Does Credit Score Really Explain Insurance Losses? Multivariate Analysis from a Data Mining Point of View

One of the most significant developments in insurance ratemaking and underwriting in the past decades has been the use of credit history in personal lines of business. Since its introduction in the late 80's and early 90's, the predictive power of credit score and its relevance to insurance pricing and underwriting have been the subject of debate. The fact that personal credit is widely used by insurers strongly suggests its power to explain insurance losses and profitability. However, critics have questioned whether the apparently strong relationship between personal credit and insurance losses and profitability really exists. Surprisingly, even though this is a hot topic in the insurance industry and in regulatory circles, actuaries have not been actively participating in the debate. To date, there have been few actuarial studies published on the relationship of personal credit to insurance losses and profitability. We are aware of only two such studies: one published by Tillinghast, which was associated with the NAIC credit study and the other by Monoghan. A possible reason for the lack of published data is that many insurers view credit scores as a confidential and cutting-edge approach to help them win the market place. Therefore, they might be reluctant to share their results with the public. In this paper, we will first review the two published studies and comment on their results. We will then share our own experience on this topic. We have conducted a number of comprehensive, large-scale data mining projects in the past that included credit information as well as an extensive set of traditional and non-traditional predictive variables. Because our projects have been true multivariate studs, conducted using rigorous statistical methodology on large quantities of data, our experience should add value to the debate. Our experience does suggest that such a relationship exists even after many other variables have been taken into account.