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- 1. 1 Recent Trends inCredit Scoring Technology Credit Scoring and Credit Control VII September 5-7, 2001 Vijay Desai, Principal Scientist HNC Software
- 2. 2 Agenda • History ofHNC and ProfitMax • Overview of HNC Technologies • Transaction Based Profiles in Credit Scoring • Credit Risk, Attrition Risk, Revenue, and Profit Score Performance • Going Beyond the Credit Risk Score • Questions and Answers
- 3. 3 History of HNCand ProfitMax
- 4. 4 Who is HNCSoftware? • Founded 1986 • U.S. offices – San Diego HQ – Chicago – Irvine – Los Alamos – Philadelphia – New York • International offices – London – Singapore – Tokyo • 1,000 + employees • Initial Public Offering: June 1995 (NASDAQ symbol: HNCS) • 2000 Revenues $254.9 million San Diego Headquarters
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- 6. 6 History of AnalyticInnovation Fraud Management •Falcon, eFalcon Application Decision Management •Capstone •Capstone Online •4Score Marketing Optimization •Fee Enhancement •Pricing Optimization •Cross sell Optimization Risk Management •ProfitMax •ProfitMax Bankruptcy •Strategy Manager
- 7. 7 History of ProfitMax •Developed in 1994 • Used by three of the top five credit card issuers in USA • More than 100 million accounts scored using ProfitMax • Provides real-time decision capability • Provides a multi-dimensional view
- 8. 8 ProfitMax Uses HNCTechnology • HNC’s enhanced neural network technology provides core predictive model capability • Transaction data provide additional information typically lost in summarization • Models use account profiles – Updated with every transaction – Reflect entire account relationship • Models and software are designed for real-time decisions using the most recent available information
- 9. 9 ProfitMax Profitability Components Revenue modelExpected revenue if account does not attrite and does not fall into collection Credit risk model Expected loss due to failure to pay Combines probability of loss with amount Attrition risk model Expected loss of revenue due to a sharp and lasting reduction in balance and activity Profit Calculator ties results together Cost computation Expected operation & funding costs, using expense parameters & predicted transact, revolve, & delinquency behaviors • Graphical user interface • Integrated testing and versioning capabilities • Reports facility
- 10. 10 Profitability Overview Risk AdjustedRevenueRisk Adjusted Revenue (Cost of Funds + Operation costs) (Cost of Funds + Operation costs) less equals Profit ForecastProfit Forecast (Forecasted Revolving Revenue + Fees + Forecasted Interchange Revenue) (Forecasted Revolving Revenue + Fees + Forecasted Interchange Revenue) (Credit Risk Adjustment + Attrition Risk Adjustment) (Credit Risk Adjustment + Attrition Risk Adjustment) less equals Risk Adjusted RevenueRisk Adjusted Revenue
- 11. 11 Overview of HNCTechnologies •Neural Networks •Context Vectors •Score Fusion
- 12. 12 HNC Solutions inNetwork Building... • Architecture Settled by ANN Tournament System • Automatic Training with Progressive Testing -- Prevents Over/Under Training • Fast, HNC Hardware for Fine-Grain Learning • Input Variable Selection Techniques: – Proprietary Variable Clustering Method, – Statistical Analysis of Partial Derivatives, – Patented, Specific-Case Explanation Facility.
- 13. 13 Enhancement with Individual Profiles •Allows Network to Recognize Change • Allows for Detection of Trends and Deviations From Trend • Allows Full Use of Event and Interval Information • Avoids Wasteful Aggregation Into Fixed Periods • Allows Network to Assess Events in Proper Context • Changes Focus of Training Process From Unrelated Events to Evolving Patterns.
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- 15. 15 Use of Profilesin a Neural Engine Transaction Transaction Profile Feature Detectors Updated Profile Neural Network Profile Store Output Score
- 16. 16 Mining Merchant Namesfor Credit Risk Using Context Vector • Observation: – People who shop at Cartier are more likely to shop at Tiffany, and very unlikely to shop at GoodWill. – People with ComCheck transaction have much higher credit risk than people who shop at Ethan Allen. • Challenge: – How to extract such information from credit card transactions and incorporate it into credit risk models to enhance the prediction?
- 17. 17 Context VectorTM • ContextVector: – High-dimensional representation – Trained using co-occurrence statistics ==> vectors are close if they co-occur more often than what is expected. • Merchant Vector: – Context Vector representation of the merchant names. – Neighboring merchant-vectors ==> tend to be shopped at by the same people. Context Vector™ Space of Merchant Names Context Vector™ Space of Merchant Names tiffanytiffany AnnTaylorAnnTaylor ComChekComChek GoodWill Harrah Casino SalvationArmy EthanAllen Cartier ThriftyDrug KaiserPharm
- 18. 18 Context Vector --Cont. • Question: – What about people who go to casino only once in a while, and usually shop at high-end stores? – Verses people who pay a lot of medical bills and take greyhound? • Account Vector: – Aggregation of the merchant vectors the account shops at. – Represent the transactional behavior over a certain period of time. – It migrates to different direction when the behavior changes. – Can be associated with different credit risk. Context Vector™ Space of Accounts Context Vector™ Space of Accounts Mktng-skill Reading Subscriber Mktng-skill Reading Subscriber Gambler Thrifty Shopper High-end Shopper Medicine Spender Family Person
- 19. 19 Score Fusion • IssuersHave Access to Multiple Scores Today • In the Credit Risk Management Area, – Issuers Have Multiple Custom Scores Based Upon: • Transaction Data • Master File • Credit Bureau – Credit Bureau Score (Credit Bureau data Based) – Custom Credit Risk Scores (Master File or Master File & Transaction Based) – Custom Bankruptcy Scores (Master File or Master File & Transaction Based)
- 20. 20 How to BestUse Multiple Data Sources? • Combine all sources of data to build a “Super Duper” score (1) • Use existing scores as inputs to build new scores (2) • Combine existing scores and make a “Super Duper” Score (3) • Literature suggests that approach 1 is as good or better than 2 and approach 2 is as good or better than 3 • Organizational constraints make approach 3 the most feasible solution today
- 21. 21 Organizational Constraints • Reasonsfor not combining all sources of data – Credit-bureau data used only on as-needed basis – Processing system constraints – Reluctance to shelve scores built with significant effort • Reasons for not using existing scores as inputs – Loss of flexibility – Credit-bureau scores used only on as-needed basis – Processing system constraints
- 22. 22 Score Fusion -HNC’s Approach • The HNC Algorithm: – is not dependent on a functional form – makes sure that there are enough accounts in each score combination – is better at identifying non linear patterns in risk – gives the best estimate of risk at each score combination – is a multidimensional approach – is very easy to implement
- 23. 23 Transaction Based Profilesin Credit Scoring
- 24. 24 High High John Doe’s Profile Portfolio’s AggregateProfile #CashAdvances Revolving Balance AmountLow Transaction-based Neural Network Scoring…Profile Historical Cardholder Activity
- 25. 25 …Recognize Shifts inIndividual Cardholder Behavior #CashAdvances Revolving Balance Amount High Low High John Doe’s Profile Two Weeks Ago John Doe’s Profile Last Week John Doe’s Profile This Week
- 26. 26 Understand Events inContext Cardholder B’s ProfileCardholder B’s Profile Cardholder A’s ProfileCardholder A’s Profile Cardholder D’s ProfileCardholder D’s Profile #CashAdvances#CashAdvances Revolving Balance AmountRevolving Balance AmountLowLow HighHigh Cardholder C’s ProfileCardholder C’s Profile HighHigh
- 27. 27 Current Profile Cardholder B’sProfile Cardholder A’s Profile Cardholder C’s Profile Cardholder D’s Profile #CashAdvances Revolving Balance AmountLow High ...Understand Events in Context High
- 28. 28 Transaction Data Containa Wealth of Information Looking only at summarized data, these two cardholders appear to have similar risk Summarized Data Cardholder #1 Cash Advance Merchandise Cardholder #2
- 29. 29 Timing of Transactionsis Vital Information Note the difference in apparent risk when transaction data are examined Transaction DataSummarized Data Cardholder #1 Cash Advance Merchandise Cardholder #2
- 30. 30 More Timing ofTransactions Cash AdvanceTransaction DataSummarized Data Merchandise PaymentMonth #1 No payment? Is this a risky account or just a late payer? Month #2
- 31. 31 Knowledge of Individualis Key Transaction Data Summarized Data Month #1 Cash Advance Merchandise Payment Month #2 Month #3 Using the transaction data can tell the difference. Past behavior is stored and utilized to make decisions. Month #4
- 32. 32 Comparison of DollarSavings $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 0.50% 1.00% 1.50% 2.00% 2.50% 3.00% 3.50% 4.00% Percentage of Accts W orked per Month AnnualDollarBenefitperAccount Cycle-Cut Real-Time Daily Batch Benefit by Scoring and Decision Points
- 33. 33 Transaction Based NeuralNet Score is Stable Over Time Month 1 Month 2 Month 3 Month 6 Month 9 %units Avg.Scr.%units Avg.Scr.%units Avg.Scr.%units Avg.Scr.%units Avg.Scr. 1st ProfitMax 100 545 79.5 543 74.2 543 68.8 544 66.1 537 Quartile Monthly 100 553 60.1 552 58.2 551 63.1 556 60.7 551 2nd ProfitMax 100 614 65.5 614 60.0 616 50.2 618 46.4 622 Quartile Monthly 100 610 47.5 609 46.5 609 45.0 618 43.8 620 3rd ProfitMax 100 654 62.7 657 55.7 659 44.0 663 40.7 667 Quartile Monthly 100 657 48.2 645 47.0 645 35.2 654 31.8 654 4th ProfitMax 100 707 84.2 709 78.4 712 71.9 715 66.8 717 Quartile Monthly 100 707 77.1 688 76.8 688 69.3 691 67.7 681
- 34. 34 Benefit Over MonthlyScore Increases with Time Worst 20% of Accounts Dollar Based Performance Spread over Monthly Score 10 Months 12 Months 14 Months 19 Months C/O 31* 65 97 191 BKO 44 45 43 96 Ever 95+ 30 88 114 185 Ever 65+ -14 22 39 100 * 31 basis points higher than Monthly score
- 35. 35 Benefit Over MonthlyScore Increases with Time (contd.) Best 30% of Accounts Dollar Based Performance Spread over Monthly Score 10 Months 12 Months 14 Months 19 Months C/O 6 11 15 32 BKO 8 9 10 20 Ever 95+ 13 27 35 51 Ever 65+ 18 30 37 55 * 31 basis points lower than Monthly score
- 36. 36 Credit Risk ModelPerformance
- 37. 37 Transaction Data Leadto Better Risk Management and Marketing John Doe Behavior Trend Assessment This Month Month Ago Conclusion 1. Summary Data Scoring Balance $1000 No change in riskBalance $1000 2. Transaction- based Scoring 7995-Gambli 6011-Cash Advance Change to higher risk from 1 month ago ng Charges 6011-Cash Advance 5411-Grocery Store 7361-Employment Service 6011-Cash Advance Summary unemployment, live on card charges Summary work, family, stable $200 $300 $150 $150 $100 $100 5651-Clothing 5641-Toys 5812-Family Restaurant 5942-Bookstore 7999-Travel 5732-Electronics $200 $100 $50 $50 $400 $200
- 38. 38 Risk Model Comparison:Overall Results ProfitMax Monthly Score FALSE Positive Rate @ 5% Detection 0.3 2 FALSE Positive Rate @ 10% Detection 1 4 FALSE Positive Rate @ 20% Detection 5 11 FALSE Positive Rate @ 30% Detection 15 27 FALSE Positive Rate @ 40% Detection 31 54 FALSE Positive Rate @ 50% Detection 60 100 KS Statistic* 106 100 *K-S Statistic and False Positive Rates are scaled to Monthly Score = 100
- 39. 39 Transaction Data Improves PredictivePower 0 10 20 30 40 50 60 70 80 0 2 4 6 8 10 % Goods %Bads ProfitMax Monthly
- 40. 40 Transaction Data Particularly Usefulfor Young Accounts 0 10 20 30 40 50 60 70 0 2 4 6 8 10 % Goods %Bads ProfitMax Monthly
- 41. 41 Score Fusion: Improvementin K-S SCORE K-S Monthly Score 100 HNC Score 106 Combined Score 111
- 42. 42 Score Fusion: Improvementin High Risk Accounts 0 .00 % 4 .00 % 8 .00 % 12 .00 % 16 .00 % 20 .00 % 24 .00 % 28 .00 % 32 .00 % 36 .00 % 40 .00 % 2 0% 3 0 % 4 0% 5 0 % P e rc e n t B a d s D e te c te d PercentImprovementinFalsePositiveRate H N C S co re C o m b in e d S c o re
- 43. 43 Score Fusion: Improvementin Low Risk Accounts 0 .0 0 % 4 .0 0 % 8 .0 0 % 1 2 .0 0 % 1 6 .0 0 % 2 0 .0 0 % 2 4 .0 0 % 2 8 .0 0 % 3 2 .0 0 % 3 6 .0 0 % 4 0 .0 0 % 4 4 .0 0 % 4 8 .0 0 % 90 % 80 % 7 0% 6 0% 5 0% P e rc e n t G o o d s D e te c te d PercentImprovementinFalseNegativeRate H N C S co re C o m b in e d S co re
- 44. 44 Attrition Risk ModelPerformance
- 45. 45 Transaction Attrition RiskModel With Profiles Cardholder “A” Cardholder “B” Conclusion 1. Summary Data Scoring Balance $6,000 Value High Balance $6,000 Value High No difference 2. Transaction -based scoring 5310-Discount Stores $200 5698-Subscription $100 7841-Videotape Rental $20 5411-Supermarket $50 5964-Catalog Merchant $400 6011-ATM Cash $200 Different spending patterns reveal propensity to attrite. Summary High-Ticket Charges Non-repeating charges, T&E Spender Summary “Card of Choice” 4722-Vacation$1200 5812-Restaurant $150 3357-Car Rental $150 5944-Jewelry $400 “More likely to attrite” “Less likely to attrite”
- 46. 46 Balance at Riskis a Better Predictor than Attrition Score by Itself 0 10 20 30 40 50 60 70 80 90 100 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 Ranking According to Two Schemes PercentofActualLosstoAttrition Curbal*AttrProb Score
- 47. 47 Attrition Score RankOrders Attrition Balance Very Well 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 1 2 3 4 5 A ttritio n S c o re Q u in tile s Percent % Acco u n ts % B alan ce
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- 49. 49 Transaction Data InRevenue Models • Transaction data important in prediction of future revenue • Spending in certain SIC categories a better indicator of continued future revenue than others; e.g. Home related spending: • 5411 - Grocery Stores, Supermarkets • 4900 - Utilities - Electric, Gas, Sanitary & Water • 5200 - Home Supply Warehouse • 5211 - Lumber Services • 0742 - Veterinary Services • 7311 - Furniture & Tool Rental • 5231 - Paint & Wall Paper Stores
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- 51. 51 Transaction Volume 0 100 200 300 400 500 600 700 800 900 1 23 4 5 6 7 8 9 10
- 52. 52 Going Beyond theCredit Risk Score •Credit risk score not the best measure of future profitability •Multi-dimensional view of customer vital
- 53. 53 Managing Profitability: Using CreditRisk Predictions Portfolio organized by Credit Risk score 0 100 200 300 400 500 600 Avg. Profit -50 0 50 100 150 200 250 300 350 CreditRiskScore % Bad Low High
- 54. 54 Managing Profitability: Using CreditRisk Predictions Portfolio organized by HNC ProfitMax prediction -50 0 50 100 150 200 250 300 350 50 70 90 110 130 150 Low High ProfitabilityPrediction Avg. Profit % Bad
- 55. 55 A Better Measureof Future profitability -50 0 50 100 150 200 250 300 350-50 0 50 100 150 200 250 300 350 Organized by Credit Risk Score Organized by HNC Profit Score Low High
- 56. 56 Why a Multi-dimensional Viewof Customers? CreditRisk Reven Attrition Risk low low high low high high • Small groups of cardholders have a big impact on bottom line • Treatments are profitably applied when taken on select cardholder groups ue
- 57. 57 Dream Customer lowlow CreditRiskCreditRisk Reven Reven Attrition RiskAttritionRisk low low high low high high Bad Rate: 0.00% Attrition Rate: 2.00% True Revenue: $342.12 True Profit: $212.04 ueue •Low Attrition •Low Credit Risk •High Revenue
- 58. 58 Risk-Revenue Tradeoff: Balance Riskand Pricing •Low Attrition •High Credit Risk •High Revenue CreditRisk Reven Attrition Risk low low high low high high ue % of Accts: 1.00% Bad Rate: 35.40% Attrition Rate: 2.60% True Revenue: $387.65 True Profit: ($198.99)
- 59. 59 “Save Me” -Retention Program CreditRisk Reven Attrition Risk low low high low high high % of Accts: 1.00% Bad Rate: 0.04% Attrition Rate: 20.00% True Revenue: $354.20 True Profit: $222.55 ue •High Attrition •Low Credit Risk •High Revenue
- 60. 60 Light Revolvers - “BuildBalance Program” CreditRisk Revenue low low high lowhigh high • Low Credit Risk • Low Attrition • Medium Revenue • Low Credit Risk • Low Attrition • Medium Revenue % of Accts: 3.00% Bad Rate: 0.10% Attrition Rate: 2.40% True Revenue: $37.40 True Profit: $19.00 Attrition Risk
- 61. 61 Most Common Uses •Profit Score – Retention Queue – Segmentation Variable • Credit Risk Score: – Minimal Acceptable level of risk – “Knock-out” criteria for Marketing Programs • Revenue Score: – Needs based approach to credit line increases – Revenue reason codes used for targeting • Attrition Score – Proactive Retention offers – Profit@Risk metric
- 62. 62 Additional ProfitMax Benefits WillAlso Come From... • Revenue Score – Tracking and Reporting - trending over time – Testing Validation - incremental change over control position • Profitability Score – Prioritizing calls in VRU (best customers wait less) – Making it more difficult for unprofitable accounts to talk to a live representative – Reactive retention queue has on-line access to profit number; can tailor save offer – Best customers routed to best offer
- 63. 63 ProfitMax and Basic Segmentation RiskRevenue Attrition Generic Strategy Low Low Low Develop Low High Low Grow Low Low High Develop Low High High Defend High Low Low Exit High High Low Maintain High Low High Exit High High High Selectively Defend
- 64. 64 ProfitMax and Advanced Segmentation GenericHigh Risk Affinity Risk Revenue Attrition Strategy Strategy Strategy Low Low Low Develop Low High Low Grow Low Low High Develop Low High High Defend High Low Low Exit Develop Maintain High High Low Maintain Grow High Low High Exit Develop Maintain High High High Sel. Defend Defend Defend
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