Yes, insurers use AI and other automated tools in claims operations, and reporting has described systems involved in adverse health-claim decisions. But the evidence does not show that insurers generally use AI to deny claims, or that denials are happening “faster than ever.” Adoption surveys include many functions besides denial, while two widely discussed health-insurance examples involve reported conduct and disputed allegations—not proof of an industry-wide practice. No comparative data in the cited sources establishes a historical record for denial speed.
What do the adoption numbers actually measure?
The National Association of Insurance Commissioners (NAIC) surveyed insurers across several lines of business. Its figures combine respondents who said they currently use, plan to use, or plan to explore AI or machine learning. They are not percentages of claims decided by AI, and they do not measure how many claims were denied.
| Insurance line | Respondents in the combined use, plan-to-use, or plan-to-explore group | NAIC report date |
|---|---|---|
| Auto | 88% of 193 responding insurers | December 2022 |
| Homeowners | 70% of 194 responding insurers | August 2023 |
| Life | 58% of 161 responding insurers | December 2023 |
| Health | 92% of 93 responding insurers | May 2025 |
These are survey results, not a census of every insurer or a count of systems actively making claim decisions. The NAIC describes uses including accident-image analysis, estimating settlement values, fraud detection, health-claim adjudication, and prior authorization. A model that helps estimate damage or flags a case for investigation is doing a different job from a system that makes or effectively controls a final coverage decision. The NAIC’s survey context and use descriptions are on its Artificial Intelligence topic page.
What are the clearest reported examples of AI-linked adverse decisions?
Cigna’s PxDx system
A 2024 Health Affairs analysis summarized ProPublica and Capital Forum reporting that Cigna used its PxDx system to deny or reject more than 300,000 claims in 2022. The reporting said medical directors were supposed to review cases individually but instead approved decisions in batches, at about 1.2 seconds per case. Those figures are reported claims summarized in a policy analysis; they are not an adjudicated regulator finding, and they do not establish that every affected claim was wrongly denied. The account also does not prove that the same process is used across the insurance industry. Read the Health Affairs analysis.
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UnitedHealth’s nH Predict and Medicare Advantage coverage
Reporting and litigation have raised allegations that UnitedHealth’s nH Predict tool displaced individualized clinical judgment in decisions about post-acute care coverage for Medicare Advantage members. A 2026 legal alert says a federal court allowed breach-of-contract and implied-covenant claims to proceed and ordered broad discovery. That is a procedural development, not a final finding that the allegations are true.
Optum disputes the characterization of nH Predict as a claims-adjudication tool, describing it as care support and saying qualified physicians make medical-necessity determinations. The allegations, company response, and court order are summarized in this 2026 legal alert; background on the dispute also appears in the Health Affairs analysis.
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Does this mean claims are being denied faster than ever?
No such industry-wide or historical conclusion is established by these sources. The Cigna reporting describes very rapid batch approvals in one case, but it does not compare denial times across decades, insurers, or types of claims. The NAIC adoption surveys measure reported use or possible future use, not processing speed. A claim that denials are occurring “faster than ever” would require comparable timing data over time, which the cited material does not provide.
Speed is also not the same as accuracy or fairness. To assess an automated claims process, it matters whether a tool only flags or prioritizes a case, whether a qualified person reviews the relevant records, what explanation the consumer receives, and whether an appeal can result in an individualized review.
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What rules and oversight apply?
Using AI does not exempt an insurer from insurance law, policy terms, or consumer-protection requirements. The NAIC says state regulators oversee insurers’ AI use and may ask how a tool supports claims decisions. Its model bulletin, adopted in December 2023, sets governance expectations and identifies information regulators may request during examinations. The NAIC states that insurers remain responsible for complying with applicable insurance laws and consumer-protection rules when they use AI; see its overview of AI oversight and governance.
State examples do not establish a universal rule
In 2022, the California Department of Insurance described allegations that insurers flagged claims from certain ZIP codes for special investigation and used biometric data in decisions about payment or denial. The department said it could audit claims criteria, algorithms, and models. The release documents concerns and investigative authority, not proof that every allegation was substantiated. California Department of Insurance release.
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Florida’s 2026 HB 527 proposed allowing AI and machine learning to assist processing while prohibiting them from being the sole basis to reduce or deny a claim, with qualified humans making those decisions. The Senate record says the bill died in Rules on March 13, 2026, so this proposal did not become a statewide requirement through that bill. Florida Senate bill record.
When is automation a flag rather than a denial?
Not every automated insurance action decides whether a person’s coverage should be paid. A 2026 Government Accountability Office review says selected private payers and vendors described using analytics to identify unusual provider billing patterns and generate leads for investigation, rather than automatically denying claims. It also says neither Medicare nor the selected private payers were using generative AI to identify fraud. The report discusses automated prepayment denials in certain CMS circumstances—for example, when a provider fails to submit requested records on time—but those are provider-payment and fraud-control examples, not necessarily decisions about a patient’s coverage. These distinctions matter when describing what a system does. GAO-26-107799.
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A denial notice is the starting point for checking the reason and the review route—not proof by itself that a computer made the decision. The process and deadlines depend on the policy, plan, program, and jurisdiction, so use the instructions that apply to your coverage rather than assuming one universal appeal procedure.
Quick Recap
- Read the denial explanation and policy documents. Identify the stated reason, the policy or plan provision cited, and any missing information or records the insurer says it needs.
- Ask who or what made the decision. Request an explanation of the role of any automated tool and whether a qualified person can review the case and the records specific to you.
- Keep a complete record. Save the denial notice, policy documents, bills, medical or repair records, correspondence, and notes of calls. Record the date you received the notice.
- Use the applicable appeal or complaint route. Follow the instructions and deadline stated for your plan or coverage. If the explanation is unclear, ask the insurer or plan administrator how to submit an appeal and what documents to include; a state insurance department or the relevant public program may have a complaint or review process.
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