Manual claims processing took 15+ days with 23% error rate in data extraction.
We built a document processing pipeline that automatically classifies, extracts, and validates data from insurance claims forms and supporting medical records — replacing hours of manual data entry with a human-in-the-loop review system.
Client
A regional insurance carrier...
Timeline
14 weeks
Team
2 engineers
Industry
Insurance
A regional insurance carrier processed hundreds of claims monthly using manual data entry. Adjusters spent the majority of their time extracting information from forms, medical records, and supporting documents rather than evaluating claims. Error rates were high, turnaround was slow, and the team was burning out on repetitive work.
Trained custom document classification models to identify different document types in a claims package
Built an OCR pipeline with post-processing rules for handling both printed and handwritten forms
Implemented entity extraction for medical codes, dates, amounts, and provider information using Python-based ML models
Created a human-in-the-loop review interface where low-confidence extractions get flagged for manual verification
91%
Extraction Accuracy
5 days
Avg Processing Time
3x
Faster Than Manual
77→91%
Accuracy Improvement
Average claims processing time reduced from 15 days to 5 days
Extraction accuracy reached 91%, up from 77% manual baseline
Adjusters freed up to focus on actual claims evaluation instead of data entry
ROI achieved within 8 months of deployment
“Our adjusters were spending more time on data entry than actual claims work. The extraction pipeline changed that completely. They actually evaluate claims now instead of copy-pasting from PDFs all day.”
— Operations Director, Insurance Carrier
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