agents × insurance

Enterprise AI agents for insurance.

Agents that triage underwriting submissions, accelerate FNOL and claims, and surface fraud signals for the SIU — each decision cited to the policy wording and logged for market-conduct review. Built for carriers, MGAs, and TPAs.

Underwriting triage FNOL & claims Fraud-signal detection

Throughput on submissions and claims, without losing discipline

An insurer's loss ratio and expense ratio are both decided in queues. Underwriters drown in submissions, most of which fall outside appetite or arrive incomplete; claims teams race the clock on first notice of loss while leakage and fraud slip through under volume pressure. The work is repetitive and rules-bound, but the stakes are real money and real regulatory exposure — bind the wrong risk or mishandle a claim and the cost compounds. That tension is precisely where an agent earns its keep.

An eeko insurance agent reads the submission or the loss notice, extracts the structured data, checks it against your appetite, guidelines, and policy wording, and either clears it, prioritizes it, or routes it to an underwriter or adjuster with the analysis already done. Every disposition cites the guideline or clause it applied and is written to an audit log — so you get throughput and a record you can defend in a market-conduct exam or coverage dispute.

Agents wired into your policy and claims systems.

Three hardened layers, tuned to underwriting discipline and fair-claims handling.

01 / toolingCORE
Tool-calling into insurance systems
Typed, permissioned connectors into your policy admin and claims systems, rating engine, ACORD intake, and third-party data so the agent reads submissions and sets up claims through controlled interfaces.
  • Policy admin & claims (Guidewire-class)
  • ACORD submission & FNOL intake
  • Rating engine + third-party data
02 / retrievalCORE
RAG on insurance data
Grounded retrieval over policy wordings, underwriting guidelines, the claims manual, and loss history so every triage and coverage answer cites the exact form, endorsement, or rule it relied on.
  • Policy wording & endorsement lookup
  • Underwriting guideline + claims manual
  • Coverage Q&A with citations
03 / oversightSECURE
Audit, guardrails & human-in-the-loop
Coverage-binding and claim-payment actions require human sign-off, the full reasoning chain is logged immutably for conduct exams, and the agent applies your filed rules consistently.
  • Sign-off on binding & payments
  • Append-only, exam-ready logs
  • Consistent application of filed rules

Where AI agents pay back in insurance

The fastest returns sit in the submission and claim queues that drive both ratios:

  • Underwriting triage — agents clear submissions against appetite, complete the data from third-party sources, and hand underwriters a ranked, pre-analyzed pipeline instead of an undifferentiated inbox.
  • FNOL & claims setup — automated first-notice intake, coverage verification, and claim file assembly that gets the right adjuster the right file in minutes, reducing cycle time and leakage.
  • Policy & coverage Q&A — agents answer producer and policyholder questions straight from the wording, citing the form and endorsement, so service teams stop guessing at coverage.
  • Fraud-signal detection — continuous review of claims for inconsistency, staging, and known fraud patterns, packaging suspicious files into evidenced referrals for the SIU.

Common questions.

How do AI agents apply to insurance?

AI agents work the submission and claim queues that define carrier economics — triaging and clearing underwriting submissions against appetite, intaking first notice of loss and setting up claims, answering policy and coverage questions from the document, and flagging fraud signals for the SIU. Each agent reads from your policy admin and claims systems through controlled interfaces, grounds its judgment in the policy wording and your guidelines, and routes anything that binds coverage or pays a claim to a licensed human.

How do you handle compliance and fair-claims requirements?

Every agent decision cites the policy clause, guideline, or rule it applied, and the full reasoning chain is written to an append-only log you can produce in a market-conduct exam or coverage dispute. Underwriting and claims actions sit behind human sign-off, models can run inside your own environment so policyholder data stays protected, and the agent applies your filed rules consistently rather than improvising — which is the core of fair-claims and unfair-trade-practice compliance.

Related paths.

More throughput, same discipline.

Show us your submission inbox or your FNOL queue, and we will scope an agent that triages at machine speed, cites the wording, and keeps an underwriter or adjuster in the loop. Response inside 24 hours.