Insurance Document Pipelines: Reducing Manual Review Time

The Challenge

An insurance company processed over 2,000 policy applications per week, each requiring manual review of supporting documents: identity verification, medical reports, financial statements, and prior coverage letters. Reviewers spent an average of 45 minutes per application, with significant variation in quality and consistency.

Our Approach

We designed a multi-stage document processing pipeline combining OCR, NLP classification, and rule-based validation. The system was built on a Python backend with Azure Cognitive Services for document intelligence and a .NET API layer for integration with the existing policy administration system.

The pipeline worked in four stages:

1. Ingestion: Documents uploaded via web portal or received via email were normalized to a standard format

2. Classification: ML models identified document types (medical report, ID, financial statement) with 97% accuracy

3. Extraction: Named entity recognition pulled key data points: names, dates, amounts, policy numbers

4. Validation: Business rules cross-referenced extracted data against application forms, flagging discrepancies for human review

The Angular frontend provided reviewers with a split-screen interface: original document on the left, extracted and validated data on the right, with confidence scores and highlighted discrepancies.

Results

• Review time reduced from 45 minutes to 8 minutes per application
• Document classification accuracy of 97.3%
• Consistency improvement: variance in review quality dropped by 60%
• Throughput increase: same team now handles 3x the volume

The system processes documents in near real-time, with the full pipeline completing within 90 seconds per submission. Human reviewers now focus on exceptions and edge cases rather than routine data entry.