AI can estimate patterns in claims data, but the available evidence does not show that a general-purpose AI tool can reliably predict whether a specific insurance claim will be approved. Approval depends on the policy and the documented facts of the loss. Treat a chatbot’s answer or an insurer’s model score as an estimate—not a coverage decision.
What AI can—and cannot—do with insurance claims
Insurers may use AI and other predictive methods to process claim information, support triage and claims management, or flag possible fraud. Those are workflow applications; they do not establish that a model independently decides whether a loss is covered. The UK Centre for Data Ethics and Innovation describes possible AI uses in personal insurance, while the Bank of England and Prudential Regulation Authority discuss both potential uses and risks in AI-supported claims management. UK Centre for Data Ethics and Innovation; Bank of England and Prudential Regulation Authority.
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Adoption is not the same as predictive accuracy. In a 2021 European Insurance and Occupational Pensions Authority public-hearing review, 31% of participating companies reported using big-data analytics tools such as AI or machine learning, and another 24% were at proof-of-concept stage. These are historical figures for the participating companies, not a census of insurers or a measure of claim-approval accuracy. EIOPA public-hearing material.
The sources reviewed do not establish a validated accuracy rate for consumer-facing predictions of an individual claim’s approval. An AI tool that sounds confident is not evidence that its forecast is dependable.
Why an AI prediction might be wrong
The policy, not the pattern, determines coverage
A model may find similarities between a new claim and past claims, but that does not show whether the current loss meets the terms of the specific policy. Coverage, conditions, exclusions, limits, and the facts of the event all matter. AI uses discussed for underwriting—deciding whether to issue or price a policy—should not be mistaken for proof that AI can adjudicate a claim under an existing policy. The U.S. Government Accountability Office’s 2019 report describes insurers exploring AI for tasks such as information gathering and risk assessment, while also noting concerns about data accuracy, privacy, ownership, and prohibited factors. GAO report.
Inputs may be missing, inaccurate, or biased
A prediction is limited by the information supplied to the model. Missing documents, incorrect details, or data that does not accurately describe the loss can skew an estimate. More broadly, the GAO and state-regulator materials identify risks that data and model choices may reproduce discrimination or other systemic biases. New York’s Department of Financial Services discusses accuracy and reliability concerns for some external data sources used in underwriting and pricing; that discussion is not a universal claim-review rule. New York DFS Circular Letter No. 7 (2024); Pennsylvania Insurance Department material.
Conditions change, and explanations can be limited
Patterns in older claims may not hold as circumstances change—a risk known as concept drift. Regulators also warn that complex systems may make it difficult to understand why a score or flag was produced. The Office of the Australian Information Commissioner says AI inferences used for decisions with legal or similarly significant effects call for care about accuracy, appropriateness, and meaningful explanation. OAIC guidance on commercially available AI products; Bank of England and Prudential Regulation Authority.
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- Read the relevant policy sections. Check the coverage, conditions, exclusions, and limits that may apply to the loss.
- Assemble the supporting information. Gather documents that show what happened and establish the nature or amount of the loss.
- Keep a record. Save copies of what you submit and the insurer’s communications.
- Question a prediction or decision. Ask what information and policy terms drove the result, correct factual errors, and request the insurer’s reasons and the applicable policy language.
- Check the applicable review process. Complaint and review rights, deadlines, and insurer obligations depend on the type of insurance and the jurisdiction where the policy was issued.
For example, New York DFS Circular Letter No. 7 (2024) describes a data-accuracy review process in specified AI-supported underwriting situations. It should not be read as creating a general appeal right for insurance claims. The EU, United Kingdom, Australia, and U.S. sources cited here likewise do not establish one universal claims procedure; local rules and the relevant insurance product matter. EIOPA’s 2025 opinion concerns AI governance and risk management, with applicability depending on the entity and law involved. EIOPA Opinion on AI governance and risk management.
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How to judge a claim-prediction tool
If a tool offers a forecast, look for evidence that it is appropriate to your claim type and jurisdiction—not just a general claim that it uses AI. Useful questions include:
- Does it assess an insurer’s workflow or merely give a consumer-facing estimate?
- What claim information and policy terms does it use?
- Does a person review the result?
- Can you see and correct the inputs, and does the tool explain its output?
- Is there independent evidence of accuracy for claims like yours?
The sources cited here do not validate a consumer tool that predicts individual claim approvals. A forecast cannot substitute for the insurer’s coverage decision or the review process available under the applicable policy and law.
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