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Midwest Actuarial Forum September 22, 2005 Rick Smith and Greg Hayward. FACT ACT Study of the Effects of Credit Scores and Credit-Based Insurance Scores on the Availability and Affordability of Financial Products. Fair and Accurate Credit Transactions Act of 2003 (FACT ACT) (62 Pages).
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Midwest Actuarial ForumSeptember 22, 2005 Rick Smith and Greg Hayward FACT ACT Study of the Effects of Credit Scores and Credit-Based Insurance Scores on the Availability and Affordability of Financial Products
Fair and Accurate Credit Transactions Act of 2003(FACT ACT)(62 Pages) An Act to amend the Fair Credit Reporting Act, to prevent identity theft, improve resolution of consumer disputes, improve the accuracy of consumer records, make improvements in the use of, and consumer access to, credit information, and for other purposes. This session will focus on one section of the FACT Act.
Section 215 of the FACT Act Requires a Study Who: Federal Trade Commission and Federal Reserve Board What: Credit Cards, Mortgages, Auto Loans, and P&C Insurance How: Statistical Relationship Utilizing Multi- Variate Analysis that controls for prohibited factors When: Due to Congress by December 4, 2005
FACT Act Study • One Joint Report to Congress • FTC – Will focus on Auto and Homeowners Insurance • Federal Reserve Board – Will focus on Banking Products • NAIC Multi-State Data Call was Withdrawn – Five Member State Panel was established by NAIC to work with FTC
Three Trade Associations Are Coordinating Industry Cooperation With the FTC • American Insurance Association (AIA) • National Association of Mutual Insurance Companies (NAMIC) • Property Casualty Insurance Association Of America (PCIAA)
Actions Taken by the Three Trades • Established a Technical Support Group • Recruited Data Providers for both the Auto and Homeowners Study • Responded to Numerous Requests from the FTC
FTC Request for Comments in the Federal Register: 1. June 18, 2004 2. February 17, 2005
June 18, 2004 FTC Request • Ten questions asking about methodology and research design: • How should effects of credit be studied? (methodology) • What is hypothetical alternative situation? • What is appropriate multivariate technique? What data? • How to determine whether negative impact exists? • What methodology will determine specific (credit) • factors causing problem? • Same for factors not in credit scores • Are data available? • If not, what are proxies? • If proxies, what analysis would allow inferences to be drawn? • If only proxy is Census-level, what analysis would allow • inferences to be drawn?
June 18, 2004 FTC Request • 112 Responses are shown on the FTC website (www.ftc.gov) • Most were from individuals with personal complaints about the use of credit. • American Academy of Actuaries • Use insurance loss data • Don’t try for “causal relationship” • Define yardstick needed to establish concern
June 18, 2004 FTC Request • Center for Economic Justice • How would consumer be treated w/o credit? (Although they would do so improperly) • AAA/SOP – Econometric, not actuarial • Don’t use Industry Study database – can’t check for “intentional bias” • Don’t study Loss Ratio • Try to get policyholder-level race, etc. via SSA, DMV’s, etc. Try surname for some groups • Get application data, not just policies issued (e.g. broad data call) • Suggest also studying average credit score as independent variable • Capture proof of payment on expenses such as rent and utilities
June 18, 2004 FTC Request • Others • Coalition to Implement the FACT Act • (insurers, other financial institutions) • Credit Bureaus • Specific insurers Need to control for geographic risk • If race proxy is used, use Census Block • Don’t use average values, and • extrapolate (like MO study) • Nat’l Assoc. of Realtors • Causal analysis • Longitudinal analysis
June 18, 2004 FTC Request • Others (continued) • Trade associations: • Model losses – with and without insurance score • Study residual market population • Study ‘lift’ among classes • Recognize geographic differences • Study non-controversial variable first: e.g. driving record
FTC Insurance Study PlanAs Presented by FTC at CAS Seminar • Append race and income to subset of sample, along with FICO score. • Build one model w/o risk scores, 2nd model with risk scores. Compare the two. • Add race/income to model. Will test to see if score is predictive within demographic groups. • Will test individual attributes.
February 28, 2005 FTC Request • Twenty-one questions asking about how models are developed and used plus evidence regarding the effect on expenses, accuracy, availability, and affordability. • 101 Responses are shown of the FTC website (www.ftc.gov) • Some Key Advice Provided to the FTC: • Numerous personal complaints about the use of credit. • Interesting study by NCRC – suggests study of non-prime (and non-traditional data sources – rent, utility payments etc.) • Michigan OFIS concern - length of time credit affects scores
Data Calls • Trade associations working with member companies • Multiple companies providing data voluntarily • Separate data calls for Auto and Homeowners.
Auto Data Call • Will use subset of data from 2003 Insurance Score study • Over 2 million earned car years • Majority of original data providers • Industry group working with FTC to add: • Credit attributes • Census block (via geo-coding address) • Vehicle MSRP • File with name/address will be handled separately • Add SSN • Send to Soc. Sec. Administration – add self-reported • race and income information • This data will be sent directly to FTC • Separate (new) call for ZIP code data • Multiple years • All data for three years • Have “clustered” Property Damage results
Homeowners Data Call • New database • Will collect three years of data by peril • Approx. 3 million policies, 7 million exposures • Industry group working with FTC and vendors to add: • Insurance scores • Credit attributes • Census block (via geo-coding address) • CLUE • File with name/address will be handled separately • Add SSN • Send to Soc. Sec. Administration – add self-reported • race and income information • This data will be sent directly to FTC • Separate call for ZIP code data • Multiple years • All data for three years • Will analyze and clustered results
FTC Insurance Study PlanAs Presented by FTC at CAS Seminar • FTC is interested in protecting and preserving US economy. • FTC realizes parties feel strongly on both sides of this issue, but FTC will build a balanced report. • FTC believes data base is fair and is generally pleased with the industry’s cooperation. • Final detailed report will include many results, charts, and graphs.