Automated Claims Processing with Document AI

Key Takeaways
- Custom layout-aware document models automated extraction from police reports, medical bills, and repair estimates.
- Straight-through automated processing reached 80% for routine property claims, dropping cycle time from 12 days to 36 hours.
- Integrated fraud validation engines flagged repair estimate cost anomalies exceeding 2 standard deviations.

The Challenge

A property insurer handling 15,000 monthly claims relied on a 60-person data entry team to transcribe scanned paper claim packets. The manual process took 12 days from First Notice of Loss (FNOL) to settlement, with an 8% clerical error rate.

Architecture & Technical Approach

• Document Classification: Fine-tuned vision models classify uploaded documents into 12 distinct insurance claim categories with 97.5% accuracy.

• Field Extraction Models: Custom Document AI models extract line items, labor costs, diagnostic codes, and tax IDs into normalized database schemas.

• Automated Adjudication Engine: Cross-references policy limits and fraud databases, routing clean claims directly to automated payout gateways.

Quantitative Benchmarks & Results

Production Reliability & Lessons Learned

Routing low-confidence extractions (< 95% model confidence) to human adjusters with pre-filled bounding box overlays maximized throughput while maintaining zero audit errors.