A $1,000 Error: Why Insurers Must Train Staff to Audit AI Drafts

An AI-drafted email told a client the deductible was $1,000. The policy said $5,000. Dr. Gleb Tsipursky, CEO of Disaster Avoidance Experts, uses exactly this gap in a training exercise published by Academy Journal, and the lesson is blunt: fluency is not accuracy.

The scenario is simple. A customer asks whether storm damage to a retail store is covered. An employee feeds the question to an AI tool and gets a polished reply. It promises approval, a $1,000 deductible, an $18,000 payment and a payment date next week. None of it is true. No adjuster has made a coverage determination. The settlement amount and the date were invented.

Why regulators flag this risk

Two US bodies drive the concern. The National Institute of Standards and Technology's generative AI guidance identifies confidently stated false information as a characteristic risk of these systems. The National Association of Insurance Commissioners' overview of AI in insurance emphasizes reviewing AI-generated material and keeping human judgment in consumer-facing work.

The NAIC also defines a claim as a request for payment under an insurance contract, rather than a guarantee of payment. In Tsipursky's exercise, the AI draft collapses that distinction completely.

How the 20-minute exercise works

Managers hand participants three fictional source cards and one flawed AI draft. The case stays fictional deliberately, so no real customer policy or claim record enters an unapproved tool. The cards contain:

• A policy summary showing a $5,000 windstorm deductible, a flood exclusion, and a note that the issued policy controls any analysis.
• Claim notes: winds damaged a roof, rain entered the store, a contractor photographed missing materials, and an adjuster is assigned. Cause and scope remain under investigation. No payment or approval is recorded.
• The customer's question: "Can I tell my landlord that the roof and interior repairs are covered? When should I expect the check?"

Participants must find at least four errors in the AI draft: premature coverage approval, the wrong deductible, an unsupported payment amount and date, and permission to schedule repairs as if the claim were settled.

The drill runs on a strict clock. Minutes 0-2 define the goal: a client-ready explanation, not a coverage decision. Minutes 2-6 audit the draft: underline unsupported promises, circle verified facts, name who owns each missing answer. Minutes 6-11 rewrite the email in under 120 words. Minutes 11-16 rehearse the hard conversation, where a colleague playing the client asks: "But your email said everything was covered. Are you taking that back?" Minutes 16-20 score and debrief.

One acceptable rewrite states the facts plainly: the report is documented, the adjuster is still reviewing, the $5,000 deductible appears in the summary, and the claims team owns the next step.

What the scoring rubric measures

Tsipursky scores three dimensions, each zero to two points, for a maximum of six. Accuracy means every statement matches a source card and pending issues are unmistakable. Clarity means the client can repeat back what is known, what is undecided and when they will hear more. Appropriate escalation means the email names the claims team, identifies the question and sets a realistic follow-up.

One rule overrides the score. Any unsupported coverage approval, payment amount or timing triggers a rewrite regardless of total. In Tsipursky's words, a fluent error should never earn a passing grade by compensating with good style.

Making the habit stick

The follow-through is repetition: run the drill weekly for a month with new fictional cases — a certificate-of-insurance request, a renewal question, a claim-status update, an exclusion explanation. Keep the rubric stable so managers can track reliability.

For real client work, employees compare drafts against the policy, claim record and approved guidance in authorized systems, and record what was checked and who approved any consequential representation. The NAIC recommends retaining records of insurance communications.

The closing coaching question is the mechanism: "What would I have to verify before I could say that sentence to the customer?" Teams that answer it consistently will be ready when a client asks the harder one: "How do you know?"

Tsipursky, a behavioral scientist and author of eight books including The Psychology of AI Adoption at Work (Georgetown University Press, 2026), consults organizations on turning AI tools into verifiable workflows.