1. A View Inside the “Black Box”: A Review and Analysis of Personal Lines Insurance Credit Scoring Models Filed in the State of Virginia By Cheng-sheng Peter Wu, FCAS, ASA, MAAA John Lucker, CISA Deloitte Consulting, LLP CAS 2004 Ratemaking Seminar, Call-3 Philadelphia March 11, 2004 By Cheng-sheng Peter Wu, FCAS, ASA, MAAA John Lucker, CISA Deloitte Consulting, LLP CAS 2004 Ratemaking Seminar, Call-3 Philadelphia March 11, 2004

2. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 2 -

3. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 3 - Our Efforts on this Topic to Date Does Credit Work? “Does Credit Score Really Explain Insurance Losses? Multivariate Analysis from a Data Mining Point of View” How Can You Go Beyond Credit? “Mining the Most from Credit and Non-Credit Data” How Do Credit Score Models Work? “A View Inside the “Black Box”: A Review and Analysis of Personal Lines Insurance Credit Scoring Models Filed in the State of Virginia”

4. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 4 - The Motivation for Our Paper What we did want to do? Contribute substantive material and insights to the ongoing debate over credit scoring Assist companies, regulators and the public in understanding how the Credit Scoring “Black Box” works Show the similarities and differences in credit scoring models What we didn’t want to do? Attribute our findings directly to the filing companies and their business practices Expose proprietary information beyond what is publicly available Render opinions on the superiority of one model over another

5. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 5 - The History of Personal Lines Pricing and Class Plans Few class plan factors before World War II Proliferation of class plan factors after the war Class plans for Personal Auto – territory, driver, vehicle, coverage, loss and violation, others, tiers/company, etc. Class plans for Homeowners – territory, construction class, protection class, insurance amount, coverage, prior loss, others, tiers/company, etc. Insurance credit scoring started in late 80’s and early 90’s as research and a developing concept – became widespread from the mid-1990’s onward

6. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 6 - The History of Personal Lines Credit Scoring Credit Score was the first important rating factor identified in 20 years Credit Score is a composite multivariate score vs. raw credit info Until recently, it was viewed as a “secret weapon” worthy of secrecy Today 90+% of Personal Lines insurers use credit scoring for some form of new biz acquisition, risk selection, pricing, and renewal Credit Score has been easy and relatively inexpensive to get, “quiet” to use, confidential, and straight forward in its implementability Today, it is the hottest, most widely contested and debated topic in the Personal Lines insurance industry

7. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 7 - The Current Environment Continues to be a hot topic for debate Many entities have conducted studies on the true correlation with loss ratio and the Disparate Impact issue Virginia, Washington, Maryland, Texas, Missouri NAIC, CAS, Tillinghast Towers-Perrin, EPIC Many states have restricted (or are considering restricting) the usage of the score or certain credit information More states want the “black box” filed and opened More companies are considering proprietary credit models for greater transparency and non-credit scoring models

8. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 8 - Study of VA Credit Score Filings Insurers filed over 40 credit scoring models in Virginia in 2002 Deloitte obtained copies of 11 of these filings, covering: 9 filings for Personal Auto and 2 filings for Homeowners 8 insurance groups $45 billion in personal lines premiums

9. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 9 - Types of Models Industry Model – Fair Isaac (FICO) 4 different FICO scores used by 3 insurance groups Uses credit information from TransUnion Multiple models by line, by market segment, and by version Industry Model – ChoicePoint 3 insurance groups for Auto Uses credit information from Experian Open model Insurance Company Proprietary / Custom Models 2 insurance groups Uses credit information from TransUnion Home and Auto are the same models

10. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 10 - Scoring Functions Rule-based Table driven format If factor x is equal to y, then get z points, etc... Sum all the points to generate a raw score All FICO models and one of the two proprietary models use this technique Formula Can be linear or non-linear Need to determine the parameters/weights One of the two proprietary models uses this technique The ChoicePoint model is a mix of the two, but is more of a formula function

11. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 11 - Scoring Functions Rule-based Advantages: simplicity, easier to explain, easier integration with a company’s class plan Disadvantages: must predetermine the groupings, potential limitations in the number of variables used in the model Formula Advantages: easier to include more variables, formula is a direct result of the modeling process and doesn’t require transformation Disadvantages: more difficult to explain and interpret

12. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 12 - Scoring Functions One way to compare a rule-based function and a formula function: review the “delta” A formula function: Z = 2 X + 3 Y, An increase of 1 in X – an increase of 2 in Z An increase of 1 in Y – an increase of 3 in Z A rule-based function: If X = 1 then 20 points; if X=2 then 40 points, if X=3 then 60 points. If Y=1 then 10 points; if Y=2 then 40 points; if Y=3 then 70 points. These two functions are essentially the same!

13. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 13 - Scoring Process Step 1 – calculate the raw score Step 2 – scale the raw score to the final score, (Score Scaling) Transform a raw score to a final score (e.g. 0.34778 becomes 570) Monotonic functions are used Simple Scaling Functions vs Complex Scaling Functions Simple scaling functions: linear shift and expand (a*score+b) Complex scaling: non-linear formula / transformation

14. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 14 - Scoring Scaling Function: Simple vs Complex Raw Score Final Score Simple Scaling Function Raw Score Final Score Complex Scaling Function

15. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 15 - Fair Isaac (higher score is better) 357 - 818 Auto 326 - 845 Auto 389 - 806 Auto 200 - 884 Home ChoicePoint (higher score is better) 220 – 998 Proprietary (higher score is worse) #1 #2 100-1000 1-100 Score Ranges

16. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 16 - Model Variables Fair Isaac – 10 to 13 variables, depending on the models ChoicePoint – 29 variables for “thin file” scores, and 37 variables for “thick file” scores Proprietary #1 – 10 variables Proprietary #2 – 36 variables

17. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 17 - = worse MoreRecent Late Payment / Past Due / Delinquency Public Derogatory Leverage Ratio, Unsatisfactory, Default, Bad Debt Info (in all but one proprietary model) Collection (in all but one proprietary model) Inquiry (in all but one FICO model) # of Accounts, Account History, Account History, Recent Account Activity Varies Model Variables

18. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 18 - More Comparisons Possible More model comparisons that could be performed: Variable strength comparison between models Score changes from one model to another Model lift and stability from one model to another To find out the answers to these questions: “Normalization of the Score Ranking and then Testing with Real Data”

19. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 19 - Normalization of the Score Ranking & Testing with Real Data Score a group of risks with different models Sort the scores for the risks from the best to the worst Group the sorted risks into deciles (or quintiles, quartiles, etc) Use the deciles (or quintiles, quartiles, etc) as the “score” for comparison between models for Predictive Power / Lift Variable Strength Score Changes / Migration

20. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 20 - Score Comparison Between 2 FICO Models - Original Score

21. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 21 - Score Comparison Between 2 FICO Models – Normalization with Decile Score Ranking Change in Decile Ranking% of Data 027.0% +-132.2% +-220.9% +-311.6% +-45.8% +-52.1% +-60.4% Total100.0%

22. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 22 - Considerations for Building or Selecting a Model How does your competitive advantage impact your choices? Degree of predictive power desired relative to other factors? How stable is the score from one period to the next? How flexible do you want your company’s models to be? What is your resource availability for development, “care & feeding”? What are your expectations with regards to the regulatory climate? What is the impact of the regulatory environment on your company? What is your potential cost savings for credit scores & credit data purchases? How can model performance be measured and monitored?

23. Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 23 - Conclusions All models are similar in the type, form and structure of the variables and the data sources they come from The models use different scoring functions and implementation approaches The models produce scores with different score ranges To perform a real comparison we must rank test the various models with real data This will continue to be a hot topic in the industry – stay tuned…!