What To Do When You Cannot Use Credit? (Personal Lines)

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

2. Agenda • The credit scoring revolution • What to do when cannot use credit? • Conclusions

3. The Credit Score Revolution

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. 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. Early Believers’ Benefit from Credit Scores

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. 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. 120 90 82 78 74 70 66 62 58 50 Loss Ratio Lift Curve Loss Ratio Credit Score Decile

10. Credit Score Revolution -Segmentation Power

11. What to Do when Cannot Use Credit Scores?

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. 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. 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. 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 3rd parties. • Both credit and non-credit

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. 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. 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. 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. 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. 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. 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. 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. …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. Conclusions

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. 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