Insurance-Based Credit Scoring Individual and contextual correlates of risk A Review of Empirical Evidence

1. Insurance-Based Credit Scoring Individual and contextual correlates of risk A Review of Empirical Evidence Presentation to the Casualty Actuarial Society Special Interest Seminar Chicago Omni Chicago Hotel October 4th, 2004 Brent Kabler, Ph.D. Supervisor of Research Division of Market Regulation Missouri Department of Insurance

2. Should actuaries and / or insurance regulators be concerned with causality? Stated differently, should actuaries and policy makers be concerned about “spurious” relationships in insurance rating and underwriting?

3. Causal understanding is requisite to explain and intervene. • Fewer causal unknowns improves prediction (the sole objective of actuaries, some might argue). • Important public policy implications arise from cause and effect relationships • Intuitive understanding (and acceptance) among insureds (actuarial standards of practice) • Results in greater transparency

4. Causal Paths Unknown Factors Credit Score Risk The relationship between credit scores and risk is, strictly speaking, spurious: credit scores serve as a proxy for (currently) unknown and unmeasured correlates of risk.

5. Paths of Causality Socioeconomic Status Credit Score Risk Personality Characteristics (Risk taking, accident proneness, etc) Known Relationship Unknown or Contested Relationship

6. Socio-Economic Status Risk Evidence is conclusive that socioeconomic status is strongly correlated with risk. In fact, socioeconomic status is the single best predictor of a wide variety of risks.

7. Risk and Socioeconomic Status: Evidence from around the world Source: UNICEF, Innocenti Report Card, Issue No. 2, February, 2001.

8. Risk and Socioeconomic Status: Evidence from around the world Source: Center for Disease Control. 2002. Summary Health Statistics for U.S. Children: National Health interview Survey, 1997. Vital Health Statistics 10(203).

9. Risk and Socioeconomic Status: Evidence from around the world Source: University of Otago Research Team. 2000. Social & Economic Deprivation and Fatal Unintentional Domestic Fire Incidents in New Zealand, 1988-1998.

10. Risk and Socioeconomic Status: Evidence from around the world Source: Missouri Department of Insurance, Statistics

11. Risk and Socioeconomic Status: Evidence from around the world In summary, individuals in relatively deprived life circumstances face a greater risk of practically everything: numerous illnesses (heart disease, diabetes, high blood pressure, cancer, asthma, lead poisoning, etc), accidents (automobile, falling, fire), violent assault and injury (robbery, gunshot wounds), and pretty much anything one might think of that poses a risk to life and limb. One reasonable hypothesis, then, is that credit scoring is a proxy for the risk of socioeconomic status.

12. Personality based correlates of risk – Empirical Evidence Alternative hypothesis – Credit scores are a kind of “personality test” that identify risk prone individuals Personality: A complex of enduring and measurable behavioral traits that persist through time. There exists voluminous literature on “accident prone” personality types, relevant to credit scoring.

13. The “accident prone” individual There exists voluminous literature on “accident prone” personality types. Personality: A complex of enduring and measurable behavioral traits that persist through time. Specific identifiable traits are known to be correlated with the propensity to have accidents.

14. Personality based correlates of risk – Empirical Evidence Tillman and Hobbs, 1947. “It would appear that the driving hazards and the high accident record are simply one manifestation of a method of living that has been demonstrated in their personal lives…, if his personal life is marked by caution, tolerance, foresight, and consideration for others, then he would drive in the same manner. If his personal life is devoid of these desirable characteristics, then his driving will be characterized by aggressiveness and over a long period of time he will have a much higher accident rate than his stable companion.”

15. Personality based correlates of risk – Empirical Evidence Note: the association between credit history and risk was identified as early as 1949. Source: Tillman,W. A. and G. E. Hobbs. 1949. The accident-prone automobile driver. American Journal of Psychiatry. 106(5): 321-331.

16. McGuire, F. L. 1972. A study of methodological and psycho-social variables in accident research. JSAS Catalog of Selected Documents in Psychology. Auto Accidents highly correlated with 1. Aggressiveness 2. Prestige seeking 3. Authority orientation rather than cooperation 4. Competitiveness

17. Shaw, L. 1965. The practical use of projective personality tests as accident predictors. Traffic Safety Research Review. 9: 34-72. Reports on a multi-year project initiated in 1953 to recruit safe bus drivers in South Africa. Involved more that 500 drivers, 85 buses logging more than 12 million miles per year. Perfected test reached over 80% accuracy in predicting which drivers would be involved in an accident. High accident group was: 1. Unobservant, unadaptable, disorganized, or disoriented 2. Emotionally unstable and extremist 3. Lacks self-control, particularly aggression 4. Anti-social attitudes, even criminal tendencies 5. Selfish and self-centered 6. Over confident and assertive 7. Irritable and cantankerous, harbors grudges, grievances, and resentments 8. Blame avoidance, always ready w/ excuses 9. Feels inadequate, w/ a driving need to prove themselves

18. Competing causal hypotheses possess profound implications for regulators and policy-makers. • Questions • Is a spurious rating variable permissible? Does it require additional scrutiny? • What if the variable is proxy for prohibited factors? • How do variables that serve as proxies for socioeconomic status impact areas that already confront affordability and availability problems?