Insurance claims intake
Extract claim facts, check completeness, and route severity without deciding coverage.
What this workflow does
Extract claim facts, check completeness, and route severity without deciding coverage. It resolves identity, enriches the case with approved evidence, and shows why the proposed route is appropriate before anything is assigned.
What you gain
Adjuster-ready claim file
What the AI agent changes
How this worked before
Rule-based routing handled only clean, predefined fields and broke when the request was incomplete or written in natural language.
What the AI agent changes
The agent can interpret unstructured context, gather missing evidence, explain its recommendation, and pause when confidence is low.
Agent trigger
A first notice of loss arrives through an approved channel.
Inputs
- Process context: FNOL, policy, attachments, claimant, and incident data
- Approved policies, ownership, and exception rules
Agent flow
- 1
Resolve the case identity and validate source freshness.
- 2
Enrich the case with approved context and show every scoring signal.
- 3
Prepare a complete intake, severity route, and missing-evidence request with confidence and exceptions.
- 4
Create the approved assignment and notify its owner.
Human decisions
After step 3
The accountable process owner approves a complete intake, severity route, and missing-evidence request.
Outcome
- Adjuster-ready claim file
- Evidence, exceptions, and audit trail
Guardrails
- Do not route low-confidence identity matches automatically.
- Use an allowlist of destinations and writable fields.
Risks and mitigations
A wrong identity match can send a case to the wrong owner.
Require deterministic identifiers and expose the routing rationale.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.
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