Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

ABSTRACT The most important new development in the past two decades in the personal lines of insurance may well be the use of an individual's credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation-seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individual's biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses. "... a man drives as he lives." Tillman and Hobbs, 1949, p. 329 "If serotonin is the brakes, dopamine is the accelerator in the drive to risky behavior." Zuckerman and Kuhlman, 2000, p. 1021 "A drug given to Parkinson's patients may have an unexpected side-effect--compulsive gambling, U.S. researchers say. An unusually large number of patients taking pramipexole [a dopamine agonist] gambled themselves into debt, while patients taking other drugs did not, the team at the Muhammad Ali Parkinson Research Centre in Phoenix, Arizona, reported." Reuters, April 13, 2003 INTRODUCTION AND BACKGROUND Perhaps the most important new development in the past two decades in insurance is use of a person's credit history to predict losses. To this end, a formula is constructed using approximately 10-50 of more than 450 variables obtainable from an individual's credit file to derive a summary numerical score for predicting insurance losses (as opposed, e.g., to banks using credit records to predict default (credit risk) or its use in commercial insurance). The positive statistical relationship between credit scores(1) and insured losses has been verified by multiple studies and multiple methods, and no study reporting a lack of statistical relationship has been published in the refereed literature. In automobile insurance, for example, Miller and Smith (2003) found that of six possible automobile coverages, credit scores are always in the top three most important loss predicting variables, and often the most important variable. Credit score is the first variable considered in personal injury protection and medical payments coverage, second in bodily injury and property damage coverage (behind age and gender), and third in comprehensive and collision coverage (age and gender being second, with make and model of the car dictating costs more). Credit history has, in fact, been used for decades in commercial lines of insurance and life insurance. Although it has been known since at least 1949 that credit history is related to driving accidents, the advent of high capacity, high-speed computers has made massive personal credit files available, and has made it feasible to routinely use this credit information for predicting insurance losses in personal lines of property and casualty insurance. Tillman and Hobbs (1949) show that drivers with bad credit history have repeated crashes at a rate six times higher than those with good credit history. Moreover, a 1968 study of Washington state drivers (Insurance Institute for Highway Safety, 2003) showed that within the group of drivers who had a history of no automobile accidents, 64 percent had good credit while 35 percent had bad credit. …