Best practice in data & scoring

1. Best practice in data & scoring Dr Paul Russell Director Analytical Solutions

2. Agenda • Some themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

3. Themes • Best practice is often discussed but almost never seen • Do the simple things well • Risk management is more than just a scorecard • The same principles apply across the credit lifecycle

4. 13 ways to grow bad debt Credit process step Target population Description Customer acquisition New customers • Identifying potential customers; • Selling credit products to new customers; • Identifying the credit risk of the customer and the proposed transaction; • Identifying the risk of fraudulent application • Deciding whether to accept or decline the transaction; • Deciding, for accepted transactions, on the terms, e.g., credit amount, pricing. • Reviewing the customers facilities (e.g., credit limits, price, etc.); • Cross-selling new products to the customers; • Ensuring good customers are retained; • Identify fraudulent transactions. Existing, non-delinquent customers Customer management Collections • Identifying self-cure customers; • Rehabilitation of potentially good customers; • Work-out customers where relationship is broken. Existing, delinquent customers

5. Why is credit risk management important? European consumer finance business, Profit Before Tax and Impairment Charges ($m) Get it right and it can support phenomenal value creation Profits Impairment charges Source: Annual Reports

6. Data Statistical Models Credit strategies Implementation tools Evaluation tools 5 core components Component Description Application data (for new customers) Account behaviour data (for existing customers) External data (e.g., credit bureaux) Risk models (PD, LGD), fraud models (application and transaction fraud) and revenue models Business rules that translate the outcome of statistical models in credit decisions (accept/decline, price, credit limits, etc.) that maximise profit Software tools to automate the calculation of the above scores and credit strategies on-line on high volumes, with a high degree of flexibility to change credit strategies “on the fly” Software tools to evaluate the performance of statistical models and credit strategies, and accuracy of implementation

7. Agenda • Some basic themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

8. Solicitation Application Customer management Collections Debt recovery Population Information Analytics touches every part of the customer lifecycle Analytics and the customer life cycle • Analytics touches every part of the customer life cycle • Amount of information about the customer grows as the relationship advances through the customer life cycle

9. Channel preference • Contact history • Demographics • Location • Bureau data • Action outcomes • Costs • Channel • Product holdings • Demographics • Bureau data • Previous relationships • Account performance • Costs • Product holdings • Usage • Delinquency • Customer contacts • Preferences • Bureau data • Actions taken • Action outcomes • Costs • Action history • Promises to pay • Promises fulfilled • Action outcomes • Bureau data • Costs • Action history • Promises to pay • Bureau data • Agents used • Promises fulfilled • Litigation outcomes • Costs Solicitation Application Customer management Collections Debt recovery Analytics and the customer life cycle

10. Define Goals Agree objectives Understand results Assess current challenger Plan Assess Review Strategy Review Design Monitor Build new strategy Track progress against expectations Implement Ensure operational deployment Analytics and the customer life cycle

11. Agenda • Some basic themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

12. The role of scoring • Credit scoring is a technique for predicting the future • This prediction can be anything of importance to the business • Arrears • Fraud • Profit • Response • Account closure • Company failure • Etc. • All scoring is based on one key assumption: • The past predicts the future

13. The role of scoring Example Scorecard How does scoring work? • Scorecards add and subtract points to a baseline constant according to individual’s or account’s data • Scorecards are easy to apply and simple to understand • The resulting score gives a prediction of future behaviour • Scores are used to rank individuals to assign the best actions

14. The role of scoring – application scorecard • Consider a scorecard built to predict whether a new applicant for a credit product will default in the next 12 months • This scorecard is used when a new customer applies… Application Form Data Score- card Score External Data (Bureau etc.) Take most appropriate action for each individual

15. Low Score / High Risk High Score / Low Risk Extremely Low Risk Consider for cross-sell of other products Standard Risk Accept on standard terms High Risk Reject or price to cover the high expected loss Extremely High Risk Reject The role of scoring - scores can drive actions

16. The role of scoring - benefits • Best use of data • Objective • Consistent • Automation • Control • Reduced losses

17. Agenda • Some basic themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

18. Building a scorecard – 3 requirements • Development sample – the historical data on which the scorecard will be built • Outcome – what we are trying to predict • Modelling methodology – the statistical tool that will help us form our scoring model The recent past Some time later THEN NOW Outcome Development Sample Statistical Model Score- card

19. The recent past THEN Development Sample Is my sample any good? • Representative • Products • Business cycle • The future • Robust • Volumes • Mature Is the outcome reliable?

20. The recent past THEN Development Sample Other Account Information Credit Bureau Data Historical Account Behaviour Application Form Information on the historical behaviour on other accounts with the same lender Information on the individual’s other credit commitments Information on the historical behaviour on the account Information collected from the applicant at the application point Building a scorecard – the development sample • This data can come from a number of sources • All relevant data should go into the development sample

21. NOW Outcome Building a scorecard – the outcome This is the behaviour that we are trying to predict • Can be a continuous variable (profit, revenue, loss given default, etc.) • More commonly it is dichotomous - yes/no • Will this applicant default? • Is this transaction fraudulent? • Will this company fail? • Etc. Good THE FUTURE Bad Observation - Now Outcome - Prediction

22. NOW Outcome What are we trying to predict?

23. Statistical Model Observation Data Outcome Score- card Building a scorecard – the statistical model Statistical Model • Many statistical tools available • Data is the most important factor Statistical tool needs to be: • Powerful – to get the best prediction from the data • Flexible – can handle varying data types and outcomes • Interpretable – easy to understand and to overlay business intelligence • Transparent – should be non-’black box’ for regulatory reasons and to ensure understanding

24. x x Reality x x x x x x x x x x x x x x x x x x x Prediction Building a scorecard - the statistical model Statistical Model • Linear regression • Logistic regression • Artificial neural networks • Etc • Other things being equal the choice of algorithm has relatively little impact on the ultimate power of the model

25. Building a scorecard – assessing the model Statistical Model Does the model solve the business problem? • Discrimination– the power to polarise individuals between good and bad - Gini statistic & Kolmogorov-Smirnov statistic • Accuracy– how much of the variability of the outcome is explained by the model • Validation– ensures that over-modelling has not occurred or that an anomalous sample has not been used • Improvement– the new model should outperforms the existing model

26. Agenda • Some basic themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

27. DATA SCORE CARD STRATEGY DECISIONS Using scoring systems • The data feeds the scoring system, which is used to aid the decisioning • The decisions a company makes determine its strategy • It is the aims and strategy of the business that must be considered when deciding how to use a scoring system, e.g. • Growing the market share • Reducing bad debt • Increasing automation • Maximising response for given marketing cost • Combating fraud

28. Low Score / High Risk High Score / Low Risk Extremely Low Risk Consider for cross-sell of other products Standard Risk Accept on standard terms High Risk Reject or price to cover the high expected loss Extremely High Risk Reject The role of scoring - scores can drive actions

29. Using scoring systems - the score distribution • Score distribution is obtained by applying the score to the development sample • Gives us a prediction for new applicants falling into a given score range

30. Building a scorecard - the score distribution REJECT REFER ACCEPT ACCEPT WITH X-SELL Score + Policy Rules + Terms of Business = Strategy

31. Agenda • Some basic themes • Analytics and the customer life cycle • The role of scoring • Building a scorecard • Using scoring systems • Risk management infrastructure

32. Data Rules Definition (Strategy Design Studio) Results Implementation – the Business Rules Engine Deployed in:- • Origination • Application processing • Portfolio Management • Customer level decisioning • Collections • Authorisations • Intelligent Messaging • Event Management • Basel II Stress testing • ….. Rules execution (Decision Agent)

33. The unsecured lending origination process A full range of client options and interfaces for channel independence and data accuracy Gather & validate application data Invoke enrichment strategy Credit bureau links Online links to gather data about existing relationships and customer behaviour Gather existing customer information Get decision & terms of business Business-driven scoring & decision-making Detect application fraud Handle referrals and manual procedures Comprehensive workflow capabilities and provision of relevant data for users Application screening and data matching Get policy decision & enrichment strategy Automated account set-up. Provision of hand-off files. Letter and e-mail production Implement final decision Business-driven scoring and decision-making

34. Analytical Data Mart Results Active History H O S T e.g. Account Management System, Authorisation System etc Evaluation Optimisation Reporting Extract Rule Definition Feedback Strategy Implementation Data Manager Strategic business environment Operational environment Implements Business logic, Segmentation, Scorecards, Strategies and Champion Challenger Defines Business logic, Segmentation, Scorecards, Strategies and Champion Challenger Variables Decision Engine

35. Beyond scoring - strategy optimisation There are disadvantages to traditional champion/challenger testing… • The time frame for observing results can be long • It can be hard to design the next step • The result can become a “semi-random walk”... Value We want to get there with the first challenger ! Using performance data enables better decisions, but is also more complex to combine all the decision influences to maximise value Challenger n Decision strategy “deploy-learn-deploy” process The challenger strategy proven in one time period, may no longer be appropriate for another time period – things change Challenger 1 Challenger 3 Influence due to: • Macro-economics? • Use of intuition? • Misunderstanding? Challenger 4 Challenger 2 Champion Time #35

36. Developments in analytics - strategy optimisation Incremental benefit ROI Most are here Some organisations are still here Elaborate Strategies • Segmentation based on predictive model dimensions: e.g. risk and revenue • “Subjective” judgment used to manage trade-offs Scoring • single predictive model e.g. credit risk score • “Heuristic” cut-offs assigned using good:bad odds X X X X X X X X Manual X X X X X X X X X X X X X X • Experience and intuition • Trial and error X X X X X X X X X X X X X X X X X X X X X X Underlying decision complexity The next step… Optimised Strategies • Allocates optimal action for each customer within constraints • Objective, mathematical goal maximisation #36

37. Stage 1: build the infrastructure • Centralisation of credit decisioning • Set-up of IT tools required to automate credit risk and market management processes and the interaction between front line and back office • Development of decision support tools • Development of credit / marketing databases Automate the processes

38. Stage 2: fine-tune for performance • Fine customer segmentation based on customer profile, product holding and behavior data • Advanced credit and marketing databases drive increased sophistication in statistical models development • Customer interactions for risk and marketing are proactively initiated at all key points • Strategies are designed at customer-level Automate the decisions

39. Stage 3: optimize for excellence • Infrastructure enables total proactive control of the business – decision analytics becomes a way of life • Risk and marketing strategies are centrally designed based on advanced statistical techniques and drive customer profitability • Decision analytics is well structured and integrated across business functions including risk, marketing, sales, operations, finance Optimize the decisions

40. 1 Build the infrastructure 2 Fine-tune for performance 3 Optimize for excellence Monthly review of credit strategies. Champion/challenger a way of life Profit driven credit strategy in place and reviewed regularly Strategy “a world-class consumer finance company” Credit policy in place How are credit policies and strategies defined, reviewed and improved? Processes fully support profit-driven strategy, and are integrated across functions Processes regularly reviewed and refined. Little manual intervention Ongoing knowledge improvement Processes well defined and automated Full suite of scorecards, ability to optimize credit strategies Processes Profit-driven organisation across functions Education on strategy review process, fully understanding the use of MIS Include all available data into the process. Focus underwriter on “key” review, not second scorecard How well defined and the processes, and what is the degree of automation? Ability to review and modify credit strategies ‘on the fly’ Create strategy review cross-functional team Scorecards in place for all critical segments, decision engine used to control terms of business. Generate key KPI’s How well do staff understand all profit drivers? What is the degree of expertise in credit scoring and decision science? Ensure clear assignment of responsibilities for risk management functions What credit management tools are used? How flexible are they? How easy is it for business user to change processes and strategies? How are credit risk, marketing and finance working together? How are operational and strategic decisions taken? Knowledge Tools Organisation The Road Map

41. Conclusions • It all starts with data • Scorecards are important • Strategy is more important • Implementing the strategy properly is vital • If you don’t monitor you’re wasting you time • Risk management is a never-ending journey

42. Best practice in data & scoring Dr Paul Russell Director Analytical Solutions