1.  Washington State Transit Insurance Pool Experience Rating August 27, 2009 Presented by: Kevin Wick, FCAS, MAAA

2. PricewaterhouseCoopers August 2009 Slide 2 Overview Basic Foundation Historical Perspective Industry Perspective Experience Rating -Traditional Framework -Defining Comparison -Incorporating Comparison Result Current Environment Common Pitfalls

3. PricewaterhouseCoopers August 2009 Slide 3 Basic Foundation Are you a bank or a pool? If pool, members contribute funds in proportion to their level of risk -Rating system measures risk With experience rating, past losses are being used to help project future risk -Supplements primary allocation basis (miles)

4. PricewaterhouseCoopers August 2009 Slide 4 Historical Perspective Is a driver with one accident more likely to have a future accident? -Are past accidents correlated with future accidents? Extent of correlation defines extent future rates should be adjusted due to past losses -Credibility, or credence, concept High or low mod may simply mean primary rating unit does not completely measure risk

5. PricewaterhouseCoopers August 2009 Slide 5 Industry Perspective Insurance Industry -More sophisticated models required if membership or “insured pool” is not homogeneous -Large databases to measure correlation Governmental Pools -Same principles -Homogeneous membership allows simplified approach

6. PricewaterhouseCoopers August 2009 Slide 6 Experience Rating Traditional Framework If a member’s losses are greater than average, they pay more If a member’s losses are less than average, they pay less Such statements imply past losses are correlated with future loss potential How are losses defined? -Number of years -Caps on specific losses How do you blend result? Miles (primary rating basis) X Base Rate X Experience Mod = Contribution

7. PricewaterhouseCoopers August 2009 Slide 7 Experience Rating Defining Comparison Actual member losses in experience window compared with “expected” losses Expected is based on share of primary rating unit, or exposure -If 10% of miles, then expected to have 10% of losses

8. PricewaterhouseCoopers August 2009 Slide 8 Experience Rating Defining Comparison – Number of Years (Decision 1) Should experience window be 3 years, 5 years, 10 years? -improved correlation -“ticket on record” concept -maturity of losses Most often most recently completed 3 or 5 years is used All else being equal, a longer experience window results in more stable experience mods

9. PricewaterhouseCoopers August 2009 Slide 9 Experience Rating Defining Comparison – Loss Limit (Decision 2) Typically losses that enter the experience rating formula are capped -Mitigate impact on one catastrophic claim A member with three $250,000 claims is viewed as having greater likelihood of future losses than a member with one $1 million claim All else being equal, lower caps result in more stable experience mods

10. PricewaterhouseCoopers August 2009 Slide 10 Experience Rating Incorporating Experience If member’s losses are 50% higher than average, should they have a 1.50 experience mod? -If yes, rates will fluctuate substantially as individual member experience fluctuates substantially -If no, then need to define how experience is considered Typically partial weight Example using 25% Weighting (credibility) Member relative loss experience is 1.50 Experience mod calculation -1.50 x 25% + 1.00 x (1-25%) 1.00 is the average mod -Mod = 1.125 The less credibility, the closer the mods are to 1.00 -If the credibility is 0%, the mod is 1.00

11. PricewaterhouseCoopers August 2009 Slide 11 Experience Rating Credibility (weighting) Considerations (Decision 3a and 3b) Experience of smaller members is more volatile than larger members -Handled through sliding scale -Larger members with the more statistically significant experience have more credence placed on such Desired stability can be achieved by adjusting the degree of credibility -What are acceptable annual rate changes? Should balance of weight be on 1.000 or prior mod?

12. PricewaterhouseCoopers August 2009 Slide 12 Hypothetical Individual Member Experience

13. PricewaterhouseCoopers August 2009 Slide 13 Hypothetical Individual Member Experience 5 Year Rolling Average with Large Claims Capped

14. PricewaterhouseCoopers August 2009 Slide 14 Example of Current Formula First step is to measure the member losses as defined in the rating formula

15. PricewaterhouseCoopers August 2009 Slide 15 Example of Current Formula Secondly, member’s experience is compared to average experience of group (normalized for size)

16. PricewaterhouseCoopers August 2009 Slide 16 Example of Current Formula Final step is to weight between average relativity of 1.000 and indicated relativity

17. PricewaterhouseCoopers August 2009 Slide 17 Example of Current Formula

18. PricewaterhouseCoopers August 2009 Slide 18 Balance of Weight on Prior Mod

19. PricewaterhouseCoopers August 2009 Slide 19 Experience Rating Summary of Decisions 1.Number of Years 2.Loss Cap 3.Weighting a.Degree of responsiveness b.Weight on 1.000 or Prior Mod

20. PricewaterhouseCoopers August 2009 Slide 20 Current Trends Coordinate experience rating with benchmarking More formal reviews and education -Workshops with stakeholders More sophistication for decisions -Basis for choices -Retrospective application Move toward more stable mods Simpler presentations

21. PricewaterhouseCoopers August 2009 Slide 21 Common Pitfalls Choices/tradeoffs and formula are not understood Dealing with long-term higher or lower than expected loss levels System patched together with caps versus addressing the issue Untested non-traditional systems Stable Responsive FairSimple

22. PricewaterhouseCoopers August 2009 Slide 22 Ben Franklin Net Incurred Loss by Year

23. PricewaterhouseCoopers August 2009 Slide 23 Clallam Net Incurred Loss by Year

24. PricewaterhouseCoopers August 2009 Slide 24 Columbia Net Incurred Loss by Year

25. PricewaterhouseCoopers August 2009 Slide 25 Community Net Incurred Loss by Year

26. PricewaterhouseCoopers August 2009 Slide 26 Cowlitz Net Incurred Loss by Year

27. PricewaterhouseCoopers August 2009 Slide 27 Everett Net Incurred Loss by Year

28. PricewaterhouseCoopers August 2009 Slide 28 Grant Net Incurred Loss by Year

29. PricewaterhouseCoopers August 2009 Slide 29 Grays Harbor Net Incurred Loss by Year

30. PricewaterhouseCoopers August 2009 Slide 30 Intercity Net Incurred Loss by Year

31. PricewaterhouseCoopers August 2009 Slide 31 Island Net Incurred Loss by Year

32. PricewaterhouseCoopers August 2009 Slide 32 Jefferson Net Incurred Loss by Year

33. PricewaterhouseCoopers August 2009 Slide 33 Kitsap Net Incurred Loss by Year

34. PricewaterhouseCoopers August 2009 Slide 34 Link Net Incurred Loss by Year

35. PricewaterhouseCoopers August 2009 Slide 35 Mason Net Incurred Loss by Year

36. PricewaterhouseCoopers August 2009 Slide 36 Pacific Net Incurred Loss by Year

37. PricewaterhouseCoopers August 2009 Slide 37 Pullman Net Incurred Loss by Year

38. PricewaterhouseCoopers August 2009 Slide 38 Skagit Net Incurred Loss by Year

39. PricewaterhouseCoopers August 2009 Slide 39 Spokane Net Incurred Loss by Year

40. PricewaterhouseCoopers August 2009 Slide 40 Twin Net Incurred Loss by Year

41. PricewaterhouseCoopers August 2009 Slide 41 Valley Net Incurred Loss by Year

42. PricewaterhouseCoopers August 2009 Slide 42 Whatcom Net Incurred Loss by Year

43. PricewaterhouseCoopers August 2009 Slide 43 WSTIP Net Incurred Loss by Year

44. PricewaterhouseCoopers August 2009 Slide 44 Yakima Net Incurred Loss by Year

45. PricewaterhouseCoopers August 2009 Slide 45 Total Net Incurred Loss by Year