“Now there should be no doubt about the value of using this highly predictive underwriting and rating tool,” said June Holmes, interim CEO for PCI. “Using credit information makes underwriting and pricing more accurate and results in many consumers paying less for their automobile and homeowners insurance policies. Consumers want to pay a fair price for insurance that matches their risk of loss. To achieve the goal of pricing based on an individual’s risk of loss; insurers simply want to use the most accurate, statistically valid tools available and credit information has proven to be one of the best predictors of loss. With these findings, legislators and regulators should be very comfortable with insurers’ use of insurance scoring.”
The FTC study was designed to explore the effect that credit information has on the availability and affordability of insurance. The study considered the statistical relationship between credit information and the risk of loss. It also examined the extent to which, if any, the use of credit scoring models, credit scores, and credit-based insurance scores affect the availability and affordability of insurance by racial and ethnic minority groups and low-income groups.
The report’s major conclusions are as follows:
• Insurance scores are effective predictors of risk under automobile policies. They are predictive of the number of claims consumers file and the total cost of those claims. The use of scores is therefore likely to make the price of insurance better match the risk of loss posed by the consumer. Thus, on average, higher-risk consumers will pay higher premiums and lower-risk consumers will pay lower premiums.
• Use of credit-based insurance scores may result in benefits for consumers. For example, scores permit insurance companies to evaluate risk with greater accuracy, which may make them more willing to offer insurance to higher-risk consumers for whom they would otherwise not be able to determine an appropriate premium. Scores also may make the process of granting and pricing insurance quicker and cheaper, cost savings that may be passed on to consumers in the form of lower premiums.
• Credit-based insurance scores appear to have little effect as a “proxy” for membership in racial and ethnic groups in decisions related to insurance. The relationship between scores and claims risk remains strong when controls for race, ethnicity, and neighborhood income are included in statistical models of risk.
“The FTC study demonstrates that by looking at an insurance score there is no way to determine a person’s race, ethnicity, or economic status,” said Holmes. “However, a specific high or low score will offer strong evidence as to the likelihood of that person filing an insurance claim. Being able to make this distinction regarding risk of loss allows insurers to charge each individual an appropriate rate. The bottom line is that insurers underwrite individuals, not ethnic or income groups, and the use of credit information results in each individual’s premium more accurately their risk of loss.”
Insurance scoring has proven to be a highly accurate and objective factor. Previous studies have found that the average loss per vehicle for people with the worst scores are double that of people with the best scores and drivers with the best scores are involved in 40 percent fewer accidents than those with the worst scores. Additionally, homeowners insurance claims history for people with the worst scores are triple that of people with the best scores. Earlier