Background
Insurance scoring models are built from selections of credit report factors, combined with insurance claim and profitability data, to produce numerical formulae or algorithms. A scoring model may be unique to an insurance company and to each line of business (e.g. homeowners or automobile), in terms of the factors selected for consideration and the weighting of the point assignments. As insurance credit scores are not intended to measure creditworthiness, they commonly focus on financial habits and choices (i.e., age of oldest account, number of inquiries in 24 months, ratio of total balance to total limits, number of open retail credit cards, number of revolving accounts with balances greater than 75% of limits, etc.) Therefore it is possible for a consumer with a high financial credit score, and excellent payment history, to receive a poor insurance score. Insurers consider credit report information in their underwriting and pricing decisions as a predictor of profitability and risk of loss.
Various studies have found a strong relationship between credit-based insurance scores and profitability or risk of loss. The scores are generally most predictive when little or no other information exists, such as in the case of clean driving records, or claims-free policies; in instances where past claims, points, or other similar information exist on record, the personal histories will typically be more predictive than the scores. Insurers consider credit report information, along with other factors, such as driving experience, previous claims and vehicle age, to develop a picture of a consumer's risk profile and to establish premium rates. The correlation, between credit-based insurance scores and overall insurance profitability and loss, has not been disputed.
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