Insurance-Based Credit Scores: Impact on Minority and Low Income Populations in Missouri

and Overview The widespread use of credit scores to underwrite and price automobile and homeowners insurance has generated considerable concern that the practice may significantly restrict the availability of affordable insurance products to minority and lowincome consumers. However, no existing studies have effectively examined whether credit scores have a disproportionate negative impact on minorities or other demographic groups, primarily because of the lack of public access to appropriate data. This study examines credit score data aggregated at the ZIP Code level collected from the highest volume automobile and homeowners insurance writers in Missouri. Findings—consistent across all companies and every statistical test—indicate that credit scores are significantly correlated with minority status and income, as well as a host of other socio-economic characteristics, the most prominent of which are age, marital status and educational attainment. While the magnitude of differences in credit scores was very substantial, the impact of credit scores on pricing and availability varies among companies and is not directly examined in this study. The impact of scores on premium levels will be directly addressed in studies expected to be completed by late 2004. Missouri statue prohibits sole reliance on credit scoring to determine whether to issue a policy. However, there are no limits on price increases that can be imposed due to credit scores, so long as such increases can be actuarially justified. This study finds that: 1. The insurance credit-scoring system produces significantly worse scores for residents of high-minority ZIP Codes. The average credit score rank in “all minority” areas stood at 18.4 (of a possible 100) compared to 57.3 in “no minority” neighborhoods – a gap of 38.9 points. This study also examined the percentage of minority and white policyholders in the lower three quintiles of credit score ranges; minorities were overrepresented in this worst credit score group by 26.2 percentage points. Estimates of credit scores at minority concentration levels other than 0 and 100 percent are found on page 8. 2. The insurance credit-scoring systems produces significantly worse scores for residents of low-income ZIP Code. The gap in average credit scores between communities with $10,953 and $25,924 in per capita income (representing the poorest and 1 Results are presented here as ranks, or more accurately, percentiles. Because of significant differences in the scoring methods of insurers, many of the results in this report are presented as percentiles rather than as percentage differences in the raw credit scores. Anyone who has taken a standardized test should be familiar with the term. Scores for each company in the sample are ranked, and each raw score is then translated according to its relative position within the overall distribution. For example, a score ranked at the 75th percentile means that the score is among the top one-fourth of scores, and that 75 percent of recorded scores are worse. If the average for non-minorities was at the 30th percentile, and the minority average at the 70th percentile, the percentile difference is 40 percentiles. The percentile difference, calculated from the statistical models, is used herein as a convenient way to summarize results for the non-technical reader.