1. Discovery Through Statistics Claim Analytics Group Disability Reserving Unleash the Power of the 21 st Century Canadian Institute of Actuaries June 29 2006

2. Discovery Through Statistics Claim Analytics Evolution of reserve calculations Where are we today? How can we improve the process? Summary Agenda

3. Discovery Through Statistics Claim Analytics Evolution of Reserve Calculations

4. Discovery Through Statistics Claim Analytics 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

5. Discovery Through Statistics Claim Analytics 1960’s

6. Discovery Through Statistics Claim Analytics 1960’s Mainframe/manual Simplified formulas Conservative assumptions Infrequent experience and table updates Technology Paper Very early computers Tapes, disks Evolution of Reserve Calculations

7. Discovery Through Statistics Claim Analytics 1970’s Evolution of Reserve Calculations

8. Discovery Through Statistics Claim Analytics 1980’s Evolution of Reserve Calculations

9. Discovery Through Statistics Claim Analytics Evolution of Reserve Calculations Mainframe calculations Move to basic principles calculations Conservative assumptions Infrequent table updates and experience studies Technology o Improved mainframes o PCs 1970’s - 1980’s

10. Discovery Through Statistics Claim Analytics 1990’s Evolution of Reserve Calculations Tim Berners-Lee Ignites the Internet

11. Discovery Through Statistics Claim Analytics 1990’s Mainframe/PC calculations Basic principles calculations Expected assumptions with explicit margins Deterministic scenario testing Infrequent table updates Annual / bi-annual experience studies Technology Faster computers, more storage Online processing Internet Evolution of Reserve Calculations

12. Discovery Through Statistics Claim Analytics Today PC-based calculations Basic principles Stochastic modeling Expected assumptions with explicit margins Infrequent table updates Annual/bi-annual experience studies Technology Advanced software algorithms Powerful computers with more storage, faster processing, Access to large databases of historic information Evolution of Reserve Calculations

13. Discovery Through Statistics Claim Analytics What Have We Accomplished? Tremendous progress made possible by evolution of computer power Calculations now explicit and seriatim Scenarios sensitivity-tested to better evaluate risk Experience studies more frequent

14. Discovery Through Statistics Claim Analytics What do we still need to do? Group Disability Reserving Frequent and comprehensive experience information o Studies at least annually o Ability for user to slice-and-dice information Information electronically provided to users Why? o More appropriate, up-to-date experience information Obstacles o Lack of priority

15. Discovery Through Statistics Claim Analytics Need key predictive factors of recovery, particularly diagnosis, but also Quebec, monthly benefit, tax status, reporting lag, incorporated into reserve calculation Why? o More appropriate reserve for each claim o Immediately capture business mix changes o Eliminate cherry picking at quarter-ends o Align with claim management practices o More understandable to management o Relevant for experience rating situations Obstacles o Lack of training in predictive modeling o How do you do it? (There can be thousands of diagnoses) o Cost to implement What do we still need to do? Group Disability Reserving

16. Discovery Through Statistics Claim Analytics How do we build diagnosis into reserve calculations?

17. Discovery Through Statistics Claim Analytics o Classify each diagnosis into a set of categories, based on likelihood and time to recovery o Develop unique termination rates for each category Weaknesses: Labour-intensive Subjective Data credibility issues Method 1 Building diagnosis into reserve calculations

18. Discovery Through Statistics Claim Analytics Building diagnosis into reserve calculations o Use predictive modeling techniques to produce scores that equate to probabilities of recovery or termination o Calculate reserves directly, using scores SOA paper outlines methodology for creating scores Scores are proven and credible Method 2

19. Discovery Through Statistics Claim Analytics Using Predictive Modeling to Calculate Reserves

20. Discovery Through Statistics Claim Analytics Claims are scored from 1 to 10. Scores show likelihood of return to work within a given timeframe. Scores are calibrated: score of 1 indicates 0 – 10% chance of recovery within given timeframe, score of 2 indicates 10 – 20% chance of recovery within given timeframe, and so on. J. Spratt Score: 4 # 452135 ClaimsScoring Claims Scoring J. Loe Score: 6 # 452009 P. Chang Score: 8 # 451156

21. Discovery Through Statistics Claim Analytics … … … … … ScoringReport Scoring Report Q.P.

22. Discovery Through Statistics Claim Analytics Five steps to developing LTD termination rates for Dave using claim scoring Dave

23. Discovery Through Statistics Claim Analytics About Dave SexMale Age44 QP90 days DiagnosisOsteoarthritis Developing termination Developing termination rates for Dave

24. Discovery Through Statistics Claim Analytics Dave’s claim scores Likelihood of RTW (%) Developing termination Developing termination rates for Dave

25. Discovery Through Statistics Claim Analytics cumulative RTW Probabilities, 1-24 Months after EP expressed as % 123456789101112 5.914.727.5 131415161718192021222324 34.5 Step One Get Cumulative RTW Probabilities Developing termination Developing termination rates for Dave

26. Discovery Through Statistics Claim Analytics 123456789101112 2.03.95.98.811. 8 14. 7 16. 8 19. 0 21. 1 23. 2 25. 4 27. 5 131415161718192021222324 28.128.729.329.830.431.031.632.232.833.333.934.5 choose uniform distribution, constant force or Balducci here, used uniform distribution expressed as % Developing termination Developing termination rates for Dave Step Two Interpolate between months

27. Discovery Through Statistics Claim Analytics Canadian Group LTD experience /1000 shown here alternative is company experience may want to make adjustments, e.g. improvement from mid- point of study 123456789101112.27.32.40.45.49.51.52.53.52.50.49 131415161718192021222324.47.46.44.42.40.38.37.35.34.32.31.29 Step Three Get mortality rates Developing termination Developing termination rates for Dave

28. Discovery Through Statistics Claim Analytics 123456789101112 1.9 7 2.0 0 1.9 9 2.9 6 2.9 8 2.9 7 2.1 5 2.1 2 2.1 1 2.1 0 2.0 9 131415161718192021222324.57.56.55 Step Four Convert cumulative RTW probabilities to month-to-month RTW rates # of claimants who will recover in period. Developing termination Developing termination rates for Dave 1 - LM cumulative RTW - LM cumulative death rate TM cumulative RTW - LM cumulative RTW # of claimants still on claim at start of period.

29. Discovery Through Statistics Claim Analytics 123456789101112 2.2 4 2.3 2 2.3 9 3.4 1 3.4 7 3.4 8 2.6 7 2.6 5 2.6 4 2.6 2 2.6 0 2.5 8 131415161718192021222324 1.0 4 1.0 2 1.0 0.98.96.94.92.90.88.87.85.84 Step Five Calculate Termination Rates Termination rate = recovery rate + mortality rate Developing termination Developing termination rates for Dave

30. Discovery Through Statistics Claim Analytics What to do after 24 months Produce scores for 24 months, then use traditional methods thereafter Produce scores for all future terms

31. Discovery Through Statistics Claim Analytics Significant progress has been made in calculating reserves. Still needed in Group Disability reserving: Better experience information Reserves that explicitly reflect the key factors for termination This is all doable today. Summary