Testing AI Agents in Insurance: Claims, Policy, and Payout Workflows

Insurance companies are increasingly using or exploring AI and machine-learning systems that can interact with policyholders and take actions in core claims, policy, and payment systems, making evaluation of both conversation quality and system effects essential. The guidance recommends testing AI agents across first notice of loss intake, claims-status and coverage inquiries, policy servicing, and payouts, verifying that claims are opened on the correct policy, coverage answers are grounded in current policy documents, servicing changes are confirmed and executed only once, and payments remain within authority limits with safeguards against fraud and social engineering. It also emphasizes paired-persona fairness tests to identify unequal outcomes, particularly under U.S. state rules and potentially applicable EU AI Act requirements. Citing NAIC expectations for validation, testing, retesting, and documentation, it advises maintaining release-level evidence that records agent configurations, workflow results, unverifiable criteria, fairness comparisons, and changes from prior versions. TestMu’s products are presented as tools for assessing spoken or written interactions, observing tool calls and system changes, and reporting pass, fail, or unable-to-verify outcomes, while the material notes that regulatory references are intended for test design rather than legal advice.

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