1. Credit History Impact on Personal Lines Loss Experience Session CPP-49 James E. Monaghan Thurs. March 9, 2000 CAS Ratemaking Seminar

2. Purpose of Study Much discussion over presence or lack of presence of correlation between credit history and future personal lines losses Purpose was to determine if this relationship exists Still much discussion over what defines correlation when outside statistical spectrum

3. Purpose of Study “Correlation” involves a large number of variables as independent variable with one apparent dependent variable, loss ratio Which variables from the credit file are predictive of future loss activity greater or less than average? What are the various strengths of each of these variables? (i.e., weights to apply)

4. Purpose of Study Are there other policy characteristics, either rated for or not rated for, which duplicate the impact of credit history on losses? (i.e., independence of credit) How much cross-dependency exists within a large number of underwriting or rating characteristics when measured against credit history?

5. Research Database All policies written as new business in policy year 1993 Calendar/accident year premium and loss during time period 1/93 though 12/95 All policy characteristics measured at time of initial writing Credit data recalled from bureau archives at time closest to original time of writing Compilation done during 1997; FCRA compliance issues

6. Current Delinquent Amounts

7. Derogatory Public Records

8. Collection Records

9. Revolving Account Leverage Ratio

10. Age of Oldest Trade Line

11. Profile Groups For purposes of loss comparisons, all risks grouped into 4 mutually exclusive categories based on 7 credit record variables APD, DPR, collections, inquiries, leverage ratio, Age of oldest trade line, worst current trade line status are the 7 variables used to create the groups

12. Profile Groups Goal in Creating mutually exclusive groups: A)Large difference between each group and its neighbor in loss ratio B) Significant percentage of premium distribution in each group

13. Profile Group Performance

14. Multivariate: Driving Record All driving record types reduced to three groupings: –Clean in 3 years prior (includes 1 minor moving violation) –One accident in 3 years prior (fault or non- fault) –All other (by definition 2+ incidents in 3 years prior of any kind)

15. Driving Record

16. Age of Driver 1

17. Classical Underwriting Profile Attempt to override all “stability” factors Group data by –driving record –marital status –home ownership –number of vehicles Is credit impact diminished or eliminated?

18. Classical Underwriting Profile

19. Rating Territory Recent review of distribution by credit group in urban, suburban and rural groupings of territories Done for auto line of business in New York, Connecticut, Ohio Virtually no variation in distribution or loss performance relativities based on territory type New York City experienced greatest average premium decrease with implementation of rate factors based on groups (relative to suburban and rural areas of New York state)

20. Policyholder Retention In both current data and data from research database, customers with better bill paying histories have higher retention Less likely to shop: price elasticity Chicken and egg question: between retention, loss performance, and credit history

21. Homeowners Result Comparison

22. Home: Amount Past Due

23. Home: Collection records

24. Home: Derogatory Public Records

25. Frequency and Severity Credit influence on auto losses is predominantly frequency on both high and low ends Credit influence on homeowners is frequency only at low end, both frequency AND severity at high end Poor credit history for homeowners is only severity impact, and it is large

26. Credit as a Rating Variable How to combine elements from a credit report (large number of “facts”) –Which to choose –How to bin or group each variable –How to evaluate each if score weights are used

27. Control of the Insured Rating variables are best not to be influenced by intentional insured behavior Credit history falls under this category To what extent: If pre-existing financial disincentives have not caused behavioral changes, will auto insurance price?

28. Stability vs. Responsiveness Some variables are fixed for specific time period: Derog public records, collection records Some are transient, potentially daily: leverage ratio, account status, etc. Rating plan should contain some balance of these two types

29. Regulatory Approval & Societal Issues White Paper: underwriting vs. rating Fairness issues of reordering, discrimination, erroneous data, “explainable events” Causality arguments Education: perception of use of credit (historically negative)