AI Document Processing: 80% Faster Claims Review
An insurance company's claims team was manually reviewing 500+ documents per day. We built an LLM-powered pipeline that extracts, classifies, and summarizes claim documents, cutting review time by 80%.
The Challenge
What was getting in the way
- 01
Claims adjusters spent 3+ hours per day reading and extracting data from PDFs, medical reports, and police reports
- 02
Manual data entry into the claims system was error-prone. About 12% of claims had data discrepancies that caused processing delays
- 03
Peak season (storm, accident spikes) created backlogs of 2,000+ unprocessed claims. Hiring temp staff took weeks
The Solution
How we solved it
We built a document processing pipeline using GPT-4o for extraction and classification, with Tesseract OCR for scanned documents. Each document goes through: OCR (if needed), classification (medical report, police report, invoice, etc.), structured data extraction (dates, amounts, names, policy numbers), and a summary generation step. Extracted data is validated against business rules and pushed directly into the claims management system via API. Adjusters now review a pre-filled summary instead of reading raw documents. For straightforward claims, the system auto-fills everything and just needs a human sign-off.
Technologies
What We Built
A look inside the project
The Process
Step-by-step delivery
Document Intake
Receive PDFs, scans, and emails via API and email parsing
OCR & Classification
Extract text from scans, classify document type automatically
Data Extraction
Pull structured fields using GPT-4o with schema validation
Validation & QA
Check extracted data against business rules, flag exceptions
System Integration
Push validated data into claims management system via API
The Results
The numbers
Faster Document Review
Extraction Accuracy
Annual Processing Cost Savings