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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -2- 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” Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -3- The Motivation for Our Paper What we did want to do? What we didn’t want to do? • Contribute substantive material and insights to the ongoing debate over credit scoring • Attribute our findings directly to the filing companies and their business practices • Assist companies, regulators and the public in understanding how the Credit Scoring “Black Box” works • Expose proprietary information beyond what is publicly available • Render opinions on the superiority of one model over another • Show the similarities and differences in credit scoring models Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -4- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -5- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -6- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -7- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -8- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. -9- 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 10 - 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 11 - 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! Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 12 - 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 13 - Scoring Scaling Function: Simple vs Complex Simple Scaling Function Complex Scaling Function 100 900 90 800 80 70 Raw Score Raw Score 700 600 500 400 60 50 40 30 20 10 0 300 200 300 400 500 600 700 0.0 Final Score Copyright © 2004 Deloitte Development LLC. All Rights Reserved. 5.0 10.0 Final Score - 14 - 15.0 20.0 Score Ranges 357 - 818 Auto Fair Isaac (higher score is better) 326 - 845 Auto 389 - 806 Auto 200 - 884 Home ChoicePoint (higher score is better) 220 – 998 Proprietary (higher score is worse) #1 100-1000 #2 1-100 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 15 - 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 16 - Model Variables More = worse Recent 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 17 - Varies 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” Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 18 - 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 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 19 - Score Comparison Between 2 FICO Models - Original Score Exhibit 8 Score Distribution Comparison between a Auto Model and a Home Model by Fair Isaac 3000 Number of Data Points 2500 2000 1500 1000 500 0 <=300 325 375 425 475 525 575 625 Mid Point of the Score Range 675 725 775 Fair Isaac - Assist 2.0, Preferred Auto Fair Isaac - Assist 2.1, HO3 Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 20 - >800 Score Comparison Between 2 FICO Models – Normalization with Decile Score Ranking Change in Decile Ranking % of Data 0 27.0% +-1 32.2% +-2 20.9% +-3 11.6% +-4 5.8% +-5 2.1% +-6 0.4% Total 100.0% Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 21 - 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? Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 22 - 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…! Copyright © 2004 Deloitte Development LLC. All Rights Reserved. - 23 -