What Market Conduct Examiners Are Starting to Ask About Claims and Underwriting AI Agents

Agentic AI for life insurance has moved out of the innovation lab and into the decisions regulators already watch most closely. Life insurers are putting AI agents directly into underwriting, claims, and group benefits administration. The efficiency gains are real, and so is the shift in what an examiner expects an insurer to show.

The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, adopted in December 2023, is now in force or substantially adopted in 25 or more states. It requires a written, board-accountable AI Systems Program that covers a full AI tool inventory, bias and fairness testing, and third-party vendor oversight. A multistate pilot of the National Association of Insurance Commissioners (NAIC) AI Systems Evaluation Tool is examining insurer AI governance through 2026.

For a life insurer, the consequence is practical. A carrier that can't produce its inventory and audit trail on demand doesn't fail a technology review, it fails a market conduct exam. The questions below follow from the Model Bulletin's requirements and the evaluation tool's focus, not from a script any regulator has published, and each comes with what a defensible answer looks like.

Can You Produce a Complete Inventory of AI Agents?

The AI Systems Program starts with an inventory, and for many insurers adopting agentic AI for life insurance, that's the first gap. Claims and underwriting AI agents are often deployed by business and data teams, on composite AI platforms, through vendor partnerships, or on homegrown stacks, with little visibility into what data each one touches or which decisions it's actually authorized to make. An inventory kept in a spreadsheet goes stale the week a team ships something new.

A defensible answer is a continuous inventory of every AI agent touching underwriting, claims, or policyholder data, including claims, underwriting, and policy administration AI agents and the Shadow AI a team launched without telling security. Mapped to the AI Systems Program's inventory requirement, it lets you hand over what the examiner asks for before they ask. The NAIC's AI resources are a good place to track how the bulletin is being applied.

Which Decisions Can Each AI Agent Make on Its Own?

An inventory says what exists. An examiner will also want to know what each AI agent is allowed to decide.

Can a claims-triage or disability and life-claims AI agent approve, deny, or escalate a claim outside its authorized policy logic?

Can an underwriting-support AI agent move a case forward on its own?

Written policy isn't a control. The control is a deterministic allow, modify, or block decision on each claims and underwriting action, aligned to the policy logic your AI Systems Program actually authorizes. A triage AI agent can still work at machine speed. If it tries to deny a claim outside its authority, the boundary blocks the action or modifies it into an escalation to a human reviewer, regardless of what the model concluded.

Why Did the AI Agent Make That Call?

Sooner or later, a decision gets challenged: in an appeal, an exam, or a lawsuit. The question is rarely about the model in general. It's about one claim or one underwriting file and why the AI agent handled it the way it did.

A model card describes how a model behaves on average. It can't tell an examiner why this decision came out this way. Answering requires a full execution trace: the sequence of data accessed, tools invoked, and actions taken, and the decision chain connecting them. Teams that can reconstruct that chain in minutes handle an appeal very differently from teams that have to reconstruct it from scattered logs over weeks.

What Data Is Your Underwriting AI Agent Weighing?

Bias and fairness testing is a core part of the AI Systems Program, and it depends on knowing what an underwriting-support AI agent actually accesses and which risk factors it actually weighs. Without that visibility, a fairness review is an exercise in guesswork, and unfair-discrimination exposure stays open.

Runtime visibility into data access, paired with boundaries on what the AI agent may read, gives compliance and legal a real answer when a decision is challenged. It also supports documentation for state-specific requirements, including Colorado's AI Act and New York's DFS Part 500, alongside the NAIC bulletin.

Whose AI Is It When a Vendor Built It?

The Model Bulletin holds the insurer responsible for vendor-supplied AI, even when a partner provides the model. A contract that promises responsible AI isn't evidence of what a vendor-embedded claims or underwriting AI agent does inside your environment. Examiners are likely to treat it as exactly that: your AI, your accountability.

The answer is runtime visibility into what the vendor's AI agent actually does, held to the same standard as one you built. Whether an AI agent runs on a composite AI platform, a vendor partnership, or a homegrown stack, one taxonomy and one set of runtime boundaries should apply, instead of a new review process for each platform.

Where Does the Processing Happen?

Data residency is part of the same conversation as agentic AI for life insurance. Some insurers need the processing layer to stay inside their own environment or region, or to run air-gapped. Regulators expect sensitive policyholder data to stay where the insurer can account for it, which makes the architecture of the security layer itself a question worth answering in advance.

What About AI for Life Insurance Agents in Distribution?

The examiner's question doesn't stop at claims and underwriting. Marketing decisions influenced by AI are also in scope, which means AI for life insurance agents, such as tools that help producers recommend products or draft outreach, deserves the same inventory, boundaries, and decision records. The principle holds across the policy lifecycle: if AI influenced the decision, the insurer needs to be able to explain it.

Turn Exam Readiness Into a Byproduct

As agentic AI for life insurance scales, insurers shouldn't have to choose between scaling AI and passing an exam. Continuous AI agent inventory and decision-chain audit trails map directly onto the AI Systems Program's documentation, testing, and vendor-oversight requirements, so compliance becomes something the program produces as it runs. That's what lets chief data and AI officers and claims and underwriting transformation leaders keep a multi-year rollout moving without stopping for a compliance review each time. It also gives chief information security, risk, and compliance officers the same thing: the ability to prove, on demand, that claims and underwriting AI agents are inventoried, governed, and defensible under a NAIC-aligned exam, not just performing well.

Explore how Zenity secures AI agents and enterprise copilots for life insurance.