Glossary
Price optimization
Pricing a policy by how much a customer will tolerate or how likely they are to shop, not by expected loss, a practice a number of states have restricted.
Price optimization means adjusting a customer's rate using factors that predict behavior rather than risk. The NAIC's white paper on the subject, adopted in 2016, describes it as using modeling to move rates toward business goals such as retaining customers. A typical example is charging a policyholder who is unlikely to shop around more than an identical neighbor who is likely to leave. The risk of loss is the same, but the price differs. Critics call the result a loyalty penalty.
An insurer builds a model of price sensitivity from renewal, quote and shopping data, then applies a smaller discount or a bigger increase to low-sensitivity customers. Say two homes carry identical risk, and the model predicts one owner will accept a 9% increase while the other would leave (hypothetical). An optimized rate gives them different premiums.
Regulators in California, Maryland, Ohio and Connecticut, among others, issued bulletins or orders in 2015 and after that limit or prohibit the practice in personal lines, on the ground that rates must not be unfairly discriminatory. The NAIC paper itself is not a ban, and rules vary by state, so ask your department of insurance what applies. See the loyalty penalty guide and the rate filing entry.
Last reviewed October 10, 2026