2. Improving Disability Claims Management with Predictive Modeling May 15, 2008 Claim Analytics Inc. Barry Senensky FSA FCIA MAAA Jonathan Polon FSA www.claimanalytics.com

3. What is Predictive Modeling? Why Use Predictive Modeling? Disability Claim Scoring Rehabilitation Analysis Agenda

4. What is predictive modeling?

5. Predictive Modeling Harnesses power of modern computers to find hidden patterns in data Used extensively in industry Many possible uses in insurance: Claim management Pricing Reserving Fraud detection

6. A Part of Everyday Life You have probably used a predictive model today Mail sorting Credit card processing Credit scores Weather forecasting Professional sports

7. Why use predictive modeling?

8. Decision Support Tool The human brain is exceptional at pattern detection Predictive modeling can be applied to augment our innate abilities

9. Augment with Predictive Modeling People: -Very strong at qualitative classifications, such as very likely, maybe likely, very unlikely Predictive Models: -Make accurate quantitative predictions, such as 87%, 49%, 12% -Can quantify the effect of factors with even minor influence on outcomes

10. Augment with Predictive Modeling People: -Biased by psychological aspects of memory -Tendency to overweight our successes Predictive Models: -Objective quantification of past experience

11. Augment with Predictive Modeling Predictive models are accurate, fast and objective The models provide estimates as to the likelihood of future outcomes This is valuable information which can help people make better decisions

12. Disability Claim Scoring

13. Claims scored from 1 to 10 based on likelihood of recovery within a given timeframe Scores are objective and accurate Scores calibrated to probability of recovery What is claims scoring? J. Spratt Score: 4 # 452135 P. Can Score: 8 # 451156 J. Loe Score: 6 # 452009

14. Building the Claim Scoring Model

15. It starts with a data extract: - Age- EP - Gender- Diagnosis - 2nd diagnosis- Income - Benefit- Occupation - Region- Own occ period - Industry- and more Building the Model

16. 1.Model presented with your historic claim data, including known outcomes. 2.Model begins making predictions on cases in the sample… 3.…compares predictions to real outcomes, and begins to detect patterns… Initial predictions are rough…

17. But… model continues to learn After millions of iterations and millions of comparisons… the model learns to predict accurately And builds a complex algorithm that fits your experience

18. Model Validation Critical test of model’s accuracy For 10% of data, client withholds outcomes For this data, compare model predictions to actual outcomes

19. Model Validation Results Model’s Predicted Recovery Actual Recovery Rate

20. Using the Claim Scoring Model

21. 1.Scores provided for all in-force claims 2.New claims scored weekly 3.Monthly trend analysis Claim Scoring Process

22. Example of New Claims Scoring Claim #NameQPDiagnosisSexAgeBenefitScore 12798P.Can119Torn Medial Meniscus M521,250 12804J.Loe180FibromyalgiaF462,500 12846J.Spratt364FibromyalgiaF462,900 Claim #NameEPDiagnosisSexAgeBenefitScore 12798P.Can119Torn Medial Meniscus M521,2508 12804J.Loe180FibromyalgiaF462,5006 12846J.Spratt364FibromyalgiaF462,9004 Note: actual reporting normally includes many more fields than shown here.

23. Quarterly Reporting: trend identification

24. Benefits of Claim Scoring

25. Flexibility Your model Your strategy Your goals Your people

26. Non-intrusive Scores received electronically Data gathered from background No assembly required

27. Objective Triage Right at the start Facilitate early intervention

28. New claims begin to show patterns 8 - 10s With active case management, will recover on their own Probably won’t recover 1 - 3s Your world of opportunity 4 - 7s

29. In-force Claims In-force claims with low scores: this often helps the insurer to make the decision to stop expending resources where the likelihood of success is low In-force claims with high scores: these claims should have recovered and may be worth revisiting

30. Financial performance 150 125 100 75 50 25 $MM $81,544,900 $106,291, 508 2001 $81,544,900 $ 123,254,080 $106,291, 508 2003 Claim Scoring

31. J F M A M J J A S O N D 5.0 m 4.5 m 4.0 m 3.5 m 3.0 m 2.5 m 2.0 m 1.5 m 1.0 m 0.5 m 2002 2003 Financial performance

32. Buy-in RegionNo buy-in Region Financial performance Earned value per hour of rehab

33. Claim Scoring Summary Fast. Objective. Optimize resources. Facilitate early action. Improve results.

34. Rehabilitation Analysis

35. Rehab: Key Questions On what types of claims does rehab significantly increase the likelihood of return to work? When is the optimal time to start rehab? When is the optimal time to stop rehab if a claim hasn’t yet terminated? The answers are in your claims history

36. What Claims to Rehabilitate If rehabilitation data is available on the claims system, its impact can be quantified A claim scoring model can generate two scores for each claim: one score if rehab is used, another score if it isn’t Rehab adds value for claims where there is a big difference between the two scores

37. What Claims to Rehabilitate No value of rehabGreat value of rehab

38. When to Start Rehabilitation Too early, resources will be expended on claimants that may recover quickly on their own Too late, claim durations may be extended Predictive models can help find the sweet spot

39. When to Start Rehabilitation Sample Chart Too soonSweet SpotToo late

40. When to End Rehabilitation Rehab can improve the likelihood of recovery Yet, some claims do not recovery, even with rehab Important to identify the point at which further investment is no longer justified

41. When to End Rehabilitation Sample Chart

42. Opportunities in approach. In profit. In potential. AfterBefore

43. Questions?