Insurance Claims Document Automation
AI agents that classify, extract, and route every claim document, medical records, invoices, and adjuster reports, end to end, cutting cycle time from weeks to days.
The Challenge
A regional property and casualty carrier processed over 40,000 claims per year. Each claim arrived as a bundle of documents: police reports, medical records, repair estimates, invoices, photos, and adjuster notes. The intake team had to manually open each document, identify what it was, extract key fields (dates, amounts, policy numbers, diagnosis codes), and enter everything into the claims management system.
The process was a bottleneck. New claims sat in a queue for days before anyone touched them. Peak seasons (storm damage, winter accidents) created backlogs that stretched into weeks. Every day a claim waited was a day the policyholder waited, and customer satisfaction scores reflected it.
The carrier had evaluated two OCR vendors, but neither could handle the range of document types. Medical records alone came in dozens of formats, and handwritten adjuster notes defeated every automated solution they'd tried.
Our Approach
We deployed AI agents that own claims intake end to end. A classifier agent reads each incoming document and decides what it is, medical record, invoice, police report, or adjuster note, then routes it to a specialized extraction agent tuned to insurance domain semantics. A validation agent cross-references the structured data and posts it into the carrier's claims management system. No human touches a document unless the agents ask for help.
The key engineering challenge was accuracy under ambiguity. Insurance documents are messy: handwritten notes, faxed copies of copies, multi-page medical records with overlapping information. So the agents reason about their own certainty: high-confidence work flows through automatically, while genuine edge cases are queued for human review with fields pre-filled, so the reviewer is confirming, not re-entering.
The agents also learn from corrections. When a reviewer fixes an extraction, that feedback loops back in, steadily improving accuracy on the document formats that appear most frequently in the carrier's real-world claims flow.
The Results
Reduction in claims intake time
Document classification accuracy
Document types handled automatically
Average cycle time, down from 8.7
The intake team now focuses on complex claims that require judgment, not data entry. During the last storm season, the carrier processed a 3x surge in claims volume without adding temporary staff, a first in the company's history. Customer satisfaction scores improved by 18 points.