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Health insurers use artificial intelligence (AI) and machine learning (ML) to check claim information, automate some routine processing, flag unusual or potentially high-risk claims, recommend actions, and route cases to human examiners. These tools do not all work the same way: some support staff decisions, while others automate parts of a workflow. A survey of insurers documents both uses, but it does not establish that every insurer uses AI or that AI alone makes every approval or denial decision.
What AI does in a health insurance claim workflow
For a post-service claim, an insurer generally needs to process information such as eligibility, service and diagnosis codes, billed amounts, contract terms, and claim edits. AI or ML may help organize or analyze that information, compare it with rules or patterns, and determine what should happen next in the insurer’s workflow. The National Association of Insurance Commissioners (NAIC) survey describes insurer-reported applications across these kinds of tasks; it is not a universal map of every company’s systems.
| Task | What the software may do | Possible workflow role |
|---|---|---|
| Information extraction and coding checks | Structure claim information or identify coding issues and claim edits. | Help resolve routine edits or send a claim for further review. |
| Contract and amount checks | Compare claim amounts with contract terms. | Support payment handling or identify a discrepancy. |
| Risk and anomaly flags | Identify duplicate billing, potential fraud, waste or abuse, or higher-risk high-dollar claims. | Prioritize a claim for investigation or examiner attention; a flag is not itself proof of wrongdoing. |
| Recommendations and routing | Recommend an approval-related action or direct a claim to a manual examiner. | Support a staff decision or determine which cases receive human review. |
| Routine processing | Automate some processing steps for claims that meet the insurer’s criteria. | Move a routine case through part of the workflow with less manual handling. |
The table describes reported task categories, not a single standard product or a prescribed sequence. An insurer may use a model to classify or estimate risk, while generative AI may produce text or summarize information. The survey includes deployed uses as well as exploratory examples, pilots, and plans, so a described capability should not automatically be read as a production system.
How widespread is AI use for claims?
The main available insurer-reported evidence is the NAIC’s Health AI/ML Survey Report, published in May 2025. An online survey conducted from November 2024 through January 2025 collected responses from 93 insurance companies in 16 participating states. Respondents met premium-size or market-share criteria; the results describe those surveyed companies, not a census of all U.S. health insurers.
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In the report’s broad operational-area table, 31 companies said claims adjudication AI/ML was already in production. Another 10 indicated implementation within one year, seven within one to three years, and two beyond three years; 43 marked the area not applicable. These are response counts for that table, not percentages of the entire U.S. insurance market. The report uses different question groups and denominators for some market segments and lines of business, so counts from separate questions should not be combined as though they shared one denominator.
Health Affairs’ account of the NAIC survey said 84 percent of the 93 surveyed large health insurers used AI for some operational purpose. That is an overall-use figure, not a claims-only adoption rate. Its summary also described 44 percent as using AI now or within a year for claims adjudication, but the underlying denominator and question framing should be checked against the original report before treating that as a standalone adoption statistic. Neither figure means that a particular consumer’s claim was handled by AI.
Is claim adjudication the same as prior authorization?
No. Claim adjudication generally happens after a service has been delivered: the insurer processes a provider’s claim and determines its payment handling. Prior authorization is a separate, pre-service review of whether planned care requires approval. The NAIC survey treats them as distinct operational categories, and their reported use counts should not be merged.
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Insurers reported using AI/ML in prior-authorization workflows to help determine whether authorization is required, review requests, check document completeness, extract information from medical records, or route a request. These are insurer-reported examples that vary across market segments; they do not establish that every authorization request is evaluated this way.
CMS’s Interoperability and Prior Authorization Final Rule concerns impacted payers’ prior-authorization processes and data exchange. Certain provisions had deadlines of January 1, 2026, while most API requirements are due primarily January 1, 2027. Those dates relate to the rule’s implementation requirements, not an endorsement of an insurer’s AI or a general specification for post-service claim-decision logic.
Can AI deny a health insurance claim?
AI can be part of a workflow that automates processing or supports an approval-related recommendation, but the available evidence does not show that AI independently makes every final approval or denial. An automated step is not, by itself, proof that a model made a medical-necessity determination or issued the final denial. Some systems instead flag cases or route them to an examiner.
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A denial alone does not establish that AI was involved, and an insurer’s general use of AI does not explain the reason for a particular claim outcome. Unless an insurer or another reliable source connects a specific decision to a particular system, it is not possible to infer that connection from the outcome alone.
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Is a human involved in an AI-assisted claim review?
Sometimes. Routing claims to manual examiners is one of the uses respondents described, so AI may help prioritize which cases receive human attention rather than decide every case autonomously. Other reported uses include automation and recommendations. The survey does not establish a universal human-review policy, nor does it show that every recommendation can be overridden in the same way.
When trying to understand a specific decision, focus on the explanation in the insurer’s claim or denial notice and ask the insurer what information and review process applied to that claim. The existence of AI elsewhere in the company’s operations does not answer those case-specific questions.
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What is known—and not known—about consumer outcomes?
AI may help process routine work, surface possible errors or fraud indicators, and direct staff attention. It can also produce opaque classifications or carry forward flawed data; a model or its use may not fit the coverage rules or a person’s circumstances. The existence of a tool does not establish that its results are accurate, fair, or beneficial in a particular case.
Health Affairs notes a key evidence gap: studies have not compared denial or wrongful-denial rates for reviews with and without AI. On the evidence described here, it is not established that AI itself raises or lowers denial rates. Insurer survey reports document uses and plans, not independently measured effects on consumers.
What oversight applies to insurer AI?
The NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023. The NAIC describes the bulletin as guidance and expectations for responsible insurer AI use aligned with its AI Principles, and reports ongoing work involving third-party data and models and an AI Systems Evaluation Tool for regulators.
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In the NAIC’s survey announcement, Commissioner Humphreys said the Working Group had been studying insurers’ AI use across major lines of business and that companies were becoming more aware of applicable state regulations and guidance. The announcement also said nearly 30 states had enacted the model bulletin at that time; that is a dated announcement figure, not a current count of state adoption.
What to do if a claim is denied
AI use is not, by itself, a reason to assume why a claim was denied or how to challenge it. Start with the insurer’s explanation of the decision and follow the appeal or review instructions provided for that claim.
- Identify the stated reason for the denial in the notice.
- Check the notice for the insurer’s appeal or review process and any applicable deadline; use the instructions that apply to your plan and claim.
- Ask the insurer to clarify the decision or the information it considered if the notice does not explain the issue clearly.
- Submit relevant supporting information through the stated process and keep copies of the notice and materials you send.
These steps address a denied claim regardless of whether AI played any role. A denial notice or the insurer’s response—not a general adoption statistic—is the relevant place to look for information about the individual decision.
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