AI agents for insurance claims
AI agents for insurance claims read incoming claim documents, check them against the policy, ask the customer for anything missing and route a complete file to the right adjuster. HATZS builds them on top of your existing claims system, with adjusters approving every coverage decision.
Book a scoping callAI agents for insurance claims read incoming claim documents, check them against the policy, ask the customer for anything missing and route a complete file to the right adjuster. HATZS builds them on top of your existing claims system, with adjusters approving every coverage decision.
Book a scoping callWhy insurance claims processing is slow
A claim rarely arrives complete. It comes by email, phone or portal, with photos, police reports, invoices and forms in different formats. Someone has to open each file, key the details into the claims system, check the policy and chase the customer for what is missing. Most of that time goes on handling paperwork, not on judging the claim.
The delay builds up in the handoffs. A claim waits in a shared inbox, then for a data-entry step, then for a coverage check, then for an adjuster with capacity. Customers call to ask for status, which adds more work. Insurance claims automation targets exactly these waiting steps, so adjusters spend their time on the decisions that need a person.
Claims also depend on rules: coverage terms, authority limits, fraud indicators and regulatory deadlines. In discovery we write these down as the limits the agent works within, and we test it on past claims before it touches a live file. When every claim is read and summarized the same way, files also reach adjusters with the same fields filled in, the same questions asked and the same audit trail, so quality is easier to check and stuck claims are easier to find.
How an AI agent works on an insurance claim
An AI agent for insurance is software that works toward a goal: a complete, correctly routed claim file. It plans the steps, calls your systems and checks its own results. This is the typical flow for first notice of loss and early triage.
- 1
Receive the claim
The agent picks up a new claim from email, a portal form or your intake queue and opens a record in the claims system.
- 2
Read the documents
It reads photos, forms, invoices and reports, extracts the key fields and flags anything unreadable or inconsistent.
- 3
Check the policy
It looks up the policy, compares the loss details with coverage and dates, and notes any condition that needs a person to look at it.
- 4
Ask for what is missing
It sends the customer a clear request for missing documents or details and updates the file when they arrive.
- 5
Route with a summary
It assigns the claim to the right adjuster or queue with a short summary of the facts, the open questions and the documents attached.
- 6
Adjuster decides
A person approves, corrects or rejects each coverage and payment decision. Every step is logged for audit.
What the agent integrates with
The agent works inside the systems your claims team already uses. We connect through APIs where they exist and use RPA only where a legacy screen has no other way in.
Claims and policy systems
Reads policy and coverage data and creates or updates claim records.
Email and customer portals
Receives new claims and sends status updates and document requests.
Document stores
Pulls photos, forms and reports, and files the extracted data back with the claim.
CRM and messaging
Keeps the customer record and conversation history in step with the claim.
Payments and finance
Hands approved claims to your payment and reserve workflows.
Reporting tools
Writes cycle-time and exception data to your dashboards.
Results from insurance automation
Lower processing costs
Workflow automation cut operational processing costs by 30% on the BrokerOS.ai insurance platform.
Implementation timeline
We follow the same four phases as our other AI agent projects, so the timings below match those on our AI agent development page.
- 1–2 weeks
Discover
Map the claims workflow, pick the first use case and agree the success measures and approval points.
- 4–6 weeks
Prove
Build a working demo on real, anonymized claims and connect the first systems.
- 6–10 weeks
Pilot
Run the agent with a small team, measure accuracy against your baseline and tune the guardrails.
- 3–6 months
Scale
Extend to more claim types and queues, with monitoring and a clear operating model.