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How insurers use AI in claims
Insurers may use AI and other predictive methods to process claim information, support triage and claims management, or flag claims for possible fraud review. These are assistance or workflow uses; they do not show that every claim is decided automatically or that a model can correctly determine coverage.
In a 2021 public hearing, the European Insurance and Occupational Pensions Authority (EIOPA) reported that 31% of participating companies used big-data analytics tools such as AI or machine learning, while another 24% were at proof-of-concept stage. Those figures describe the companies in that review, not all insurers, and they are not measures of claim-decision accuracy. EIOPA’s public-hearing material gives the context.
Government and supervisory sources describe insurers exploring or using technology across insurance operations. For example, the U.S. Government Accountability Office discussed AI applications including automated information gathering and risk assessment, with much of its analysis focused on underwriting rather than claim approval. The UK Centre for Data Ethics and Innovation and the Bank of England and Prudential Regulation Authority also describe potential insurance uses and associated risks. GAO (2019), CDEI (2019), and Bank of England and PRA (2022) discuss these uses; none establishes a reliable consumer-facing approval predictor.
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Why a prediction may not match the insurer’s decision
Your policy and the documented loss determine coverage
A model may recognize similarities to prior claims, but similarity is not proof that your loss is covered. The insurer’s assessment depends on the specific policy language, including coverage terms, conditions, and exclusions, as well as the facts and supporting documents for your claim.
The information may be incomplete, wrong, or biased
A forecast is only as useful as the information behind it. Missing documents, incorrect descriptions of an event, or unreliable outside data can distort an output. Regulators have also warned that data and model choices can reproduce bias or have discriminatory effects. The New York Department of Financial Services’ 2024 circular addresses AI and external consumer data in underwriting and pricing; it raises data-quality concerns in that context, not a universal rule for claim decisions.
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Past patterns can change, and explanations may be limited
Patterns shift over time, so a model trained on past cases may not perform the same way in new circumstances. The Bank of England and PRA identify this kind of change, known as concept drift, as a risk for AI used in claims management. Complex systems can also make it difficult to understand why a score or flag was produced. The Australian Information Commissioner advises care when AI inferences affect legal or similarly significant interests, including attention to accuracy, appropriateness, and meaningful explanation. See the OAIC guidance on commercially available AI products.
What to do if you have a live claim
- Read the policy sections that apply. Check the relevant coverage, conditions, and exclusions rather than relying on a generic AI answer.
- Assemble evidence of the loss. Gather the documents, photos, estimates, or other records that establish what happened and the nature or amount of the loss.
- Keep a record. Save what you submit and the insurer’s communications so you can check what information was considered.
- Question a prediction or decision. Ask what information or policy term drove the result, correct factual errors, and request the insurer’s reasons and the applicable policy language.
- Check the process where your policy was issued. Review the insurer’s complaint or claim-review procedure and verify local rules and deadlines. Rights and procedures vary by jurisdiction and type of insurance.
Do not assume that a specific review right applies everywhere. For example, New York’s 2024 circular describes a data-accuracy review process in specified AI-supported underwriting situations; it does not establish a universal appeal right for insurance claims.
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What the evidence can—and cannot—tell you
The sources cited here describe AI use, potential applications, and governance risks. They do not provide an independently validated accuracy rate for predicting whether an individual consumer’s insurance claim will be approved. Adoption figures are not accuracy figures, and a fraud flag is not proof that a claim will be denied. No consumer-facing tool is substantiated here as a dependable way to find out whether an insurer will pay.
Rules also differ across countries and insurance contexts. EIOPA’s 2025 opinion sets out supervisory expectations for AI governance in the EU, with applicability depending on the entity and relevant law; it does not create one global claims procedure. See EIOPA’s 2025 opinion.
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