Explainable AI in Insurance: Why Agents Need the Why

A client with a well-controlled condition gets a worse offer than you expected. The carrier’s AI-assisted underwriting program returned the decision in minutes, and nobody on your side can say what drove it. You cannot explain the outcome to the client, you cannot tell whether a different carrier would see the file differently, and you cannot judge whether an appeal is worth the effort.

That gap has a name. Regulators and standards bodies call the fix explainable AI, and it is quietly becoming the thing that separates AI tools a licensed agent can actually use from AI tools that just hand down verdicts. Here is what explainable AI means in insurance, why the people who oversee the industry care about it, and what to ask of any AI tool that touches your cases.

What Explainable AI Means

The clearest definitions come from the National Institute of Standards and Technology. Its AI Risk Management Framework (NIST AI 100-1) lists “explainable and interpretable” as one of the characteristics of trustworthy AI, and it separates three ideas that get blended together in sales conversations:

• Transparency answers “what happened” in the system. Which data went in, which model ran, who decided to deploy it.

• Explainability answers “how” a decision was made. NIST defines it as a representation of the mechanisms underlying the system’s operation.

• Interpretability answers “why” a decision was made and what it means to the person reading it.

For an agent, interpretability is the one that matters most on a live case. NIST says risks to interpretability “often can be addressed by communicating a description of why an AI system made a particular prediction or recommendation.” In plain terms, a usable AI tool tells you the reason, in language fit for your role, not just the answer.

NIST adds a point that should resonate with anyone who has argued a case with an underwriter. Explainable systems, it notes, “can be debugged and monitored more easily, and they lend themselves to more thorough documentation, audit, and governance.” A reason you can see is a reason you can check, document, and challenge.

Why Regulators Are Asking for the Why

Insurance is a regulated business, and the regulators have been explicit that AI does not change who is accountable for a decision.

The National Association of Insurance Commissioners (NAIC) adopted regulatory principles on artificial intelligence at its 2020 Summer National Meeting, then followed in December 2023 with a model bulletin that, in the NAIC’s words, “sets forth expectations as to how insurers will govern the use of AI.” The same NAIC overview states that decisions or actions “made or supported by AI must comply with all applicable insurance laws and regulations.”

The operative sentence for explainability is this one: state insurance regulators “may require companies to explain how these tools are used in underwriting, pricing, marketing, or claims decisions.” A carrier that cannot explain its model has a regulatory problem before it has a customer problem. That pressure flows downstream to every vendor whose tool informs an underwriting or marketing decision, and eventually to the agent who relies on it. We walked through the bulletin itself in what the NAIC’s AI rules mean for agents.

How Much AI Is Already in Life Insurance

The NAIC’s own surveys show why this is a present-tense issue rather than a future one. According to the NAIC, of the 161 life companies that responded to its survey, 58% reported they use, plan to use, or plan to explore AI or machine learning models. That is lower than the 88% of 193 responding auto insurers, but it is still a majority of the life market, and the life report dates to December 2023, so the real number has had time to grow.

Put differently, there is a better than even chance that a life application you submit this month is touched by a model somewhere in the carrier’s process. Whether anyone can explain what that model did is the open question.

Where Opaque AI Hurts an Agent

An unexplained AI output creates three specific problems on a case.

You cannot defend the outcome to the client

Clients accept bad news when it comes with a reason. “The carrier rated the build and the A1c” is a conversation. “The system said so” is a lost client, and often a lost referral.

You cannot route the case

Most of a producer’s leverage in the life market comes from knowing which carrier is friendlier to a given profile. If the tool that scored the case will not say which factors drove the score, you have no basis for trying a different carrier, and you are back to guessing. Our guide to life insurance carrier matching covers why the reason behind a match is worth more than the match itself.

You cannot catch a wrong answer

NIST’s framework is blunt about this. A transparent system, it says, “is not necessarily an accurate, privacy-enhanced, secure, or fair system,” but “it is difficult to determine whether an opaque system possesses such characteristics.” Two of the biggest AI risks we have written about, hallucinated answers and biased outcomes, are both far easier to spot when the tool shows its reasoning. A fabricated citation is obvious once you can see it. A proxy for a protected class is obvious once you can see which factor moved the score.

Questions to Ask Any AI Tool Before You Trust It

You do not need to audit a model. You need to ask a few questions the way you would ask them of a new underwriter, and treat a vague answer as the answer.

1. Does it show the factors behind each output? Not a confidence percentage. The actual inputs that moved the result, in order of weight if possible.

2. Is the explanation written for an agent? NIST recommends descriptions “tailored to individual differences such as the user’s role, knowledge, and skill level.” A data scientist’s feature chart is not an explanation for a producer on a call.

3. Can you trace an answer to a source? If a tool says a carrier declines a condition, it should point to the guideline language or the carrier rule it relied on, and that source should be current.

4. Does it tell you when it is unsure? A tool that never says “I do not know” is hiding something, usually from itself.

5. Can you keep the explanation on the file? Explainability that disappears when you close the window does nothing for a compliance review or a client dispute six months later.

6. Who is accountable when it is wrong? NIST ties the whole structure together with one line: trustworthy AI “depends upon accountability,” and accountability “presupposes transparency.” If the vendor’s answer is that the model is a black box, the accountability is yours alone.

How We Think About This at Peach Pilot

We built Peach Quote around the idea that an AI recommendation an agent cannot explain is not a recommendation. When Peach Quote suggests a carrier for a client, the design goal is that the agent can see the reason: the health profile factors, the carrier’s published niche, and the producer’s own licensing and appointment situation that make one carrier a better starting point than another. The agent reads the reasoning, decides whether it holds, and owns the submission.

That is the division of labor NIST and the NAIC describe. The tool shows how and why. The licensed professional judges whether it is right. The carrier makes the call on the application. None of that works if the first step is a verdict without a reason.

Frequently Asked Questions

Is explainable AI required by law in insurance?

The NAIC’s model bulletin is guidance that states adopt and enforce through their existing insurance laws, and the NAIC notes regulators may require companies to explain how AI tools are used in underwriting, pricing, marketing, or claims decisions. Requirements vary by state, so check your own department of insurance for what applies to carriers and producers where you are licensed.

Does explainable AI mean I get to see the carrier’s underwriting model?

No. It means the tools you use should be able to tell you why they produced a given output, and carriers should be able to explain their AI-supported decisions to regulators. Carrier models themselves remain proprietary, and carriers make final underwriting and issue decisions.

What is the difference between explainability and transparency?

In NIST’s framing, transparency answers what happened, explainability answers how a decision was made, and interpretability answers why and what it means to the user. The three support each other, and a tool that offers only one of them leaves gaps.

Conclusion

Explainable AI is not a research topic for life insurance agents anymore. A majority of surveyed life carriers are using or exploring AI models, regulators have said they may demand explanations for how those models shape underwriting and claims decisions, and the standards body that defines trustworthy AI puts explainability near the center of it. For a producer, the practical version is simple: prefer tools that show the why, keep the why on the file, and treat any AI output without a reason as a lead to check rather than a decision to act on.

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