NLP — Claims & Endorsement Processing Pipeline
An operational NLP pipeline that removes the manual data-entry bottleneck in claims processing — ingesting scanned documents, extracting structured fields via NER, validating against business rules, and routing low-confidence cases to human reviewers before writing to downstream systems.
OCR + NER
Pipeline
Claims + endorsements
Routing
Human-in-the-loop
Review
Problem & use case
Claims operations teams manually keyed structured data from scanned claim forms and endorsement documents — time-consuming, error-prone, and a bottleneck to settlement speed.
Solution & engineering approach
An OCR + spaCy + PyTorch NER pipeline that extracts structured fields, validates them against business rules, routes ambiguous cases to human review, and writes confirmed records to the claims management system and Elasticsearch index.
Architecture & data flow
- 01
OCR scanned claim + endorsement documents
- 02
spaCy + custom NER extract structured fields
- 03
Validate against business rules
- 04
Push to claims system + Elasticsearch index
- 05
Human-in-the-loop review for low-confidence cases
Technology stack
Implementation status
Implemented — OCR + NER pipeline integrated with claims system and Elasticsearch