Generative AI: Insurance

Generative AI in insurance

Insurance runs on documents: loss notifications, repair estimates, broker submissions, schedules of values and policy wordings. Generative AI is finally good at reading them. We build claims extraction pipelines, FNOL agents and underwriting intake that turn those documents into validated data, route anything uncertain to a handler, and leave the audit trail a regulator expects. We have shipped this pattern for a UK broker's claims operation.

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Key highlights

We build claims document extraction, FNOL voice and chat agents, commercial submission intake, policy wording copilots and SIU case support.
Extracted fields are validated against your policy and claims systems of record, and low confidence items go to a handler review queue.
Coverage and settlement decisions stay with handlers and underwriters. The AI reads, gathers and drafts.
We integrate with Guidewire, Duck Creek, Sapiens, Acturis and Open GI through their supported APIs.
For a UK broker we combined AWS Textract with LLM structured extraction and confidence based triage, so reviewers only touch low confidence claims.

Why insurance AI projects stall

Insurers have no shortage of documents to automate. AI projects stall because extraction is not validated against the policy, decisions are not explainable, and the pilot never connects to the core claims or policy system.

Handlers retyping the same fields. Claim forms, police reports, repair estimates and photos arrive as a bundle. Handlers open each one, key the policy number, loss date and amounts by hand, and errors flow straight into reserves and payments.

Underwriters buried in submissions. Commercial submissions arrive as broker emails with schedules of values, loss runs and proposal forms in different layouts. Underwriters spend their time keying data instead of pricing risk, and slow quotes lose business.

Demos that ignore the policy wording. A generic chatbot answers coverage questions from general knowledge rather than the actual wording and endorsements on the policy. One wrong answer and the project is shut down.

Pilots disconnected from the core system. The extraction demo writes to a spreadsheet. Getting it into Guidewire or a 20 year old policy admin system was never scoped, so the pilot cannot go live.

Decisions nobody can explain. Regulators and courts expect a reason for a declined claim or a priced risk. A model output with no trail from source document to field to decision is a liability, not a saving.

Out of hours FNOL goes to voicemail. Policyholders report losses at night and at weekends. A voicemail and a callback the next day is a poor first experience at the worst moment, and details are lost.

Use cases

Generative AI use cases we build for insurance

Each use case is built around documents and data you already hold, with validation against systems of record and a handler or underwriter making the decision.

Claims document extraction and triage

Documents are classified, OCR is run through AWS Textract or Azure Document Intelligence, and an LLM extracts fields into a typed schema. Values are checked against the policy and claims systems, and confidence scores route claims to fast track, review or escalation.

FNOL voice and chat agents

Voice and chat agents take first notification of loss around the clock, verify the policy, capture the incident in a structured form, request photos and open the claim in your claims system. Injury, vulnerability and complex losses go straight to a person.

Commercial submission intake

Agents read broker emails and attachments, extract proposal data, schedules of values and loss runs into your underwriting workbench, flag missing information and draft the request back to the broker for the underwriter to send.

Policy wording and coverage copilot

RAG over the specific policy wording, schedule and endorsements in force, so handlers get an answer with the clause cited. It supports the handler's coverage decision rather than making it.

SIU and fraud case support

When your fraud model or rules flag a claim, an agent assembles the claim history, related parties, prior claims and document inconsistencies into a case summary for the special investigations unit to review.

Correspondence and complaint drafting

Draft claim updates, decision letters and complaint responses grounded in the claim file and your templates, written in plain language and checked by a handler before sending, with complaint signals logged for Consumer Duty reporting.

How we deliver

From one claim type to a governed rollout

01
Use case discovery and data readiness
We pick one line of business and one document flow, sample real claims or submissions, and check access to policy and claims systems. Output: a written scope, the field list with acceptance thresholds and the integration plan.
02
Evaluation harness and prototype
A golden set of real, masked documents is labelled with your handlers, then OCR and model combinations are tested field by field for accuracy, cost and latency. The winning pipeline is chosen on measured results.
03
Guarded pilot with handlers in the loop
The pipeline runs on live claims with every output reviewed, alongside the current process. Corrections feed the golden set, and confidence thresholds are set from evidence rather than guesswork.
04
Production rollout with monitoring
Write back to the claims or policy system, routing lanes switched on, and dashboards for accuracy by field, straight through rate, cost per claim and drift. Further loss types and lines follow the same template.

AI governance for insurance

Insurance regulators now expect written governance for AI used in claims, pricing and underwriting. We build the controls and the evidence into the system so your compliance team is not reconstructing them later.

NAIC Model Bulletin on AI (US) FCA Consumer Duty (UK) EU AI Act (life and health pricing high risk) IRDAI data residency (India) Solvency II awareness GDPR and UK GDPR PCI DSS for premiums UAE Insurance Authority awareness

Every field traces back to a document. Each extracted value stores the source document, page, bounding box, model and version, confidence score and the handler who confirmed or changed it. That gives you the documented governance the NAIC Model Bulletin asks of US insurers and the evidence of good customer outcomes the FCA expects under the Consumer Duty.

Pricing and decisions stay explainable. The EU AI Act treats risk assessment and pricing for life and health insurance of individuals as high risk. We keep pricing and claim decisions in versioned rules and models your actuaries can audit, and use LLMs for reading, extraction and drafting, with human oversight on every decision.

Policyholder data stays where it must. Models run on enterprise endpoints with retention disabled, in the region your data must stay in, such as Indian cloud regions for IRDAI regulated policyholder data, or as self hosted open weight models. Personal and health data is redacted where the task allows, and access is role based and logged.

Models and tooling we use for insurance AI

Proven OCR for forms, frontier models for extraction and reasoning, open weight models where data must stay inside, and adapters for the core systems insurers run.

AWS Textract Azure Document Intelligence Claude GPT Gemini Llama (self hosted) LangGraph pgvector Guidewire and Duck Creek APIs LiveKit and Asterisk voice Microsoft Presidio PII redaction Langfuse tracing

Related case study

AI Document Processing for Insurance Claims

Automated insurance claims processing with AI document extraction and validation. 85% of claims auto triaged, processing time cut from 5 days to 4 hours.

Read the full case study

FAQ

Common questions

How much does generative AI for claims or underwriting cost?

It depends on the number of document types and integrations, so we start with a fixed price prototype on one flow and price production from a written scope. Model and OCR costs are passed through at cost and reported per claim or per submission, so you can compare them with handling cost directly. You get a written estimate within 48 hours, and our pricing page explains how engagements are structured.

Is policyholder data safe with an LLM?

Yes, with the right setup. Models run on enterprise endpoints with retention disabled in your required region, or self hosted, personal and health data is redacted where the task allows, and every prompt and output is logged with role based access. Consumer AI tools are never used.

How accurate is AI extraction on messy claims documents?

Accurate enough to automate most standard fields, but you should measure it on your own documents before trusting it. We build a labelled golden set from your real claims, report accuracy field by field, and set confidence thresholds so anything uncertain goes to a handler. Values are also validated against the policy record, which catches most remaining errors.

How long does it take to automate a claims flow?

Usually 10 to 16 weeks for one line of business. A measured prototype takes about 4 weeks, then a guarded pilot runs on live claims alongside the current process, followed by write back into the claims system. Further loss types reuse the same pipeline and move faster.

Should we buy an insurance AI platform or build our own?

Buy if a platform already covers your document types and plugs into your core system. Build when your documents, lines of business or core system are unusual, when you want to own the extraction logic and data, or when the platform would sit outside your audit trail. We often combine both: a bought OCR service with custom extraction, validation and routing.

Will AI decide whether a claim is paid?

No. In our designs the AI extracts, validates and recommends a routing lane, while coverage and settlement decisions stay with handlers or with versioned business rules you control. Every recommendation and override is logged so decisions can be explained to customers, regulators and courts.

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