as safeguards? What default and loss rates will we have given our applicant population and score cut-offs? And how should scoring be managed over time? What monitoring is necessary? Did the score perform as expected? When is it time to rebuild?

10 Choosing Your Partners Firm Type 10 Pros: Independent Score Developer - No question of conflict of interest - Broader understanding of data sources Data-Affiliated Score Developer - Better understanding of own data - Better understanding of own scores - Focused on long-term relationship Cons: - Relationship is usually just one project - Could face a conflict of interest - Less understanding of best data source If unsure of best data source for your institution, lean toward Independent but if fairly confident one source is best for you, lean toward Data-Affiliated (assuming you have confidence in their integrity) Confirm developer can work with any bureau s data

11 Gathering & Managing the Data For Now 11 Data capture in the past often incomplete Special IT processing often required to gather data for model build May require research to fully understand nuances of coding schemes in the past May have to go to bureaus to get past data that might have been retained internally Any model developer will tell you: gathering ones own data takes longer than you think

12 Gathering & Managing the Data For the Future 12 Model development time is ideal time to put in place systems to capture data for the next model build Be careful not to fall into circular trap of: we didn t t get this field in the past, so it s s not in our new model, so why capture it if it s s not in the model? or you ll never get it

13 13 Crunching the Numbers Tom Ware PayNet Analytical Services

14 Comparing Different Scores 14 When choosing a Pooled-Data Score to use, or when making a preliminary decision as to what score(s) ) a Hybrid Score should be developed on, the most fundamental question is: - Which is better, Score A or Score B? The Lorenz/ROC Curve is the Ultimate Measure

15 Lorenz Curve Example 15 Score A Score B Random Score A All Deals Bad Deals Cumulative % Cumulative % Cumulative % All Bad All Bad All Bad All Bad All Bad Score A Random Deals Deals Deals Deals Deals Deals Deals Deals Deals Deals % 40% % 20% 1/10th % 10% % 70% % 40% 1/10th % 20% % 80% % 50% 1/10th % 30% = 70% approval rate % 90% % 60% 1/10th % 40% % 90% % 70% 1/10th % 50% % 90% % 80% 1/10th % 60% % 100% % 80% 1/10th % 70% % 100% % 90% 1/10th % 80% % 100% % 90% 1/10th % 90% % 100% % 100% 1/10th % 100% TOTAL TOTAL TOTAL BOOKED 70 2 Bad% = 2.9% BOOKED 70 5 Bad% = 7.1% BOOKED 70 7 Bad% = 10.0%

16 Lorenz Curve Example Cumulative Percentages % 90% 80% 70% Defaults go from 5 to 2, a 60% reduction Score A Score B Bad Deals 60% Approvals go from 50% 70 to 87, a 24% increase 40% 30% Random 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% lowest scoring =70% Applications highest scoring Approval Rate

17 Lorenz Curve Example The Perfect Score % 90% 80% 70% The Perfect Score The Bad deals (10% of the population in this example) all have lower scores than the Good deals so a 90% approval rate will approve all the Goods, and decline all the Bads Bad Deals 60% 50% 40% Random 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% lowest scoring Applications highest scoring

18 Comparing Different Scores 18 Two common metrics are used to summarize these curves ROC Area and K-S* K While these are convenient statistics, be aware that the curves themselves are more important - Because the stats are just ways to summarize - And where your institution operates on the curve (high, medium or low approval rate) is where the lift really matters for your institution * (Kolmogorov( Kolmogorov-Smirnoff)

19 Developing & Launching the Credit Scoring Plan 19 To explain these stats we must first look at the slight difference between the Lorenz Curve and the ROC Curve

20 Lorenz Curve vs. ROC Curve 20 Lorenz Curve ROC Curve 100% 100% 90% 90% 80% Score A Score B 80% Score A Score B 70% 70% Bad Deals 60% 50% 60% 50% 40% Random 40% Random 30% 30% 20% 20% 10% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Applications 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Good Deals Only

21 Lorenz Curve vs. ROC Curve The Perfect Score 21 Lorenz Curve ROC Curve 100% 100% The Perfect Score The Perfect Score 90% 90% 80% 80% 70% 70% Bad Deals 60% 50% 60% 50% 40% Random 40% Random 30% 30% 20% 20% 10% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Applications 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Good Deals Only

22 The ROC Area % 90% Score A 80% 70% 60% Bad Deals 50% 40% Random ROC AREA 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Good Deals Only

23 The K-S Statistic % 90% 80% 70% 60% Score A K-S Bad Deals 50% 40% Random 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Good Deals Only

24 Combining Different Scores 24 Knowing which one score is the best by itself is useful and sometimes all you need to know but combining different scores is almost always more powerful But how much more powerful? And which combinations? The more different the data, the more powerful the combination so adding a consumer score to a commercial score is likely to add more lift than adding a second commercial score

25 Combining Different Scores 25 But, it still may be worthwhile to use multiple commercial bureaus for larger and/or riskier transactions And it certainly makes sense to build a model that tries a second commercial bureau when the first bureau is a no-hit

26 Combining Different Scores 26 In building any credit score, it is very important that the build population mirror the implementation population so: If the blue score (below) is first-pull, and the red score will only be pulled when blue is a no-hit, then the blue scorecard should be built on population A and B, but the red should be built just on C A B C

27 Combining Different Scores 27 For those institutions that don t t want to build a Custom or Hybrid model, but that do want to intelligently and scientifically combine different existing Pooled-data data scores, there is a fairly easy way to do it: the Joint Odds Table

28 Combining Different Scores Joint Odds Table 28 The Other Score to combine with the first The PayNet Rating % 100 1% 2% 3% 5% 9% 90 2% 90 1% 2% 3% 4% 5% 10% The 80 3% 80 1% 2% 3% 4% 5% 6% 11% Score 70 4% 70 1% 2% 3% 4% 5% 6% 7% 11% You 60 5% 60 1% 2% 3% 4% 5% 6% 7% 8% 11% Currently 50 6% 50 1% 2% 3% 4% 5% 6% 7% 8% 10% 12% Use 40 7% 40 2% 3% 4% 5% 6% 7% 8% 10% 12% 30 8% 30 3% 4% 5% 6% 7% 8% 10% 12% 20 10% 20 5% 5% 6% 7% 8% 10% 12% 10 12% 10 9% 10% 11% 11% 11% 12% Italics % = Probability of Default % in italics = Default Rate

29 29 Making it a Reality Bill Gillin GE Capital Solutions

30 Making it a Reality 30 (Implementation 101) When does implementation begin? What does implementation entail? What are some common pitfalls to avoid

31 When does implementation begin? 31 Partnering with the modelers Dual path with concurrent timelines Accelerating user buy-in early on

32 What does implementation entail? 32 Managing Through IT Constraints The Human Factor: Getting Buy-in Roll-Out & Phasing In On-going Score Management Issues

33 33 Managing Through IT Constraints Ensuring data accuracy and data derivation Understanding nuances between modeling and transactional data Right Sizing the testing effort

34 The Human Factor : Getting Buy-in 34 De-mystify modeling approach Don t underestimate the value of analyst acceptance Target Buy-in from all levels of organization Be open to trade-off between statistical accuracy and user acceptance

35 Roll Out & Phasing In 35 Avoid big bang approach Start from the ends and work towards the middle Establish milestones and benchmarks Establish clear channels for analyst feedback/questions

36 Score Management 36 Near Term monitoring through the door activity Longer Term monitoring performance Establish feedback loops for constant improvement

37 37 Avoiding Pitfalls Data Availability Understanding targeted population and segmentation assumptions Painting yourself into a corner Underestimating scope of testing Not a one-time effort

38 38 Questions?