AI Bias in Insurance: What Agents Should Know

A client comes back with a decision that does not match what you expected. Clean history, stable income, and yet a rate class a notch worse than your field underwriting suggested, or an accelerated path that dropped them into full underwriting. More and more, some part of that decision ran through a model. When the client asks why, “the system decided” is not an answer they will accept, and regulators have made clear it is not one carriers can hide behind either.

AI bias in insurance is the risk that a model produces unfairly discriminatory outcomes, usually without anyone intending it. Here is what the rules actually say, how bias gets into a model, and what it changes for the producer working the case.

What Regulators Mean by AI Bias

The NAIC adopted its Principles on Artificial Intelligence on August 14, 2020. They are organized around five themes: fair and ethical, accountable, compliant, transparent, and secure, safe and robust. The document is explicit that the principles are guidance and do not carry the weight of law. They still set the expectations carriers are measured against.

The fairness principle asks AI actors to avoid “proxy discrimination against protected classes.” That phrase is the heart of the issue, and it is worth understanding before you ever have to explain an outcome to a client.

The principles also reach further than carriers. They are addressed to insurers and to other parties that play an active role in an AI system’s life cycle, including third parties such as data providers and advisory organizations.

How Bias Gets Into a Model

Nobody sets out to build a discriminatory underwriting model. Bias tends to arrive through the side door, in four common ways.

Proxy variables

A model does not need to see a protected trait to reproduce its effect. An input that looks neutral on its face can correlate closely with one. The model optimizes for prediction, not fairness, so if a proxy helps it predict, it will lean on the proxy unless someone tests for that and stops it.

Historical data

Models learn from past decisions and past outcomes. If an old pattern was baked into the data, the model learns the pattern along with everything else. This is the same reason AI in insurance fails without clean data: a model can only be as fair as the record it was trained on.

Third-party data and vendor models

Carriers frequently license outside data and buy models they did not build. The NAIC’s model bulletin expects a carrier’s AI program to address how it acquires, uses, or relies on third-party data and AI systems developed by a third party. In that framing, “the vendor built it” does not move the responsibility off the carrier.

Drift

A model that tested fine at launch can behave differently a year later as the population and inputs change. The bulletin lists model drift among the things a carrier should be assessing, which is a quiet admission that fairness is not a one-time check.

What the Model Bulletin Asks of Carriers

On December 4, 2023, the NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers. Its core point is simple: decisions made with AI have to meet the same standards as any other decision, and those standards require that decisions are not “inaccurate, arbitrary, capricious, or unfairly discriminatory.” The tool does not change the rule.

In practice, the bulletin expects a carrier’s written AI program to cover:

• Governance. Clear ownership and oversight of the AI systems a carrier uses in regulated practices.

• Testing for bias. Verification and testing methods to identify errors and bias, and the potential for unfair discrimination in outcomes.

• Third-party standards. Policies for vendor data and vendor-built models.

• Consumer notice. Processes for notifying impacted consumers that AI systems are in use, with access to information appropriate to where the system sits in the insurance life cycle.

Adoption happens state by state. The NAIC’s implementation map, current as of April 1, 2026, lists Alaska as the first adopter on February 1, 2024, with states including Illinois, Pennsylvania, Virginia, and Washington following. California, Colorado, New York, and Texas are listed separately with their own insurance-specific regulation or guidance. If you write in several states, the expectations your carriers operate under are not uniform. For a broader walkthrough, see what the NAIC’s AI rules mean for agents.

Why This Is Early Days for Life Insurance

Life carriers have moved more slowly on AI than other lines. In the NAIC’s line-by-line surveys, 58 percent of the 161 life companies that responded 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, according to the NAIC’s AI research page.

The practical reading is that governance is being built while adoption is still growing. Expect more of the decisions on your cases to be touched by a model over the next few years, not fewer. Programs like accelerated underwriting already lean on external data and carrier models to decide which applicants skip the exam.

What AI Bias Changes for the Agent

Most of the formal obligations sit with carriers. That does not make this someone else’s problem. The producer is the person the client calls.

Your field notes are the counterweight

A model works from the data it receives. An accurate, complete application and clear notes on what the client disclosed give the underwriter something to weigh against an automated flag. When a decision looks wrong, a well-documented file is what makes a reconsideration request credible.

Know how to route a question

The NAIC principles say consumers and regulators should have a way to inquire about, review, and seek recourse for AI-driven insurance decisions, in terms that are easy to understand. In practice that path runs through the carrier’s underwriting team. Processes vary by carrier, so learn each partner’s route for questioning a decision before a client needs it.

Do not promise an outcome

Automated paths are fast when a case fits and fall back to traditional underwriting when it does not. Setting that expectation up front protects the relationship when a promised quick decision turns into an exam and a longer wait.

Hold your own tools to the same standard

Agencies use AI too, for lead scoring, call summaries, and deciding who gets a callback first. If a model in your shop influences which clients get attention or which products get offered, ask the questions the principles ask: what data it uses, what it is for, and who checks the outcomes.

Where Peach Pilot Fits

Peach Pilot is built on the view that AI should help licensed agents do the work, not make the decision for them. Peach Quote helps agents compare carrier options against a client’s profile, but quoting is not underwriting. The carrier still reviews the application and makes the call.

Keeping the agent’s judgment and documentation at the center of the case is what lets a producer explain an outcome, or challenge one, when a client asks. When a decision still surprises you, our guide to why life applications get declined covers the common causes that have nothing to do with a model.

Frequently Asked Questions

Is AI bias in insurance illegal?

Unfair discrimination is prohibited under state insurance law no matter how a decision is made, and the model bulletin says compliance is required regardless of the tools an insurer uses. Whether a particular model outcome crosses that line is a question for regulators and carriers, and it depends on the state.

Does the NAIC model bulletin apply to agents?

The bulletin is addressed to insurers. It shapes how carriers govern their AI systems, including systems and data from third parties. Agents feel it indirectly, through carrier processes, requirements, and how decisions get explained.

Can a client find out whether AI was used on their application?

The bulletin expects a carrier’s AI program to include notice to impacted consumers that AI systems are in use. What that notice looks like depends on the state and the carrier, so the carrier’s underwriting team is the right place to ask.

The Bottom Line

AI bias is not a reason to fear automation in underwriting. It is a reason to keep a human who understands the case in the loop. Carriers carry the governance burden. Agents carry the client relationship, and a well-built file plus a clear explanation is still the strongest tool in it.

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