3/23/2011. Beyond the Credit Score. The History of Credit Score

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1 Beyond the Credit Score Ratemaking and Product Management Seminar March 20 22, 2011 Gary Wang, FCAS, MAAA Experience the Pinnacle Difference! 1 Beyond the Credit Score Credit score has been proven to be significant However, the debate over its use continues How do we move beyond credit score? By focusing on fundamentals The fundamentals are the same whether credit is allowed or not In Maryland, the insurance market adapted and so did some insurers There is/will be life beyond credit score The History of Credit Score Began to show up in the mid 1990 s Shows significant risk segmentation Level Loss Ratio GLM 1

2 Widespread Adoption Use of Credit By the early 2000 s. most insurers were using credit 96% 93% 92% 90% 90% 88% 87% 84% 82% 80% 76% Florida CRTF (1/2002) MI DOI (12/2002) IL DOI Auto (2/2002) IL DOI Home (2/2002) Best Week (1/2004) Studies Supporting Credit Scoring Fair Isaac Loss Ratio Study December, 1996 Statement of the American Insurance Association on the Lack of Correlation Between Income and Credit Score Whether Tested Against the Average of Median Score March 1999 Predictiveness of Credit History for Insurance Loss Ratio Relativities (Fair Isaac) November, 1999 The Impactof Personal CreditHistory on Loss Performance in Personal Lines (Monoghan Study) Winter, 2000 University of Texas Study March, 2003 EPIC Actuaries Study June, 2003 Texas DOI Study #1 December, 2004 Texas DOI Study #2 January, 2005 Credit Based Insurance Scores: Impacts on Consumers of Automobile Insurance (FTC Auto Study) July, 2007 Federal Reserve Study August, 2007 Studies Challenging the Use of Credit Arkansas Credit Score Survey September, 1996 Georgia Credit Hearings October, 2001 Task Force On The Use Of Credit Reports In Underwriting Automobile And Homeowners Insurance January, 2002 The Use of Insurance Credit Scoring in Automobile and Homeowners Insurance (Michigan OFIS) December, 2002 Effect of Credit Scoring on Auto Insurance Underwriting and Pricing (Washington DOI) January, 2003 Insurance Credit Scoring in Alaska February, 2003 Insurance Based Credit Scores: Impact on Minority and Low Income Populations in Missouri January, 2004 Report on the Credit Scoring Data of Insurers in Maryland February,

3 Serious Issue Public Perception This use of credit scores in order to derive insurance rates is yet another fraudulent fraudulent financial financial scheme to scheme charge anyone to charge and anyone everyone and more than the services or product being everyone sold are more worth. than This the is the govt's fault for not safeguarding services the taxpayers' or product personal being information. sold are Govt. worth has allowed access to every conceivable piece of information by those companies selling "required" products to consumers. When I say "required" I am referring govt s to the fault insurance that is required by mortgage lenders and the car loan industry, health insurance, etc. You cannot acquire these most important components of modern living without insurance. The consumer is therefore caught in a web of deceit and shameless shellacking by financial web of services deceit products and shameless that increasingly shellacking demand more and greater charges on everything the consumer is FORCED to purchase just to maintain a modest standard of living. It really will take a revolution on the part of the consumer to reverse the wholesale thievery they face every day in connection with every aspect of their financial wholesale lives. The thievery market is out of control and has been for a very long time (February, 2010) What Does This All Mean? Credit debate will continue Public perception is a difficult issue to overcome At least 49 credit scoring bills introduced in 24 states in 2009 NAIC hearings MI OFIS court case Lawsuits Economic crisis What will be the outcome? Across the board solution either completely allow or ban the use of credit scoring Some version of what we have today Next Logical Questions Is there a replacement for credit? What is the next credit? What do I do without credit? 3

4 Are We Asking the Right Question? CAS Statement of Principles Regarding Risk Classification: The grouping of risks with similar risk characteristics for the purpose of setting prices is a fundamental precept of any workable private, voluntary insurance system. ASOP #12: Rates within a risk classification system would be considered equitable if differences in rates reflect material differences in expected cost for risk characteristics. How Do We Move Beyond the Credit Score? The fundamental issue really isn t about credit The real question is how to develop the most effective risk classification system The answer is the same with or without credit Get the most value out of what you are using Identify additional segmentation variables Explore new ways of analyzing variables Biological & Psychobehavioral Correlates Psycho behavorial 1. Sensation seekers 2. Risk perception judgments 3. Risk appraisal judgments Behavorial 1. Impulsive 2. Attention to detail 3. Focus 4. Responsibility Credit Risk Insurance Risk Brockett, Patrick L. and Linda L. Golden. Biological and Psychobehavioral Correlates of Credit Scores and Insurance Losses: Toward an Explication of Why Credit Scoring Works

5 Get the Most Out of Your Existing Factors Insurance score is powerful indicator However, it does overlap with traditional and new variables being use to rate insurance The presence or absence of credit can change the predictive power of existing variables Interactions can also be powerful tools to enhance segmentation Customer Loyalty Persistency 0.00% 0.00% -0.32% -1.00% -2.00% -3.00% Percent Change -4.00% -5.00% -6.00% -7.00% -8.00% -1.21% -2.45% -3.85% -4.55% -5.11% -5.35% -5.31%-5.51% -7.56% Persistency Property Damage Collision Interactions Insurance Score by Length of Residence ity Relativi Missing < Insurance Score Group

6 Identify Additional Segmentation Factors Determine the significance of additional factors that may have been deemed insignificant when insurance score was included Consider factors not currently being used As a basis for consideration of new factors, focus on the behavioral drivers of insurance/credit risk Other Variables to Consider Examples Payment history with company Payment history with other creditors BI Limit Umbrella limits Presence of recreational vehicles (boats, snowmobiles, golf carts) Payment plan Accident and violation history Marital/family status Motorcycle insured? Behavioral profile Years insured Years employed Years at residence Endorsement activity 2010 Pinnacle Actuarial Resources, Inc. Prior Endorsement Activity 6

7 Additional Analysis Techniques Data Mining Cluster/segmentation analysis Principal components Association analysis Predictive Modeling Generalized Linear Modeling Decision trees Linear regression models Neural networks Application Direct analysis of frequency/severity/loss cost Analysis of residual based on GLM fit As part of a risk score development Example of Analysis Using Multiple Techniques Linear Regression GLM Loss Cost Analysis Principal Components Residuals Decision Tree Score/Group Development GLM Loss Cost Analysis Separate frequency & severity analysis Combine into a pure premium analysis Neural Networks Credit Score Relativities Normal Analysis 7

8 New Risk Group After Residual Analysis When replacing Credit with New Risk Group - achieve 64% of segmentation of credit just by more sophisticated analysis of current variables. What Happened in Maryland Insurance score used in auto and homeowners until. Banned from homeowners rating effective October 1, 2002 Outcome Removal of credit from rating plans rate inequities Rate innovation as companies adjusted to life without credit Result overall market adapted, and 2010 Pinnacle Actuarial Resources, Inc. A Few Companies are Differentiating Themselves Incurred Loss Ratio Years Better Than Industry DWP Growth Years Better Than Industry

9 What is the Next Credit? Characteristics of credit scores as a risk segmentation tool Significant separation of indicated rating differences Reasonable distribution of risks across the scale Lack of overlap with existing characteristics What was the last credit? What is the next credit? Usage Based Insurance Driving Forces Strong relationship to exposure Safety Environmental concerns Public acceptability Data Elements Captured Mileage driven Time of day Speed Braking Sharpness of turns Where you drive Usage Based Plans Progressive MileMeter GMAC Unigard Insure The Box (UK) Liberty Mutual (commercial) Real Insurance (Australia) AIOI (Japan) MiWay (South Africa) Usage Based Insurance Programs Progressive Snapshot Discount TM Available in 25 states Device cost ranges from $0 $30 Elements: miles, time of day, sudden stops, speed Based on up to a 75 day snapshot of driving history Advertised discounts of up to 60% MileMeter Available in Texas Insurance priced in cents per mile (purchase 1,000 6,000 miles every six months) Mileage verified by photo of odometer Advertised savings of up to 75% 9

10 Usage Based Insurance Programs Insure the Box UK mileage based insurance program Purchase an initial bank of 6,000 miles Purchase additional miles as needed Can also earn reward miles by purchasing from selected retailers and safe driving Elements: number of trips, miles, roads used, speed, time driving, acceleration and braking Property Characteristics Property Total Living Area Year Built Number of Stories/Style Number of Families Foundations Finished Basements Exterior Wall Roofing Number of Baths Fireplaces Swimming pools Trampolines Real Estate elements Mortgage value Market value Interest Rates Loan terms Hazard Elements Distance to Fire Station Distance to Coast Flood zone Brush Fire Earthquake Elevation Sink hole Property Characteristics 10

11 Territory Definitions Issues with current territory definitions Outdated Boundaries can be difficult to understand Do not vary by coverage/peril Have historically not been developed taking full advantage of available data Leads to Misclassification Mis interpretation of other factors Anti selection Revising Territory Definition Data Company and industry historical data Catastrophe model output Level of location detail County ZIP code Census block Census Tract Address Latitude and longitude Analysis smoothing and clustering Territory Rating 33 11

12 Data Daytime running lights ESC/DSC Weight Engine size Segmentation CID Body type Vehicle Characteristics Cylinders Driving wheels High performance code Transmission Wheel base Height Width Applications Liability symbols Enhanced physical damage classification New model classification Example Company Vehicle Classification Job Run 2 Model 1 - Collision Pure Premium - Smoothed standard risk premium model (single claim type) % multip lier Log of % 0.4 0% -8% -5% -12% 0-18% -17% -14% % 60% 65% 40% 40% 43% 31% 32% 11% -15% -21% 82% 48% % 9% -15% u re (y ears) Exp osu % A B C D E F G H J K L M N P R U X 0 MSEGMENTATION_CODE Onew ay relativities Unsmoothed estimate Smoothed estimate 12