In July 2007, The Federal Trade Commission (FTC) released a report presenting the results of a study concerning credit-based insurance scores in automobile insurance. The study found that these scores are effective predictors of risk. It also showed that African-Americans and Hispanics are substantially overrepresented in the lowest credit scores, and substantially underrepresented in the highest, while Caucasians and Asians are more evenly spread across the scores. The credit scores were also found to predict risk within each of the ethnic groups, leading the FTC to conclude that the scoring models are not solely proxies for redlining. The FTC indicated little data was available to evaluate benefit of insurance scores to consumers. The report was disputed by representatives of the Consumer Federation of America, the National Fair Housing Alliance, the National Consumer Law Center, and the Center for Economic Justice, for relying on data provided by the insurance industry.
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At the most basic level, initial ratemaking involves looking at the frequency and severity of insured perils and the expected average payout resulting from these perils. Thereafter an insurance company will collect historical loss data, bring the loss data to present value, and compare these prior losses to the premium collected in order to assess rate adequacy. Loss ratios and expense loads are also used. Rating for different risk characteristics involves at the most basic level comparing the losses with "loss relativities"—a policy with twice as many losses would therefore be charged twice as much. More complex multivariate analyses are sometimes used when multiple characteristics are involved and a univariate analysis could produce confounded results. Other statistical methods may be used in assessing the probability of future losses.
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