AI is changing parts of insurance claims processing by helping analyze accident images, estimate damage and repair costs, project settlement values, and flag possible fraud. These are distinct decision-support tasks—not evidence that AI uniformly replaces adjusters or decides every claim. Insurers remain responsible for consumer-impacting decisions made or supported by AI.
Where AI fits in a claims process
The National Association of Insurance Commissioners (NAIC) identifies several ways insurers may use AI in claims. Each addresses a different task, so evidence about one use does not establish how well another works.
Damage assessment from images
AI systems can analyze accident or damage photos, sometimes alongside historical data, to help assess damage. This can support claims triage or an adjuster’s review; the cited NAIC materials do not establish a universal accuracy rate or show that every image-based assessment replaces human judgment.
Repair-cost estimation
Image analysis and other claim information may be used to estimate repair costs. An estimate is an input to the claims process, not by itself proof of what an insurer will pay or that a particular estimate is correct.
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Settlement estimation
AI may be used to estimate an ultimate claim settlement value. This is different from identifying visible damage or calculating a repair estimate: settlement can involve a broader assessment of the claim. The NAIC’s list of possible applications does not establish that an AI estimate is binding or that a system makes the final decision.
Fraud detection
AI can also be used to identify claims or patterns that may warrant fraud review. A flag is not proof of fraud. It should be treated as a signal for appropriate investigation, not as a finding about a claimant on its own.
Does AI decide insurance claims?
Not as a general rule established by the available sources. AI may support a decision or automate part of a workflow, but the NAIC’s overview of possible claims applications does not say that insurers universally delegate claim decisions to AI. Whether a specific system makes or influences a particular decision depends on the insurer’s process and the applicable jurisdiction.
The distinction matters to consumers: an automated photo assessment, a repair-cost estimate, a settlement projection, and a fraud alert are not interchangeable. Ask what task the system performed and how the insurer reviewed its output if an AI-supported result affects your claim.
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Insurer obligations and the NAIC model bulletin
On December 4, 2023, the NAIC adopted a model bulletin setting out expectations for insurers’ use of AI systems. It is model guidance, not a single federal rule; its legal effect and implementation depend on the state. Check the insurance regulator and applicable requirements in the state where the policy or claim is handled.
The bulletin states that “decisions or actions impacting consumers that are made or supported by advanced analytical and computational technologies, including Artificial Intelligence (AI) Systems (as defined below), must comply with all applicable insurance laws and regulations.” That includes laws governing unfair trade practices and unfair discrimination. The obligation applies when AI supports a consumer-impacting decision, not only when a system acts without human involvement.
The bulletin calls for an insurer AI systems program, with controls proportionate to the potential harm to consumers. The program is expected to address the system’s lifecycle: design, development, validation, implementation, use, ongoing monitoring, updates, and retirement. The expectations also extend to systems developed by third-party vendors, rather than ending at the insurer’s own software boundary.
Risks insurers should assess
The NAIC identifies risks that call for controls and testing. They are not inevitable defects in every AI system, but they are relevant when a system can affect claimants.
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- Inaccuracy: An incorrect damage assessment, estimate, or flag could affect how a claim is handled. Insurers should validate systems for the specific task and monitor results in use.
- Unfair discrimination: AI-supported decisions must comply with applicable insurance laws, including rules against unfair discrimination. Evaluation should consider outcomes and the data and methods that may shape them.
- Data vulnerability: Claims systems may process sensitive information. Insurers should assess how data is protected and handled, including by a vendor.
- Limited transparency or explainability: If an insurer cannot document or explain how a system informs a decision, it may be harder to review an outcome, identify a problem, or provide meaningful oversight.
How to evaluate an AI-supported claims system
For insurers, regulators, or anyone assessing a system, the useful questions are specific to the task—not whether a product is simply described as “AI-powered.” The NAIC bulletin supports a lifecycle and governance review; its claims examples supply the task distinctions.
- What claim task does it support? Identify whether it handles image triage, damage assessment, repair-cost estimation, settlement estimation, or fraud detection.
- What evidence validates that task? Ask how the insurer tested the system for its intended use and how it checks whether results remain reliable after deployment. Do not treat a result for one task as proof of performance in another.
- Where does human review or escalation occur? Establish who reviews uncertain, disputed, or consequential outputs, and how a claimant’s information can be reconsidered.
- How are consumer impacts controlled? Look for processes to detect and address inaccurate or unfair outcomes, and to comply with applicable insurance law.
- Can the insurer explain and document its use? Useful records include the model and data used, its intended role, validation, oversight, and monitoring.
- How are data and vendors governed? Review data handling and protections, and whether the insurer can evaluate and oversee a vendor-provided system throughout its use.
- What happens after deployment? Ask how outcomes are monitored, how updates are assessed, and how a system can be changed or retired if it no longer performs acceptably.
What the available evidence does—and does not—show
The NAIC describes possible claims applications; that catalog is not proof that a particular insurer has deployed a system successfully, or that a specific system produces better outcomes. The cited sources do not establish a universal or quantified improvement in claim speed, accuracy, cost, or fraud detection. Claims about those benefits require insurer- or system-specific evaluation evidence.
The NAIC’s AI topic overview summarizes claims applications and regulators’ work. Its model bulletin on insurers’ use of AI systems sets out governance expectations; the adoption announcement describes the bulletin’s adoption on December 4, 2023.
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