2. Barry Senensky FSA FCIA MAAA www.claimanalytics.com Overview of Claim Scoring November 6, 2008

3. On the Agenda About us What is Claim Scoring? Predictive Modeling Building a Claim Scoring Model Using Claim Scoring Summary Questions & Answers

4. Founded in 2001 by two actuaries to apply predictive modeling techniques to insurance questions Clients in Canada and U.S. Several products About Us

5. What is claims scoring?

6. LTD/STD 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 Whatis claims scoring? What is claims scoring? J. Spratt Score: 4/6 # 452135 P. Can Score: 3/9 # 451156 J. Loe Score: 5/7 # 452009

7. Predictive Modeling

9. Computer Performance MeasureIBM 7094 c. 1967 Laptop c. 2004 Change Processor Speed (MIPS).252,0008,000-fold increase Main Memory 144 KB256,000 KB1,778-fold increase Approx. Cost ($2003) $11,000,000$2,0005,500-fold decrease

10. What is a Predictive Model A Predictive Model is a model which is created or chosen to try to best predict the probability of an outcome Have been around for 40+ years Harnesses power of modern computers to find hidden patterns in data Used extensively in industry Many possible uses in insurance:

11. About Predictive Models May be parametric… apply numerical methods to optimize parameters E.g., gradient descent, competitive learning Or non-parametric often have a decision tree form typically optimized using exhaustive search

12. Predictive Modeling Tools Some common techniques Generalized linear models Neural networks Genetic algorithms Random forests Stochastic gradient boosted trees Support vector machines

13. Why arent Insurance Companies building more predictive models? Life & Health Insurance Industry is conservative and can be slow to change Not a traditional actuarial tool The times are changing! –Especially P&C Insurers Its only a matter of time! –It just makes too much sense! –Innumerable applications to help solve insurance problems

14. Building a Claim Scoring Model

15. Start with a data extract: - Age- EP - Gender- Diagnosis - 2 nd 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 data

18. Model Validation Critical test of models accuracy Outcomes of 10% of historical data withheld by client Once model declared complete, this data is used to test model compare model predictions to actual outcomes

19. Model Validation Results Models Predicted Recovery Actual Recovery Rate

20. 1.Scores can be calculated for all in-force claims 2.New claims can be scored weekly or even sooner Claim Scoring Process

21. Claim #NameEPDiagnosisSexAgeBenefit6m Score 24m Score 12798P.Can119Torn Medial Meniscus M521,250 12804J.Loe180FibromyalgiaF462,500 12846J.Spratt364FibromyalgiaF462,900 Reporting Note: actual reporting includes more fields than shown here. Claim #NameEPDiagnosisSexAgeBenefit6m Score 24m Score 12798P.Can119Torn Medial Meniscus M521,25039 12804J.Loe180FibromyalgiaF462,50057 12846J.Spratt364FibromyalgiaF462,90046

22. Using claim scoring

23. Are you kidding me? &

24. Objective Triage Facilitate early intervention

25. Review of Old In-force Claims High scores opportunities for recovery Low scores opportunities for expense savings Discover new opportunities

26. Workload Allocation Claims can be allocated by degree of challenge 4s to 7s more difficult, time-intensive more experienced and expert claims handlers 1s to 3s, 8s to 10s simpler newer / less experienced claims handlers Equalize workload of claims personnel Smooth the workload

27. Prioritize Time Can be used by claims handlers to prioritize their time Snapshot of your workload

28. Social Security / Other Offsets 1s to 3s are good candidates to review Even better to build a model specific to determining which claims to send to Social security and when… Learn when to reach out

29. Decision Support Tool Rehab IMEs Surveillance Other forms of intervention Settlements Optimize resource $

30. Measure performance Scores represent expected recovery rates Can be used to measure actual to expected (A/E) recoveries What you can measure, you can improve Actual Recovery % Predicted Recovery % A / E Regional Office 1 3027111 Regional Office 2 5061 82

31. Planning/Forecasting Scores indicative of future recovery experience Use to develop financial projections for group business unit. Dont get blindsided!

32. Reporting: trend identification

33. Benchmarking Accurately, objectively compare claim practices with other companies How are we really doing?

34. Sample Predicted Recovery Rate Predicted Recovery Rate: Age 45, Male, Displaced Disk 0% 20% 40% 60% 80% 100% Predicted 81%65%74%73% Company ACompany BYour CompanyAverage

35. Summary Fast. Accurate. Objective. Optimize resources. Facilitate early action. Improve results.

36. Opportunities in approach. AfterBefore

37. Questions?