How to build a Claims FNOL Intake and Triage Agent

TL;DR

• What it does: Captures FNOL details, extracts structured fields, scores severity, and routes to the right queue with next steps.

• Who it’s for: Claims Ops, Adjusters, TPAs, Brokers, Contact Center teams.

• Time to value: ~30–60 min setup; 2–5 min per claim after.

• Output: Structured claim JSON, priority score, missing-info checklist, acknowledgment, ticket creation.

• Fit: Works with email/web forms + file uploads (Drive/SharePoint); pushes to Guidewire/Jira/ServiceNow; exports CSV/JSON/PDF.

Common Pain Points of FNOL

• Incomplete or messy intake (missing policy number, dates, location).

• Slow back-and-forth for clarifications; long first-response times.

• Manual rekeying into core systems; frequent errors.

• Poor prioritization; severe claims buried in queues.

• Scattered photos/docs across channels; PII/compliance risks.

What the Agent Delivers

• Structured extraction of claimant, policy, incident details, location, attachments.

• Smart follow-up questions to fill gaps and reduce back-and-forth.

• Coverage validation hook + severity/urgency score with queue routing.

• Duplicate detection and optional fraud/red-flag signals.

• One-click outputs: claim ticket, acknowledgment email, triage summary; exports (PDF/CSV/JSON).

Step-by-Step Build (StackAI Nodes)

1. Claim FNOL Input (in-0)

• Purpose: Starting point where the user provides claim details (who, what, when, where, how).

• How it works: User submits claim information, which flows into the next step.

2. Extract and Triage Claim Information(llm-0)

• Purpose: AI reads and structures claim details.

• How it works:

  
  

  • Extracts key fields: claimant name, contact, incident date, loss type, description, location, urgency.

  • Flags missing details and asks clarifying questions if needed.

  • Categorizes urgency and suggests initial triage actions.

3. Analyze Claim Data (action-0)

• Purpose: Perform deeper analysis on extracted data.

• How it works:

  
  

  • Uses Python code to validate, process, or enrich claim details.

  • Outputs: analysis success/failure, executed code, results, and optional visuals (charts/images).

  
  

4. Format the Summary (template-0)

• Purpose: Create a clear, structured summary.

• How it works:

  
  

  • Combines claim details + triage recommendations.

  • Outputs in Markdown with two sections:

    
    

    • Claim Details

    • Triage & Next Steps

5. Display Result (out-0)

• Purpose: Show the final summary to the user in a clear, review-ready format.

Summary Table