Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations

Abstract

An important development in personal lines of insurance in the UnitedStates is the use of credit history data for insurance risk classification to predict losses. This research presents the results of collaboration with industry conducted by a university at the request of its state legislature. The purpose was to see the viability and validity of the use of credit scoring to predict insurance losses given its controversial nature and criticism as redundant of other predictive variables currently used. Working with industry and government, this study analyzed more than 175,000 policyholders' information for the relationship between credit score and claims. Credit scores were significantly related to incurred losses, evidencing both statistical and practical significance. We investigate whether the revealed relationship between credit score and incurred losses was explainable by overlap with existing underwriting variables or whether the credit score adds new information about losses not contained in existing underwriting variables. The results show that credit scores contain significant information not already incorporated into other traditional rating variables (e.g., age, sex, driving history). We discuss how sensation seeking and self-control theory provide a partial explanation of why credit scoring works (the psycho-social perspective). This article also presents an overview of biological and chemical correlates of risk taking that helps explain why knowing risk-taking behavior in one realm (e.g., risky financial behavior and poor credit history) transits to predicting risk-taking behavior in other realms (e.g., automobile insurance incurred losses). Additional research is needed to advance new nontraditional loss prediction variables from social media consumer information to using information provided by technological advances. The evolving and dynamic nature of the insurance marketplace makes it imperative that professionals continue to evolve predictive variables and for academics to assist with understanding the whys of the relationships through theory development.

The Cost of Legal Restrictions on Experience Rating

“…As noted above, this suggests that there is a latent, domain-general component to risk type. This finding adds to a growing body of evidence that riskiness is a trans-substantive characteristic of individuals (e.g., Barksy et al 1997;Dohmen et al 2011;Golden et al 2016). It also complements existing research suggesting that risk aversion, though not completely stable across contexts (Barseghyan et al 2011), also has a latent, domain-general component (e.g., Einav et al 2012;Barseghyan et al 2016).…”

Do Credit-Based Insurance Scores Proxy for Income in Predicting Auto Claim Risk?

“…What's more, our results tend to buttress the claim advanced by the insurance industry, as well as by Brockett and Golden (2007) and Golden et al (2016), that insurance scores are predictive of risk because they operate as a rough measure of policyholders' "responsibility" or level of caution. Of course, our results do not directly test this hypothesis for the same reason that no prior study has been able to do so: it is not clear how researchers could measure policyholder responsibility independently of credit-based information.…”

“…Many property and casualty insurers use insurance scores in their actuarial models for their automobile and homeowners coverage lines. The widespread use of insurance scores in these lines of coverage stems from a simple fact: they are predictive of claim risk (Miller and Smith 2003;Golden et al 2016). At the same time, however, insurance scores may be correlated with one or more suspect classifications, including, most importantly, race and income.…”

“…Simply put, it is not obvious why individuals who take on too much credit or fail to keep up with their bills are more likely to experience a car accident, home fire, or any other insured loss. To be sure, many have offered explanations, most often arguing that people with poor credit scores are less careful or responsible in general, and thus tend to be more likely to experience accidents (Brocket and Golden 2007;Golden et al 2016). But these claims are difficult, if not impossible, to prove.…”

The actuarial science editorship of the Accounting & Finance Review: some general considerations

“…This article addresses the transitions for disability and death to long-term-care (LTC) insurance, using of a generalized linear model. In 2016, the paper awarded was "Empirical evidence on the use of credit scoring for predicting insurance losses with psychosocial and biochemical explanations" (Golden, Brockett, Ai & Kellison, 2016). This second paper introduces an innovative approach, in which the credit score of insurers is employed to improve auto insurance claims reserves.…”