AI in insurance: claims, underwriting and broker work that AI handles well (2026).
Where AI is paying off for insurers, MGAs and brokers, from first notice of loss to underwriting submissions, and the rules that apply when AI touches pricing and claims decisions.
Insurance runs on documents and conversations: claim forms, photos, repair estimates, medical reports, broker submissions, policy wordings and endless questions about cover. That’s why it’s one of the industries where AI earns its keep fastest. The best results today come from systems that gather, read, check and draft, so that adjusters, underwriters and brokers spend their time on judgement. Where AI makes or shapes decisions about people’s cover or claims, the rules tighten, and the design has to follow.
Where AI pays off in insurance
Claims intake (first notice of loss)
An AI agent takes the first report of a claim by web, app, WhatsApp or email at any hour. It asks the right questions for the claim type, collects photos and documents, checks the policy is active, and opens a complete claim file. Adjusters start with everything they need instead of chasing it.
Claims documents
Repair estimates, invoices, medical reports and police reports are read, the key facts extracted and cross-checked against the policy and each other. Inconsistencies are flagged for a person, such as a repair date before the incident or an invoice that doesn’t match the estimate.
Underwriting submissions
For commercial lines, broker submissions arrive as emails with schedules, loss runs and questionnaires in every format. AI extracts the risk details into your underwriting system, flags what’s missing and drafts the questions back to the broker. Underwriters see a clean submission, not an inbox.
Policy questions for customers and brokers
An assistant that answers “am I covered for…?” from the actual policy wording, with a reference to the clause. It hands over to a person when the answer depends on judgement or the customer is making a complaint.
Renewals and service
Drafting renewal communications, answering mid-term changes, chasing documents and keeping the CRM current.
The rules that apply to insurance AI
- EU AI Act. AI used for risk assessment and pricing of natural persons in life and health insurance is high-risk under Annex III. Those obligations now apply from 2 December 2027, after the Digital Omnibus delay. Customer-facing chatbots must already tell people they’re talking to AI. See our EU AI Act guide.
- UK. The FCA applies its existing rules to AI rather than new ones. Under the Consumer Duty, AI-assisted processes must deliver good outcomes for customers, including vulnerable ones, and a senior manager is accountable. UK GDPR’s new automated-decision safeguards (in force since 5 February 2026) require that people can contest significant automated decisions and get human intervention. Our guide to UK AI rules covers both.
- US. Colorado’s replacement AI law, SB 26-189, covers automated decision-making in consequential decisions including insurance, effective 1 January 2027. Deployers must give notice, explain adverse outcomes, and offer correction and human review. California’s CCPA rules on automated decision-making technology require compliance from 1 January 2027. State insurance regulators also expect insurers to be able to explain how their models reach outcomes.
What makes insurance AI trustworthy
| Requirement | How it’s built |
|---|---|
| Answers grounded in the policy | Retrieval over the actual wordings and endorsements, with clause references, and “I can’t confirm that” when the wording doesn’t say |
| Accurate extraction | Confidence checks on every field, cross-checks against the policy and claim, and review for anything uncertain |
| Fair outcomes | Outcome monitoring by customer group before launch and in production |
| Explainability | A record of the inputs, the system’s output and the person who decided, for every claim or underwriting decision it touched |
| Fraud resistance | Uploaded documents and messages treated as untrusted input (see prompt injection is an input-validation problem) |
Where to start
For most insurers and MGAs, claims intake or submission handling comes first. Both are high-volume and easy to measure (time to a complete file, time to quote), and a person reviews the output before anything is decided. For brokers, a policy-question assistant and submission preparation usually give the fastest return.
The adjuster’s best day is one where every file arrives complete. That’s a realistic first goal for AI.
Questions insurers ask
Should we build or buy?
Buy for common, standardised tasks. Build when the system has to work with your own wordings, systems, products and underwriting rules, or when the workflow is part of how you compete.
Can AI detect fraud?
Machine-learning models trained on your claims history flag suspicious patterns. Language models help by reading the documents and spotting inconsistencies. Both should send cases to an investigator, not decline them.
Does it work with our core platform?
Usually, through its APIs, document stores and email. Integration with the policy and claims system is typically most of the work.
Modulus Labs AI builds claims intake agents, document AI and policy assistants for insurers, MGAs and brokers, with decision records and human review built in. Tell us where the paperwork piles up, and we’ll reply within one business day.