AI Hallucinations in Insurance: What Agents Must Check

You ask an AI assistant whether a carrier will take a client on a specific heart medication. The answer comes back in two tidy paragraphs, names the rate class you can expect, and even cites a section of the underwriting guide. It reads like it came from someone who knows. The problem is that none of it may be true, and nothing in the tone of the answer tells you which parts are.

That is an AI hallucination, and it is the single most practical AI risk a life insurance agent faces today. Here is what it is, why it happens, where it shows up in a producer’s day, and a simple routine for checking AI output before it reaches a client or an application.

What an AI Hallucination Actually Is

The National Institute of Standards and Technology uses the term “confabulation.” In its Generative AI Profile (NIST AI 600-1, July 2024), NIST defines it as the production of confidently stated but erroneous or false content, which it notes is known colloquially as “hallucinations” or “fabrications.”

NIST lists confabulation as one of 12 risks it considers unique to or made worse by generative AI. The NAIC puts the same idea in plainer language on its artificial intelligence research page: tools like ChatGPT, Claude, and Gemini “may generate information that sounds accurate but is incorrect,” so AI-generated information should be reviewed carefully, especially when used for important decisions.

The key word in both is confident. A hallucination does not look like an error. It looks like an answer.

Why AI Tools Make Things Up

Large language models do not look facts up the way a search engine or a carrier portal does. NIST explains that these models generate outputs that approximate the statistical distribution of their training data, predicting the next word in a sentence. That process often produces accurate text. It can also produce text that is fluent, specific, and wrong.

NIST flags two conditions where this is especially likely, and both describe insurance work:

• Long, open-ended answers. The more the model writes, the more room it has to fill gaps with plausible filler.

• Specialized domains. NIST calls out domains that require highly contextual or domain expertise. Carrier-specific underwriting rules, state replacement requirements, and product riders are exactly that kind of knowledge.

There is a third trap. NIST notes that AI output can include confabulated logic or citations that appear to justify the answer, which can lead people to trust it more than they should. A fake page reference is more dangerous than no reference at all, because it looks like proof.

Where Hallucinations Show Up in an Agent’s Day

Most agents are not asking AI to underwrite a case. They are using it for the small jobs around the case, and that is where wrong details slip in unnoticed.

Underwriting and product questions

Asking a general chatbot how a carrier treats a condition, a medication, or a build is the highest-risk use. The model has no access to the current guide, may blend several carriers together, and will rarely say it does not know. Carrier guidelines also change, so even an answer that was right once can be stale. Our guide to finding carrier underwriting guidelines faster covers where the real answers live.

Client-facing explanations

AI is good at turning dense policy language into plain English. It is also capable of adding a benefit the policy does not have, or softening an exclusion that matters. If a summary goes to a client, you own every sentence in it.

Call notes and case summaries

Summaries of client conversations can quietly drop a disclosed condition or invent a detail that was never said. If those notes feed an application, a small error can become a misstatement on the file.

Compliance and regulatory questions

Rules vary by state and change over time. A confident answer about a replacement form, a free look window, or a licensing requirement should be treated as a lead to check, not as the rule.

A Simple Verification Routine

You do not need to stop using AI. You need a habit that catches the wrong answer before it costs you. This routine takes a few minutes and fits most cases.

1. Separate drafting from deciding. Use AI to draft, summarize, and organize. Do not use it as the final word on anything that affects eligibility, pricing, or a client’s decision.

2. Trace every fact to a primary source. Carrier guides, the carrier portal, the policy contract, or your state insurance department. If the AI cites something, open it and confirm the citation says what the AI claims. NIST’s own guidance for organizations includes reviewing and verifying sources and citations in AI output.

3. Be most skeptical of specifics. Numbers, dates, rate classes, form names, and quoted rules are where hallucinations do the most damage and hide the best.

4. Ask the same question another way. If the answer shifts when you rephrase, that is a signal the model is guessing.

5. Read the summary against the source. Before a call summary goes into a file, skim it against your own notes or the recording. Look for anything added, and anything missing.

6. Keep a human sign-off. The agent, not the tool, confirms what goes on an application or in front of a client.

This is the same principle behind keeping a human in the loop rather than handing the decision to automation.

Why This Matters More in Life Insurance

Life insurance has been cautious with AI compared with other lines. According to the NAIC’s research page, 58 percent of the 161 life companies that responded to its surveys said they use, plan to use, or plan to explore AI or machine learning models, compared with 88 percent of the 193 auto insurers that responded.

That caution is reasonable. A wrong answer in this line can mean a declined application, a rated offer the client did not expect, a misstatement discovered during the contestability period, or a chargeback. The cost of a confident mistake lands on the client and the producer, not on the tool.

NIST also describes automation bias, the tendency to defer too much to automated systems, and notes that it can make confabulation risks worse. The faster and more polished the tool, the easier it is to stop checking.

What to Look for in AI Tools Built for Agents

General chatbots are not the only option, and not all AI tools carry the same risk. When you evaluate one, ask:

• Where do the answers come from? A tool that draws on carrier-specific source material is different from one answering from general training data.

• Can you see the source? Answers should point to the guide, rule, or document behind them so you can check them.

• Does it say when it does not know? A tool that admits uncertainty is safer than one that always has an answer.

• Who makes the final call? Good tools support the licensed agent’s judgment. They do not replace it.

For a broader look at the landscape, see our honest list of AI tools for life insurance agents.

Where Peach Pilot Fits

Peach Pilot is built on the view that AI should help agents move faster through the work while the agent stays in charge of the decisions. Peach Quote helps agents compare carrier options against a client’s profile, but quoting is not underwriting, and the carrier still reviews the application and makes the call. The same rule applies to any AI in your workflow: verify first, then act.

Frequently Asked Questions

What is an AI hallucination in insurance?

It is when an AI tool produces a confident answer that is false or unsupported, such as an invented underwriting rule, a wrong form name, or a citation that does not say what the tool claims. NIST calls this confabulation.

Can AI hallucinations be eliminated?

Not entirely with today’s tools. NIST describes confabulation as a natural result of how generative models are designed. The practical answer is to verify output against primary sources, especially for specific facts.

Is it safe to use ChatGPT for underwriting questions?

Treat it as a starting point at most. General chatbots do not have reliable access to current carrier guidelines and can blend carriers together. Confirm anything that affects eligibility or pricing with the carrier’s own guide or underwriting team.

Who is responsible if AI gives a client wrong information?

The person who passes it on. If an agent shares an AI-drafted explanation or puts AI-generated notes in a file, the agent is accountable for its accuracy.

The Bottom Line

AI can save a producer real time on drafting, summarizing, and organizing. It cannot be trusted to be right just because it sounds right. Treat every specific fact as a claim to check, keep primary sources close, and make sure a licensed human signs off on anything that reaches a client or an application.

Peach Pilot supports licensed agents’ workflow. Carriers make final underwriting and issue decisions.