Why AI governance guidelines are important and how it applies to today’s insurance regulations

Artificial intelligence has moved faster into insurance operations than almost any regulatory framework was built to handle. Underwriting models, claims-triage systems, and fraud-detection tools are no longer experimental add-ons. Instead, they’re embedded in day-to-day decisions that determine who gets coverage, at what price, and whether a claim gets paid. The real question facing carriers today isn’t whether to use AI. It’s whether they can prove, to a regulator’s satisfaction, that it’s being used responsibly.

That’s what AI governance is for. And in an industry built entirely on the promise of paying claims fairly, it’s foundational infrastructure. Not an option.

What “AI Governance” Actually Means

AI governance is often mistaken for a single policy document or a compliance checklist. It’s neither. Governance is the ongoing system of oversight, controls, and accountability that ensures an AI system consistently does what it’s supposed to do, fairly, and in a way the organization can explain after the fact.

That distinction matters: governance is proactive risk management, while compliance is simply the paperwork trail it leaves behind. A company can technically check every compliance box and still have weak governance if no one is actually testing the model for bias, monitoring its outputs over time, or empowering someone to intervene when it behaves unexpectedly.

Most governance frameworks, regardless of industry, rest on a handful of core pillars:

• Transparency: can the organization explain how and why the AI reached a given decision?
• Accountability: is there a specific person or team responsible for the system’s behavior?
• Fairness and bias testing: is the model regularly evaluated for disparate impact on protected classes or vulnerable groups?
• Human oversight: can a person intervene, override, or halt the system when something goes wrong?
• Auditability: is there a documented trail that a regulator, auditor, or internal reviewer could reconstruct?

These aren’t abstract ideals. Each one shows up, almost line for line, in how insurance regulators are now writing the rules.

Why It Matters — Beyond Just Avoiding Fines

It’s tempting to treat AI governance as a defensive measure, something built purely to survive a regulatory exam. That’s part of it, but far from the whole picture.

Consumer trust is the real currency at stake. Insurance is a promise-based product. Customers pay premiums for years, sometimes decades, on the expectation that a claim will be handled fairly when they need it most. An opaque algorithmic denial, one nobody at the company can fully explain, does lasting damage to that trust regardless of whether the decision was technically correct.

Bias doesn’t stay small. A human underwriter with a blind spot might make a handful of unfair decisions before it’s noticed. An AI model trained on historical data can encode that same blind spot and apply it identically, at scale, to every applicant who fits the pattern. It’s likely that even those tasked with monitoring the system won’t realize what’s happening with time enough to fix it before it’s surfaced.

Errors compound at scale, too. This is the flip side of AI’s greatest advantage. A flawed underwriting model doesn’t make one bad call; it can make thousands before anyone catches it, because the same logic gets applied uniformly across an entire book of business.

The legal exposure was never actually new. Existing unfair trade practice statutes, unfair claims-settlement laws, and consumer protection rules already apply to decisions made by AI exactly as they apply to decisions made by a person. Governance is simply the evidence trail that demonstrates a company followed those laws. Without it, a carrier has no way to show a regulator (or a court) that a contested decision was sound.

And reputationally, it’s becoming a differentiator. Carriers that can demonstrate mature, well-documented AI governance are increasingly viewed more favorably by regulators, reinsurers, and rating agencies — not just customers. In a crowded market, “we can prove our AI is fair” is turning into a genuine competitive advantage.

Why Insurance Is a Special Case

Every industry deploying AI faces some version of these pressures, but insurance sits at a particularly sharp intersection.

First, insurance is inherently data-driven and decision-heavy. Underwriting, pricing, and claims handling — the three functions most core to the business — are precisely the areas where AI adoption has moved fastest, because they’re exactly the kinds of repetitive, data-intensive decisions AI is good at automating.

Second, insurance is one of the most heavily regulated consumer-facing industries in the country, built on a century-old framework designed around two priorities: fairness to policyholders and solvency of the insurer. AI doesn’t get a carve-out from either of those priorities just because a machine made the decision instead of a person.

Third — and increasingly the sharpest edge of the issue — agentic AI is changing what “the decision” even means. A traditional model might score a claim as high-risk and hand it to a human adjuster. An agentic system can go further: requesting documents, adjusting a reserve, or approving or denying a claim outright, with no person in the loop at all. That shift blurs the line between AI as a tool and AI as a decision-maker, and accountability gets blurrier right along with it.

How Governance Principles Translate into Insurance Regulation

The abstract pillars of AI governance aren’t just theory in the insurance world.They have already been written into the regulatory frameworks carriers operate under today.

• Accountability shows up as the requirement, under state adoptions of the NAIC’s Model Bulletin, that insurers maintain a written AI Systems Program with clear senior-management ownership. This suggest that a named accountability structure be in place instead of a diffuse responsibility spread across departments.
• Fairness and bias testing appears as the validation and testing expectations baked into that same bulletin, requiring insurers to actively check for errors and discriminatory outcomes rather than assuming a model is fair by default.
• Transparency and auditability are the entire premise behind the AI Systems Evaluation Tool regulators are now piloting for use in market conduct exams — a structured way to actually verify governance claims rather than take them on faith.
• Third-party oversight matters enormously here. If the insurer is acquiring the technology from a vendor instead of building in-house or acquiring the technology via M&A, they need to license it. Regulatory expectations now extend to vendor-supplied models and data just as much as internally developed ones, which means a carrier can’t outsource its accountability along with its technology.

It’s also worth noting that this translation isn’t uniform everywhere. Some states have adopted a principles-based approach that gives insurers latitude in how they meet these expectations, while others have moved toward more prescriptive, detailed requirements. That variation adds a layer of complexity, but the underlying governance principles driving all of it are consistent.

What Happens Without Strong Governance

The risks here aren’t hypothetical. Consider a few composite, plausible scenarios that reflect the kinds of failures regulators are actively watching for:

• A claims-triage model that, without anyone noticing, systematically under-pays claims from a particular demographic group because of a pattern buried in its training data.
• An underwriting algorithm that can’t explain its own pricing factors when a regulator asks during a market conduct exam — not because the company is hiding something, but because no one built the system to be explainable in the first place.
• A vendor-supplied fraud-detection model that no one inside the company can actually audit, because the contract never secured the access rights to do so.

Each of these scenarios carries real regulatory consequences: market conduct actions, corrective orders, mandated remediation, and — perhaps most costly of all — the reputational fallout once a failure like this becomes public.

Building a Governance-Ready Foundation

None of this requires reinventing how a company operates. It does require treating AI governance as infrastructure rather than an afterthought:

• Maintain a full, current inventory of every AI tool in use, whether built in-house or otherwise.
• Document bias and error testing on a regular, repeatable cadence, not just at launch.
• Build clear human-in-the-loop checkpoints, especially for any system approaching autonomous, agentic decision-making.
• Negotiate vendor contracts that include real audit rights — access a company can actually use, not just a clause that sounds reassuring.
• Name a specific, accountable owner for AI governance. A committee with diffuse responsibility is, in practice, no one’s responsibility.

The Bottom Line

AI governance isn’t a hurdle standing between insurers and innovation. It’s what makes responsible innovation possible in an industry where trust is the entire product. The carriers that treat governance as a core operating discipline, rather than a reactive compliance exercise, will be the ones best positioned as regulatory scrutiny of AI — and especially agentic AI — continues to intensify. The time to build that foundation is now, before an exam, a complaint, or a headline forces the issue.

Further Reading

• NAIC, Insurance Topics: Artificial Intelligence — content.naic.org
• NAIC, Model Bulletin on the Use of Artificial Intelligence Systems by Insurers (adopted December 2023) — content.naic.org
• Crowell & Moring, “NAIC Intensifies AI Regulatory Focus: What Health Insurance Payors Need to Know” (March 2026)
• Fenwick, “Tracking the Evolution of AI Insurance Regulation”
• Plante Moran, “How the NAIC AI Model Bulletin Is Evolving and Why Insurers Should Prepare Now” (March 2026)
• WaterStreet Company, “What the NAIC Model Bulletin Means for Insurance AI” (April 2026)
• Kennedys Law, “Understanding the NAIC Model AI Bulletin: What It Means for Insurers”
• actuary.info, “AI Regulation in Insurance 2026: The NAIC Model Bulletin, State Adoption, and the Federal Preemption Battle” (March 2026)
• Compass MSP, “The NAIC Just Added AI Governance to Your Insurance Cybersecurity Obligations”

This article is intended as general industry commentary and does not constitute legal or compliance advice. Insurers should consult qualified counsel regarding their specific AI governance obligations.