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AI is already being used to sort claim information, extract details from documents, flag possible fraud, support handlers and, in some narrow workflows, automate parts of a settlement. But these ten examples are a mix of insurer-built systems, vendor products and insurer deployments—not ten equivalent products, and not evidence that AI has “fixed” claims forever.
Ten examples of AI in insurance claims
The figures below come from company or vendor case studies unless otherwise noted. They describe different claim types and steps, so they are not comparable performance benchmarks. Where a result is not established in the cited material, it is identified as not stated rather than inferred.
| Example | Claim type and AI role | Reported result and evidence |
|---|---|---|
| DOMCURA KIM (Germany; insurer-built, offered to the market) | A modular AI agent platform for claims; the case study describes payment of qualified claims. | Microsoft Customer Stories, dated August 7, 2026, reports qualified claims paid in about 10 minutes and operational costs reduced by 50%. These are case-study claims, not an independent audit. |
| Allianz Project Nemo (Australia; insurer deployment) | Launched in July 2025 for low-complexity food-spoilage claims. Specialized agents support planning, security, coverage checks, weather verification, fraud screening and payout calculation; a human makes the payout decision. | Allianz reports an 80% reduction in claim processing and settlement time for this specified workflow. |
| Direct Pojišťovna (insurer-built windshield-claim workflow) | Worked with BigHub on a modular, agent-based workflow using Azure AI Foundry and Azure Document Intelligence, with human involvement. | Microsoft Customer Stories, dated July 10, 2026, reports handling time falling from 15 minutes to about 2 minutes per claim and 60% of windshield claims fully automated. The insurer’s 70% automation figure is a target for the following two to three years, not an achieved result. |
| Swiss Re ClaimsGenAI (handler assistance) | A generative AI tool intended to help claims handlers work through claim documents; Swiss Re says it is exploring wider potential. | A quantified result is not stated in the described source. |
| Nolana (vendor platform) | Nolana describes examples involving dormant Lloyd’s claims, broker first-notice-of-loss intake and Zurich travel claims. | Specific performance figures are not stated here. The examples and any outcomes should be treated as Nolana’s own case-study claims, not independent evaluation. |
| Vitraya (vendor platform for health claims) | Its case study describes agents for document review, policy-benefit checks and fraud screening in health-claim adjudication. | Vitraya reports customer outcomes, but a specific result is not stated here; the reported outcomes are vendor claims pending independent corroboration. |
| UST SmartOps (health-claim report validation) | UST describes a solution used for a US health insurer to validate claims reports and support approval or rejection decisions. | UST reports a 66.6% acceleration in approval/rejection decision-making. This is a UST case-study claim. |
| Vestval Flow (handler copilot) | The vendor describes summaries generated from policy and claim documents, plus drafted responses for handlers. | Independently verified outcomes are not established in the described case-study material. |
| AKINO Labs (UK motor and property first-notice-of-loss triage) | A workflow that acknowledges some new claims without handler contact. | A September 2026 case-study result from AKINO says 74% of new claims are acknowledged without handler contact. This is vendor-reported. |
| Aviva’s AI claims journey (insurer implementation, not an off-the-shelf product) | McKinsey describes Aviva working with QuantumBlack and Orphoz and building more than 80 AI models. For personal-injury claims, the described process uses a human path by default. | The case study reports the number of models, not a comparable claims-processing performance result. Its publication year is not established in the described page. |
What AI does in the claims process
Claims handling is a chain of tasks rather than a single decision. AI can assist with first notice of loss (FNOL)—the initial report of a claim—by extracting information from forms and documents, organizing an incoming file or acknowledging a claim. It can also help route work, check policy information, flag possible fraud, summarize a file or suggest a next step. A system that summarizes documents or drafts a response supports a handler; it does not, by itself, decide coverage or settle a claim.
The clearest examples of narrow automation in this group are repeatable claim categories such as food spoilage and windshield damage, alongside qualified claims in DOMCURA’s reported workflow. Their defined scope matters: a result for one claim type should not be assumed to apply to complex injury claims, catastrophe losses or another insurer’s portfolio.
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Why the reported percentages are not a league table
The case studies measure different things: elapsed time to process and settle a particular claim type, handling time per windshield claim, the share of claims automated, an acceleration in decision-making, or the share acknowledged without handler contact. They also come from different organizations and do not establish a common baseline, claim mix or measurement method. A larger percentage in one study therefore does not show that its platform is better than another.
For example, reducing handling time per windshield claim is not the same outcome as reducing end-to-end settlement time for food-spoilage claims. Likewise, an acknowledgement without handler contact says something about intake, not whether a claim was accurately assessed or paid. Treat each number as a result reported for its stated workflow, not as a general forecast.
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Human review, escalation and governance
Automation should be described at the level the workflow supports. Allianz says a human makes the payout decision in Project Nemo, and Direct Pojišťovna explicitly describes human involvement. Aviva’s case study says personal-injury claims default to a human path. These examples illustrate why it is important to distinguish task assistance, automated handling of a defined step and a system that can complete a qualified claim.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess an AI claims platform
For insurers evaluating claims automation software, compare systems on the same workflow and ask for evidence that matches the intended use:
- Claim scope: Which line of business and claim types are supported, and which cases are excluded or escalated?
- Decision boundary: Does the system collect and summarize information, recommend an action, automate a defined step or settle qualified claims?
- Human controls: Who reviews coverage, fraud flags and payment decisions, and how can staff override or escalate a result?
- Integration: How does it connect to existing policy, claims, document and payment systems? The examples here do not establish a common integration model across vendors.
- Measurement: What is the baseline, what counts as processing time or automation, which claims were included, and who verified the reported result?
- Governance and data handling: What records support a decision, how are errors handled, and what controls apply to the data and models in the relevant jurisdiction?
Ask for results on the claim types and operating conditions your organization actually handles. A vendor case study can show what its publisher says happened in a deployment, but it is not a substitute for a defined pilot, due diligence and legal or compliance review.
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