Claim document extraction for an insurance platform
Problem
An insurance claims platform received accident notification reports, odometer photos and vehicle ID plates as unstructured images attached to each claim. Staff retyped the same fields — odometer reading, VIN, accident narrative — by hand from every attachment before a claim could be triaged.
Approach
Built an event-driven pipeline that classifies each attachment, then routes it to the right extraction model: fast plate recognition for number plates, layout-aware OCR for odometer readings and VIN plates, running on serverless infrastructure that scales to zero between claims.
Outcome
Key-information extraction pulled VIN, odometer and ANR straight from photos.
Python · ONNX Runtime · YOLO · PaddleOCR · OpenCV · AWS Lambda · AWS Batch · Amazon SQS · Docker · FastAPI · PyTorch · scikit-learn · MLflow · Pydantic · Boto3