1. © Deloitte Consulting, 2005 What To Do When You Cannot Use Credit? (Personal Lines) Cheng-Sheng Peter Wu, FCAS, ASA, MAAA CAS 2005 Special Interest Seminar Chicago September 19-20, 2005

2. © Deloitte Consulting, 2005 2 Agenda  The credit scoring revolution  What to do when cannot use credit?  Conclusions

3. © Deloitte Consulting, 2005 The Credit Score Revolution

4. © Deloitte Consulting, 2005 4 Personal Lines Pricing and Class Plans – History  Few rating factors before World War II  Explosion of class plan factors after the War  Auto class plans:  Territory, driver, vehicle, coverage, loss and violation, others, tiers/company…  Homeowners class plans:  Territory, construction class, protection class, coverage, prior loss, others, tiers/company...  Credit scoring introduced in late 80s and early 90s

5. © Deloitte Consulting, 2005 5 Personal Lines Credit Scoring – History  First important factor identified over the past 2 decades  Composite multivariate score vs. raw credit information  Introduced in late 80s and early 90s  Viewed at first as a “secret weapon”  Quiet, confidential, controversial, black box, …etc “Early believers and users have gained significant competitive advantage!”

6. © Deloitte Consulting, 2005 6 Early Believers’ Benefit from Credit Scores

7. © Deloitte Consulting, 2005 7 The Current Environment  Now everyone is using it:  Marketing and direct solicitation  New business and renewal business pricing and underwriting  How to stay competitive if everyone is using it?  Regulatory constraints:  Many states have conducted studies on the true correlation with loss ratio and potential discrimination issues - WA study, TX study, MO study  Many states have/are considering restricting the use of credit scores or certain types of credit information  More states want the “black box” filed and opened

8. © Deloitte Consulting, 2005 8 Some Facts About Credit Scores  A composite score that usually contains 10 to 40 pieces of credit information  Loss ratio lift is significant – a powerful class plan factor or rate tiering factor (2.0 ratio of worst 10 to best 10%)  Benefits/ROI are measurable  Lift curve can be translated into bottom-line benefit  Blind test and independent validation can be done to verify the benefit

9. © Deloitte Consulting, 2005 9 Loss Ratio Lift Curve 82 66 58 62 70 74 78 90 120 50 Credit Score Decile Loss Ratio

10. © Deloitte Consulting, 2005 10 Credit Score Revolution - Segmentation Power

11. © Deloitte Consulting, 2005 What to Do when Cannot Use Credit Scores?

12. © Deloitte Consulting, 2005 12 What to Do when Cannot Use Credit  One idea is to find “Credit Score Proxies” “Length of account” --- “Length of policies”, “Age of policyholders”? “Late payment” --- “Late payment in paying premium bill”, “Insurance lapse”? “Derogatory / Bankruptcy information etc” --- who has less chance to have derogatory or bankruptcy? etc…

13. © Deloitte Consulting, 2005 13 What to Do when Cannot Use Credit  Another idea is - why limited to “Credit Proxies” only, and go from credit scores to data mining and predictive modeling A credit score is just one example of an insurance predictive model The same methods used to build credit scores are used in data mining to build insurance predictive models – “Go Beyond Credit Models”. Broaden the usage of “predictive variables”

14. © Deloitte Consulting, 2005 14 Go Beyond Credit Models  The key is to use as much information as possible  in a multivariate way  Choice of statistical techniques is important, but the real key is the quality and breadth of predictive variables used.  GIGO  Actuarial/insurance knowledge is critical  Untapped riches reside in many companies’ transactional records.

15. © Deloitte Consulting, 2005 15 Data Sources  We classify possible data sources into two groups  Internal data sources: predictive information gleaned from the company’s own systems  Regardless of how or whether it is currently used  External data sources: predictive information available from 3 rd parties.  Both credit and non-credit

16. © Deloitte Consulting, 2005 16 Internal Data Sources  Policy information  Limits, Deductibles, Measure of exposure (# cars, #houses, #employees, $sales, premium size…  Line-Specific information  Driver, Vehicle, Business Class …  Policyholder information  Age, gender, marital status …

17. © Deloitte Consulting, 2005 17 Internal Data Sources  Customer-level information  Transactional data  Coverage, premium and loss transactions  Billing information  Correlation with credit  Agent information A little creativity in using these data sources will go a long way!

18. © Deloitte Consulting, 2005 18 An Example of a “Creative” Variable  “Distance between Agent and Insured”: close by agents know you better!  Insured’s address available in policy data system  Agent’s address available in agency database  Map two addresses into longitude and latitude using “geo-coding” tools  Calculate the distance using “longitude-latitude distance formula”

19. © Deloitte Consulting, 2005 19 External Data Sources  Credit  Predictive both for commercial and personal lines  MVR – CLUE  Zipcode/geographic information  Rating territory  Many different sources available  The sky is the limit but  Consider cost, hit rate, implementation, …etc

20. © Deloitte Consulting, 2005 20 Types of Variables Generated  Territory-level  Demographic, weather, crime,...etc  Policy / policyholder-specific  Many traditional rating variables fall into this category  Behavioral  Less traditional – fits more neatly into data mining paradigm than classification ratemaking  Credit, billing, prior claims, cancel-reinstatements…

21. © Deloitte Consulting, 2005 21 How Many Variables?  It is possible to generate literally hundreds of predictive variables Some will be redundant Some will not be very predictive Some will be somewhat predictive Some will be “killer”  A good model can contain as few as 15-20 or as many as 60-70 variables Usually no single “ideal” model

22. © Deloitte Consulting, 2005 22 Which Variables to Use?  Choosing is a major part of the data mining process Use variety of exploratory statistical techniques Use prior modeling experience / actuarial knowledge  Several considerations Actuarial / underwriting knowledge Client’s business needs Legal / regulatory considerations Data availability / cost Systems implementation considerations

23. © Deloitte Consulting, 2005 23 In Our Experience…. Do “Go-Beyond Credit” PMs work?  YES: non-credit predictive models are Valuable alternative to credit scores Flexible Tailored to individual companies Leverage company’s untapped internal data Comparable predictive power to credit scores  And mixed credit / non-credit PMs can be even stronger

24. © Deloitte Consulting, 2005 24 …But It’s Not a Walk Through the Park Challenges for PMs:  IT resources constraints  Project management  Business process buy-in  Success of system and business implementation  Training and organizational change

25. © Deloitte Consulting, 2005 Conclusions

26. © Deloitte Consulting, 2005 26 Industry Trends  How do companies try to stay competitive regarding the use of credit?  How do companies prepare for increasing regulatory constraints?  Industry trends Companies are developing modeling capabilities and pursuing various applications Companies are developing proprietary credit scoring models rather than buying “off-the-shelf” credit scores. Companies are also going beyond credit, to build scoring models that don’t rely solely on credit

27. © Deloitte Consulting, 2005 27 Keys to Building Credit Alternative Models  Fully utilize all sources of information  Leverage company’s internal data sources  Enriched with other external data sources  Use large amount of data  Employ systematic analytical process  Use state-of-the-art modeling tools  Apply multivariate methodology  Disciplined project management