Comparing Insurance Policies with AI

An insurance policy is a contract built from several parts: the declarations page lists the insured, limits, deductibles and premium; the insuring agreement states what the insurer promises to pay; Definitions control what key words mean; Exclusions remove coverage; Conditions set duties, such as giving prompt notice of a claim; and Endorsements add, remove or change terms. Many U.S. policies use standard forms from ISO, such as the HO-3 homeowners form or the CG 00 01 commercial general liability form. Carriers change these forms with their own endorsements, and some write entirely proprietary forms.

AI helps by reading all of it at once. A well-built tool pulls limits and deductibles from the declarations page, identifies which endorsements are attached, and compares each section against another policy or a baseline form. It can explain differences such as named perils versus open perils coverage, and replacement cost versus actual cash value, which subtracts depreciation. It can also explain occurrence versus claims-made triggers, or a flat deductible versus a percentage deductible for wind.

The main risk is confident error. A model may miss an endorsement buried at the end of a PDF, describe an exclusion too broadly, or mix language from two documents. It may also describe what a standard form usually says instead of what this policy says. Anti-concurrent causation wording is a typical example of a clause that summaries often oversimplify. It can deny a loss when an excluded cause, such as flood, combines with a covered cause.

A common misconception is that an AI summary can be handed to a client as the answer. The policy wording controls, and how a claim is paid depends on the facts and sometimes on state law. The safe pattern is to use AI for the first pass, with citations. A licensed professional then checks the source language before advising the client.