AI underwriting helps an insurance carrier estimate what an employer group may cost to insure. It reviews information such as the group’s employee census (ages, locations, number of dependents, and enrollment tiers), plan design, industry, and medical and prescription claims. AI looks for patterns that may predict future costs, such as higher use of specialists, hospital services, or expensive prescription drugs.
If the in-force carrier, the main “databases” are usually the carrier’s own claims and enrollment records, pharmacy-benefit-manager prescription data, provider-network cost data, and broad industry benchmark databases.
Some data sources contain medical claims, pharmacy claims, diagnoses, eligibility, premiums, and risk scores from millions of covered people. AI uses these large data sets to estimate whether a particular employer group is likely to have costs that are above or below average.
There is an important difference between small and large groups. For ACA-regulated small-group coverage, insurers generally cannot use the employer’s health history or claims experience to set rates; pricing is limited mainly to age, geographic area, family size, and tobacco use where permitted. For larger groups, self-funded plans, and stop-loss coverage, claims experience and risk projections can play a much larger role.
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