Insurance runs on documents: policies, endorsements, claims files, medical and repair reports, correspondence and regulations. That makes it a natural fit for generative AI, which excels at reading, summarizing and drafting text. The challenge is doing it in a regulated, high-trust environment.

High-value use cases

1. Claims file summaries

Claims handlers often read hundreds of pages to understand a complex claim. AI can produce structured summaries — timeline, parties, damages, coverage questions, open items — with references to source pages, so handlers spend more time on judgment and less on reading.

2. Policy and coverage Q&A

Using retrieval-augmented generation, staff can ask questions like “is water damage from a burst pipe covered under this policy form?” and receive answers grounded in the actual policy wording, with citations.

3. Correspondence drafting

AI drafts letters and emails to customers, brokers and repairers in clear language, following templates and tone guidelines. Humans review before sending, especially for coverage decisions.

4. Underwriting research

Summarizing submissions, extracting key risk data from documents and researching businesses speeds up commercial underwriting.

5. Knowledge for agents and call centers

Assistants help service staff find answers in product manuals and procedures during calls.

6. Software modernization

Many insurers run decades-old systems. AI coding assistants help document and modernize legacy code.

The guardrails

Ground answers in documents. Use retrieval with citations rather than letting models answer from general knowledge about insurance.

Keep humans accountable. AI may draft and summarize; licensed professionals decide coverage and claims outcomes, especially denials.

Protect data. Claims contain health, financial and personal information. Use enterprise deployments with contractual data protections, access controls and logging.

Test for errors that matter. Evaluate on real files: omissions of key facts, wrong dates or amounts, misread exclusions. See how to build an evaluation set.

Fairness. Check whether outputs differ systematically across customer groups, and avoid using AI outputs as hidden underwriting factors.

Explainability and records. Keep records of AI involvement in decisions to respond to regulators and complaints.

Governance framework. Use a recognized framework such as the NIST AI Risk Management Framework, and align with insurance regulators’ guidance.

Getting started

  1. Pick one document-heavy process with measurable time costs.
  2. Run a pilot with a small team, using real but protected data.
  3. Measure time saved, error rates and user satisfaction against a baseline.
  4. Scale what works, with monitoring.

A sample claims summary template

Asking the AI to fill a fixed structure makes summaries consistent and easier to check. A template might include:

  1. Claim overview: policy number, insured, date and type of loss.
  2. Timeline: key events with the source document and page for each.
  3. Parties: claimant, witnesses, repairers, medical providers, other insurers.
  4. Damages claimed: amounts and supporting documents.
  5. Coverage questions: relevant policy sections and possible exclusions, quoted with references.
  6. Inconsistencies: dates, amounts or statements that conflict between documents.
  7. Missing information: documents still needed.
  8. Suggested next steps for the handler to consider.

Instruct the model to write “not found in the file” rather than guessing, and to cite page references for every fact. Handlers can then spot-check citations quickly.

Measuring success

Useful metrics include handler time per file, the share of summaries needing correction, the number of serious errors such as wrong amounts or missed exclusions, customer response times, and handler satisfaction. Comparing a pilot group with a control group gives far more reliable results than anecdotes.

What to watch next

  • Regulatory expectations for generative AI in claims decisions.
  • Vendor tools specialized for insurance documents.
  • Measured productivity results from large insurers.
  • Agentic AI handling end-to-end simple claims.

Key takeaways

  • Insurance is document-heavy, which suits generative AI.
  • Claims summaries, policy Q&A and correspondence drafting are high-value uses.
  • Ground answers in policy documents with citations.
  • Licensed professionals must stay accountable for coverage and claims decisions.
  • Protect data, test for consequential errors and keep records of AI involvement.

Frequently asked questions

Can generative AI decide claims?

It can assist with summaries and drafts, but coverage and claims decisions — especially denials — should be made and owned by qualified people.

Is it safe to upload claims files to AI tools?

Only to enterprise deployments with appropriate data-protection terms, security controls and compliance approval.

Where should an insurer start?

With a single document-heavy process, a measured pilot and a clear evaluation of time savings and errors.