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Why Low Credit Scores Predict More Auto Liability Claims: Two Theories

Why Low Credit Scores Predict More Auto Liability Claims: Two Theories. Patrick Butler* American Risk & Insurance Association August 7, 2007 *National Organization for Women pbutler@centspermilenow.org. #774.7804. Policy dilemma: get “A” w/o getting “B”. Mandatory liability insurance

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Why Low Credit Scores Predict More Auto Liability Claims: Two Theories

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  1. Why Low Credit Scores Predict More Auto Liability Claims: Two Theories Patrick Butler* American Risk & Insurance Association August 7, 2007 *National Organization for Womenpbutler@centspermilenow.org #774.7804

  2. Policy dilemma: get “A” w/o getting “B” • Mandatory liability insurance • Public demands it • Insurers oppose it because of “B” • Price regulation • To keep mandatory insurance affordable • Recent example is initiatives to ban or regulate credit score (CS) pricing • In response auto insurers commissioned a large study completed in 2003

  3. Figure 1. Liability Claims vs. Credit Scores(from Miller & Smith 2003) +90% • Claims per 100 car years: +90% to -25% of average • 1.90 / 0.75 = 2.5 times -25%

  4. Theory 1 “More driver negligence” • Basis for theory • Each liability claim requires a negligent act by insured car’s driver • Logic • Cars of low CS drivers average more liability claims • Therefore these drivers are more negligent

  5. Theory 1 (continued)Explanations • Miller & Smith 2003 (insurance industry explanation) “[Credit-based] scores seem to provide an objective means of measuring personal responsibility and its effect on insurance losses.” • Brockett et al. 2005 (1st academic study) • Presented at WRIEC, Salt Lake City • Published 2007

  6. Theory 1 (continued)biological explanation Brockett & Golden 2007 (J. Risk & Insurance) • Title (underline added): “Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works” • Conclude that the research examined by their study “suggests that the discussed individualized biological and psychobehavioral correlates provide a connection between credit scores and automobile insurance losses.“

  7. Model for Theory 1 is the biological explanation for correlation with driver age Involvement Rate =Accidents per 1,000,000 miles (from Williams 1999) • Driver Age • Similar age effects confirmed per worker hour of exposure

  8. Theory 1 Unaddressed Matters • Claims & accidents are referenced to driver- and car-years, but individual annual-miles exposures vary widely • A conflict with risk aversion theory • Greater financial constraint predicts more risk aversion, i.e., less negligence • Insurers report that lower CS also predicts more uninsured motorist (UM) claims. • But a UM claim requires non negligence by the insured car’s driver

  9. CS Theory 2“More miles per insured car year” • UM & Liability Claims must correlate (+): • The more miles a group of cars averages, the more claims per 100 car years the group produces • more negligence claims, and also • more non-negligence (UM) claims • This means that the cars of financially-constrained drivers must be averaging more miles. Why so? • But first some basics:

  10. Basics for Theory 2 • Given – Accidents are a cost of car operating • Given – Premiums are a cost of car owning • But is the range in annual miles enough to explain the 2.5 times CS variable range in claims per 100 car years?

  11. Fig. 2. Yes. Household Cars Distributed by Odometer Miles Shows Range • Subgroups by car age, and by driver sex and age (despite difference in averages) span entire annual-miles range • So cars within insurance classes span low- to high-miles

  12. Figure 3 (Recasting of Figure 2) • The CS range in claims: 2.5 times = 15,000 mi / 6,000 mi

  13. Figure 4. Why miles must be individually measured • Avg miles explain why new cars average more liability claims than old cars • But distribution shows many new cars are driven fewer miles than old cars

  14. Logic of Theory 2 illustrated(Hard but sure way to economize)

  15. Theory 2 fits other predictors • Zip code income (-) • Education & occupation (-) • No prior insurance (+) • Installment plan (+) • Any marker of tight budgets predicts more liability claims per 100 car years Therefore, the highest per-car premiums are charged to those who can least afford them

  16. Theory 2 recommends • Informed by Theory 2, the strong public demand for enforcing mandatory liability insurance could be accompanied by • A strong demand that automobile insurers provide the audited odometer-mile exposure unit as an option • At cents-per-odometer-mile class prices this option would constitute a free-market remedy for the upward cost-price spiral that the traditional car-year exposure unit sets off for groups of economizing drivers • With this option drivers could car pool or take the bus to save on insurance while keeping their own cars insured and available for use

  17. Table 2. What’s at stake – the challenge for both theories e. g., Albany, NY: lowest and highest quoted for one car class profile (same driver age, record, sex, and marital status). • Range: $1,136 / $258 = 4.4 times

  18. Conclusion – Research questionWhich policy response to the dilemma “free-market vs. affordability vs. mandatory insurance” • Theory 1, some issues • Identify the negligent driver groups on an accidents-per-1,000,000-mile basis • Find incentives to reduce negligence per-mile • Theory 2, some issues (discuss in Q & A) • Federal surveys show average miles per car year decreases moderately as household income decreases • Some high premiums for adult-driver-class cars may reflect not only higher miles per car, but also the higher per 1,000,000 mile accident involvement rates of “undisclosed” young and old drivers sharing the cars

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