AI Claims Intake for Insurance Brokers & MGAs
AI that turns FNOL emails, forms and calls into structured claim files, reads the documents and routes each claim; handlers make every decision.
An example of how we'd build this for a company in this niche: the plan, the architecture and the targets we'd start from. Not a specific client's story.
- Target
- –30%
- handler time to set up a complete claim file, within six months of go-live
- Target
- ≥ 95%
- field accuracy on key data (dates, amounts, policy numbers) on the evaluation set, before go-live
- Target
- Same day
- from first notice to an assigned handler, from go-live
Who this is for
Picture a broker, or an MGA with delegated claims authority, handling a few thousand claims a year across motor, property and liability, where first notices of loss arrive by email, web form and phone in every possible format. Handlers spend the start of each claim re-typing policy numbers, reading attachments and chasing missing documents before any real assessment begins. The goal is an intake layer that captures and structures every notice, extracts what the documents say and routes each claim to the right handler, with people making every decision.
What makes it hard
- Notices arrive as forwarded email chains, scanned PDFs, phone photos and voicemails, often with the policy number missing or wrong.
- Each insurer or capacity provider has its own claim forms, bordereaux and notification deadlines, and a late notification can put cover at risk.
- Attachments range from repair estimates and invoices to police reports and medical notes, and each one has to be read, filed and checked against the policy.
- Fraud signals help, but an automatic 'suspected fraud' label on an honest customer becomes a complaint and possibly a legal problem.
- Everything has to be explainable later: what the AI extracted, who reviewed it and who made the call.
What we'd build
Multi-channel FNOL intake
One queue for claims arriving by email, web form, portal or phone, with calls transcribed and every notice turned into a draft claim record.
Document extraction
Reads policies, invoices, repair estimates and photos, pulls out dates, amounts, parties and policy references, and shows each extracted value next to the source text it came from.
Policy match & completeness checks
Matches the notice to the policy in the admin system, highlights possible coverage questions for the handler, and drafts requests for missing documents for a person to send.
Triage & routing
Suggests line of business, severity and complexity, and routes each claim to the right team or insurer, with the reasons behind every suggestion shown.
Human review & audit trail
Handlers accept, edit or reject every AI suggestion, and each change is logged with the model version, the inputs and the person who decided.
Fraud signals as flags
Patterns such as a loss days after the policy started, bank details shared across unrelated claims or reused photos are raised as flags for a trained handler, never as automatic decisions.
Architecture
From the people who use it down to the hardware and third-party systems it talks to.
- Apps & channels
- Claims inbox & web form
- Broker or client portal
- Phone intake with transcription
- Handler review workspace
- Platform
- Intake workflow engine
- Review queue & task routing
- Audit log
- Evaluation & quality monitoring
- AI services
- Document classification & OCR
- LLM extraction with source links
- Triage & routing suggestions
- Fraud-signal rules
- Integrations
- Policy administration system
- Insurer claims systems
- Document management
- Bordereaux & reporting exports
Delivery plan
The same four phases as every project we run - see how we work.
- Weeks 1–3
Discovery
Walk through past claims with handlers, map channels, insurer requirements and deadlines, and build an evaluation set of anonymised documents to measure the AI against.
- Weeks 4–5
Design & architecture
Claim data model, review screens, the line between what the AI may suggest and what only a person may decide, and the hosting and access setup.
- Weeks 6–16
Build
Email and web intake with extraction first, then triage, phone intake and the policy-system integration, with accuracy measured against the evaluation set on every release.
- Weeks 17–18
Launch & handover
Shadow mode first, comparing AI suggestions with handlers' actual decisions, then go-live line by line and handover of code, prompts and evaluation tooling.
The team
- Product-minded tech lead
- AI engineers
- Backend engineer
- Frontend engineer
- UX/UI designer
- QA & evaluation engineer
Compliance & security
- Injury claims carry health data (GDPR Art. 9) and fraud checks may touch criminal-offence data (Art. 10), so access is restricted by claim type and every view of a sensitive document is logged.
- Fraud signals stay flags for a person: GDPR Art. 22 restricts decisions with legal or similarly significant effects that rest solely on automated processing.
- Under the EU AI Act, risk assessment and pricing in life and health insurance is high-risk; claims intake for other lines generally isn't, but we'd confirm the classification with your compliance team.
- Assisting customers with claims counts as insurance distribution under the IDD, so its duty to act honestly, fairly and professionally applies to AI-assisted intake too.
Tech stack
- Python
- FastAPI
- LLM APIs (EU-hosted)
- OCR & document AI
- PostgreSQL
- pgvector
- Temporal
- TypeScript
- React
- OpenTelemetry
Questions we usually get
Will the AI approve or reject claims?
No. It prepares the file: it extracts data, suggests routing and raises flags, and a handler makes every decision. That keeps accountability where regulators and customers expect it.
Where does our claims data go, and is it used to train models?
We'd host in an EU region and use model providers under terms that exclude training on your data, or run open-weight models in your own cloud if you prefer. Access is limited by role and claim type, and every view of a sensitive document is logged.
How do we know the extraction is accurate enough?
Discovery produces an evaluation set from anonymised past claims, and extraction and routing are measured against it before go-live and on every release. Launch starts in shadow mode, comparing the AI's output with what handlers actually did, so you see real accuracy before relying on it.
Services behind this blueprint
More on the InsurTech industry.
Building something like this?
30 minutes with the engineers who'd build it. We'll test this plan against your situation: scope, integrations and budget.