Analytics in Debt Manager 9

1. FICO™ Forum: Debt Manager™ 9 User Group Fairfax, VA | June 4–5, 2014 Analytics in Debt Manager 9 Reports, Dashboards, Scores, Models David Lightfoot Vice President, Product ManagementFICO Joe Milligan Lead Engineer, Product SupportFICO Martin Germanis Vice President, SalesFICO Doug Thompson Principal Consultant, Pre-SalesFICO

2. Taxonomy • Reports and Dashboards • Predictive Analytics • Prescriptive Analytics

3. Three Types of Analytics… Descriptive Prescriptive Predictive “What’s happening and why?” “What might happen?” “What action should we take?”

4. Reports and Dashboards

5. Reports Overview • Analysis Reports (Spindown, History) • Note Perspective param, doc map on left • List-type Reports (Account Status, List of Pending Pmts) • Note drilldown/drillthroughs • Dashboards (Agent Collection Effort) • Note gauges/chart functionality • Creating subscriptions • Report Builder • Explain model vs. replicated data

6. Reports and Dashboards What else would you like to see in Debt Manager 9?

7. Predictive Analytics

8. Business Challenges Inherent debtor differences Insufficient strategy differentiation

9. Collections Predictive Models & Scores Overview The Modelis the thing that produces the Score Determine customer debt likelihood Increase productivity Target and treat different customers & strategies

10. Off-the-Shelf Models in Debt Manager 9.5 Payment Projection Models - Predict the amount of payments from certain account holders Roll Rate Models - Predict probability that customer is going to increase in delinquency

11. Roll-Rate Scores in Debt Manager 9.5 Early Stage 2 Cycle - Rank-orders accounts based on probability of rolling from cycle two to cycle three Early Stage 1 Cycle - Rank-orders accounts based on probability of rolling from cycle one to cycle two

12. Payment Projection Scores in Debt Manager 9.5 Rank-orders accounts based on expected collection amount for accounts that go to cycle three & beyond

13. Custom Models Overview Industry Specific Model Customer Bankruptcy Model Account TypeModel Different models for different product types • Understand the likelihood of a customer going bankrupt • Adjust collections strategies Different collections & recovery models for different industries Target customers most effectively

14. Collections Scores Business Value • Get up-and-running quickly • No model development data requirements • Deployed with your system 15 - 20 % better account segmentation Improved customer service Improved resource utilization

15. Predictive Analytics What else would you like to see in Debt Manager 9?

16. Prescriptive Analytics

17. Strategy Challenges • High levels of delinquent customers • Same or lower capacity “Mature” portfolios with diminishing returns from current strategy

18. Prescriptive Analytics is the process of modeling and improving (optimizing) decisions and treatments using data and predictive models

19. Maximize Value at Every Stage Channel Management

20. Case Study – European Bank SOLUTION AND BENEFITS Decision Modeling and Optimization for Early Stage Collections FICO® Decision Optimizer • 3-month KPI: Bucket 1-Bucket 2 roll-rate reduced by 3% • 6-month KPI: Bucket 1-Writeoff roll-rate reduced by 10% • 3-year ROI expected to be 24:1 BUSINESS CHALLENGE • Improve performance of early stage collections strategies on unsecured portfolios • Reduce roll rates • Achieve all this without increasing expenses or headcounts

21. Unlocking value from prescriptive analytics – Four key steps DECISION MODELING Evaluates and monitors data that would impact decisioning Builds a graphical model for one or more decisions Establishes mathematical relationships within key variables DECISION OPTIMIZATION Solves for profit-improvement risk management strategies Allows users to apply robust constraints Allows users to stress-test results DECISION DEPLOYMENT Incorporates optimized strategies into core processing solutions immediately Manages and maintains the decisioning strategies to efficiently respond to market demands and changes SCENARIO ANALYSIS Uses permutations on key constraints to evaluate alternatives Explores range of what’s possible

22. Step 1: Model Customers’ Reactions to Your Actions Customer Action Reaction E(Roll) = 5% E(Pay) = $250 E(Attr) = 3% Risk score = 680 Rev Balance = $12,250 Rev Util = 61% Time in File = 132 Segment = A Option1 Delayed Call E(Roll) = 4.7% E(Pay) = $260 E(Attr) = 3.8% Option2 Gentle Call E(Roll) = 4.4% E(Pay) = $290 E(Attr) = 4.8% Option3 Urgent Call

23. Step 2: Simultaneously Consider All Possible Actions with Constraints Customer Action Reaction Delayed Call E(Pay)=$175, P(Attr)=2% Gentle Call E(Pay)=$180, P(Attr)=2.4% Decision Model Urgent Call E(Pay)=$187, P(Attr)=3.4% Portfolio Action Reaction Min Roll Rates; Same Staff $5 BB Annual Profitability $5.2 BB Annual Profitability Min Attrition; Less Staff Solver Balanced Approach $5.32 BB Annual Profitability

24. Step 3: Consider Multiple Scenarios and Select Best Operating Point Efficient Frontier – Early Stage CollectionsChoosing the optimal operating point from multiple choices FICO Optimization helps you understand all options Scenario G Increase profitability by $10 per account, without incurring additional expense 120 Efficient Frontier H I G 115 F J E Projected PROFIT per Account Current Operating Point Where you are today D 110 C 105 B Scenario B Maintain profitability per account and decrease expense by 6% Projected CHANGE IN RESOURCE over "Baseline" 100 A 95 -10% 0% 5% 10% -5%

25. Step 4: Operationalize Benefits • Optimized treatments can be converted into filters, jobs and strategies in Debt Manager in order to execute consistent decisions every month, week, day, etc. • Datasets with optimized treatments can be loaded directly into collection system if optimization is run every month, week, day, etc.

26. Examples of where FICO has applied Prescriptive Analytics in Collections and Recovery

27. Prescriptive Models Summary Better assess & target customers Optimize strategies Efficiently allocate resources

28. Prescriptive Analytics What else would you like to see in Debt Manager 9?

29. FICO™ Forum: Debt Manager™ 9 User Group Fairfax, VA | June 4–5, 2014 Thank You Martin Germanis 571-386-3001 MartinGermanis@fico.com David Lightfoot 415-446-6332 DLightfoot@fico.com Doug Thompson 303-973-7942DougThompson@fico.com Joe Milligan 571-386-3015 JoeMilligan@fico.com