By James Lynch, Chief Actuary, Insurance Information Institute

 

 

With automobile rating factors in the news, here at Insurance Information Institute we have been fielding a number of calls on the topic. Here is some background information.

I testified May 1 before Congress, and rating factors were among the issues I was asked about. Here is that testimony. Here is a link to a webcast of the hearing.

The National Conference of Insurance Legislators has a model act on use of insurance scores that about 40 states have adopted.

Insurance scores have been thoroughly examined for around two decades, and there is no doubt that they are good at predicting the likelihood of loss. The NAIC has a roundup of scholarship here.

There is concern that the scores act as a proxy for income, a variable that insurers are banned from using. Here is recent research questioning that assumption, “Do Credit-Based Insurance Scores Proxy for Income in Predicting Auto Claim Risk” (This study “finds that insurance score does not act as proxy for income in a standard actuarial model of auto claim risk.” It is also notable because one of the authors, Daniel Schwarcz, served as consumer representative to the National Association of Insurance Commissioners from 2007 to 2014.)

And we get asked a lot why an insurance credit score can predict whether someone is likely to be in an accident. Here’s a study: “Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations” From the study: “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).”

Here is a I.I.I. background paper on insurance scores.