Insurance Companies Using Credit Scores

Insurance Credit Score

Insurance premium is always based on risk.  Homes with an alarm system tend to have less burglaries, thus generating a lower premium.

Simply generalized, discounts are given for less risk and surcharges are imposed for more risk.  Insurance carriers also consider credit history as a rating factor; there are actually only 3 states which actually do not allow credit scoring as a rating factor for homeowners insurance; naturally, those states have higher premium rates for everyone except those who have less than desirable credit scores.

According to the Property Casualty Insurers Association of America, the industry has proven that credit scoring is directly related to risk of loss.  As of 2017,  only California, Maryland and Massachusetts are not allowing credit scoring as a rating factor for home insurance.

Many social groups have gathered to allege that insurance credit scoring leads to racially prejudicial treatment of minority groups; further alleging that insurance credit scores are a proxy for race therefore finding the practice unfair and discriminatory. These allegations may have been supported by the fact that minority groups have a larger percentage of members with lower than average credit scores.  The soaring truth would simply be that those with higher credit score regardless of their race would pose less risk and therefore pay lower insurance rates.  The credit scoring procedure is based on credit scoring and in no way, shape or form asks about the applicant’s race.

Insurance companies are in business to research risk, market their desirable appetite, underwrite risk and hopefully generate an underwriting profit from their complicated business process; each state limits the underwriting gain allowed, there are only a few states which allow over 10% profit before requiring rate roll backs.  Thus, the profit is limited but the losses are not.  Insurance carriers rely on actuarial firms to provide the necessary data to safeguard them from losses and optimize the chance of gain.