Did AI Help Deny Your Life Insurance Claim?
Insurers now use artificial intelligence in claims handling and fraud detection, and insurance regulators have begun requiring them to document how those systems work. That documentation requirement matters to anyone whose claim has been denied, because records an insurance department may demand in an examination are records a claimant can argue for in a claim dispute. A denial letter that recites a conclusion is no longer the only account of how the decision was reached.
A beneficiary who receives a denial letter is told a conclusion: the claim is not payable, and here is the reason. What the letter does not say is how the insurer arrived at that conclusion — whether a human examiner reviewed the file from the outset, whether the claim was flagged by an AI fraud-detection system, whether a predictive model scored it for investigation, or whether data drawn from outside the insurer's own records contributed to the decision.
Those questions are no longer speculative. Insurance regulators have confirmed that insurers use these systems, have published the standards insurers must meet in governing them, and have told insurance departments exactly which records to demand when examining them.
What regulators say insurers are actually doing
The National Association of Insurance Commissioners, the body through which state insurance regulators coordinate, maintains a public account of how insurers are using artificial intelligence.
On the underwriting side, the NAIC reports that "[i]n underwriting, AI was used for renewal evaluations and inspections to verify policy characteristics." On the claims side, its account is more specific and more relevant to a denied claim: "in claims, AI is used for accident image analysis and to estimate ultimate claim settlement values, along with fraud detection." It further notes that "AI is used in claims processing, where it can help estimate repair costs or assess damage using photos and historical data."
Two things in that account warrant attention. The first is that fraud detection is expressly identified as a claims-side application. The second is that AI is described as being used "to estimate ultimate claim settlement values" — that is, in the valuation of claims, not merely in their administrative processing.
What "AI" means in an insurance claim
The term invites a misconception worth clearing up at the outset. The systems at issue are not conversational chatbots. The NAIC's model bulletin addresses "AI Systems" broadly, and what that covers in a claims context is more mundane and more consequential: predictive models that score a claim for the likelihood of fraud, rules engines that route a file to investigation rather than payment, automated matching of an application against medical and prescription databases, and the use of data purchased from outside vendors to inform a decision.
None of that looks like artificial intelligence to a beneficiary reading a denial letter. All of it falls within what regulators are now examining.
Where this touches a life insurance claim in particular
The claims most exposed to AI review are precisely those in which life insurers already conduct the most investigation.
A death occurring within the policy's contestability period triggers a review of the application, and that review increasingly involves automated comparison of the application against medical, pharmacy, and prescription-history databases rather than a manual reading of records. A claim flagged by a fraud-detection system is routed to investigation rather than to payment, and the flag itself may rest on a score generated by a model rather than on a particular fact an examiner identified. And where an insurer draws on information from outside its own files — a category regulators call external consumer data and information sources — the material informing the decision may never appear in the claim file as the beneficiary receives it.
Colorado's definition of that category is instructive as to its breadth. "External consumer data and information source" encompasses "credit scores, social media habits, locations, purchasing habits, home ownership, educational attainment, occupation, licensures, civil judgments, and court records."
The governing rule: AI does not change the legal standard
The NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023, and a number of states have since adopted it. Its central proposition is the one that matters most to a claimant.
Section 3 of the bulletin provides that "[d]ecisions subject to regulatory oversight that are made by Insurers using AI Systems must comply with the legal and regulatory standards that apply to those decisions, including unfair trade practice laws." The obligation attaches "regardless of the tools and methods Insurers use to make such decisions."
In other words, an insurer does not acquire a lower standard of conduct by using AI to make a decision. The unfair claims settlement practices requirements that govern a human adjuster's handling of a claim govern the system that handles it instead. The NAIC's own guidance adds that "[h]uman oversight remains an important part of insurance decision-making."
That principle is the foundation of everything that follows. If an AI system produced a denial that a human examiner could not lawfully have produced — a denial without reasonable investigation, or one resting on a factor the law does not permit — the fact that software produced it is not a defense.
What documentation now exists, and why that matters
The practical significance of the model bulletin for a denied claim lies in Section 4, which identifies what an insurance department may request when investigating or examining an insurer's use of these systems. The categories it names include:
written documentation of the insurer's AI systems program and evidence of its adoption; governance framework materials and governance accountability structures; data governance controls and bias analysis procedures; inventories, descriptions and development documentation for predictive models; validation, testing and audit documentation; information on evaluation of model drift; assessments of data lineage, quality and integrity; third-party due diligence records; contracts with third-party vendors; and audit reports confirming third-party compliance.
That list was written for regulators. It is also, read from the other direction, an inventory of what an insurer that uses these systems is expected to possess. A beneficiary challenging a denial is not speculating when asking whether a model inventory, a validation report, or a vendor contract exists; the regulatory framework presumes that it does.
Third-party vendors
Insurers frequently license these systems rather than build them, and the resulting instinct is to treat the vendor's methodology as proprietary and beyond the insurer's knowledge. The model bulletin does not accommodate that position.
Section 4.0 addresses the insurer's process for acquiring or relying on third-party AI systems, and requires "due diligence and the methods employed by the Insurer to assess the third party...to ensure that decisions...will meet the legal standards imposed on the Insurer itself." The insurer remains legally responsible for the decision notwithstanding that it outsourced the development of the tool that produced it.
Colorado expressly reaches claims management
Colorado has gone furthest among the states, and its statute is notable for a reason directly relevant to a denied claim rather than a declined application.
SB 21-169 was enacted to "[p]rotect Colorado consumers from insurance practices that result in unfair discrimination on the basis of race, color, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression." It requires carriers to "test whether any ECDIS, and/or algorithms and predictive models utilizing ECDIS result in unfairly discriminatory outcomes," and where such outcomes are found, insurers "must remediate any unfairly discriminatory ECDIS, algorithms, or predictive models and conduct additional testing to demonstrate the effectiveness of the remediation." Insurers "are required to report the results of the testing beginning in 2024 and annually thereafter."
The statute also imposes governance obligations: a "multi-disciplinary, cross-functional team" with "clearly defined roles and responsibilities," and "comprehensive and detailed documentation of policies and procedures as well as thorough record keeping."
Critically for present purposes, the Division of Insurance's own account of the statute's scope identifies the regulated practices as "Marketing, Underwriting, Pricing, Utilization Management, Reimbursement Methodologies, and Claims Management," and identifies life insurance among the covered lines. The statute does not stop at the point of sale.
Employer-provided coverage: the administrative record
Where the coverage was provided through employment, ERISA supplies an independent basis for inquiry, and in some respects a stronger one.
A claimant denied benefits under an ERISA-governed plan is entitled to a full and fair review of the denial, and to the documents, records and other information the plan relied upon in making its determination. Where the determination was informed by an AI-generated flag or score, or by data drawn from an external source, the natural question is whether that material formed part of what the plan relied upon — and if it did, whether it was produced.
The point has particular force in ERISA litigation because of the record-based nature of the review. Where a court will examine only what was before the administrator, a claimant has every reason to establish during the administrative appeal what was in fact before it. See ERISA claims and appeals.
What is coming
Regulatory attention to these systems is increasing rather than stabilizing.
The NAIC has developed an AI Systems Evaluation Tool, described as "a guide for regulators in a market conduct, financial analysis or financial exam context to gather information about the extent and use of AI by an insurance company in their operations." As of March 2026, the NAIC reports, "this Tool is being piloted by 12 participating states," and "it is anticipated the Tool will be adopted at the 2026 Fall National Meeting."
The practical consequence is that insurers will be answering structured regulatory questions about these systems on an ongoing basis. Material generated for that purpose is material that exists.
What a beneficiary should ask for
A claimant who suspects that AI contributed to a denial is not without recourse, but the requests have to be specific. In addition to the complete claim file and the policy or plan documents, consider requesting, in writing:
the identity of each individual who reviewed the claim and the date of each review; whether the claim was referred to a special investigations unit or equivalent function, and what prompted the referral; whether any AI system, predictive model or scoring tool was applied to the claim at any stage; the identity of any external consumer data or information source consulted and the data obtained from it; any vendor or third party that supplied a system or score applied to the claim; and the insurer's documentation of its governance, testing and validation of any such system as it bears on the decision in question.
An insurer may resist some of those requests, and the scope of what is ultimately obtainable depends on the governing law, the forum and the procedural posture. But the requests are not fishing: they are addressed to categories of records that the applicable regulatory framework contemplates the insurer maintaining.
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***This page is for general informational purposes only and does not constitute legal advice. Regulatory materials are cited as of the date of writing and are subject to amendment; the NAIC model bulletin takes effect in a given jurisdiction only as adopted there, and adoption and implementation vary by state. Prior results do not guarantee a similar outcome.
