Processing Insurance Documents on AWS at Scale

The Problem

An enterprise insurance company in the US processes thousands of policy documents, claims forms, endorsements, and regulatory filings. Each document contains critical structured and unstructured data that needs to be extracted, classified, and fed into downstream systems for underwriting, claims processing, and compliance reporting. The manual review process was slow, error-prone, and could not keep up with growing document volumes.

The challenge was compounded by the variety of document formats. Some were born-digital PDFs with clean text. Others were scanned images of handwritten or typewritten forms. Some came from standardised templates while others were free-form correspondence. The existing process required experienced staff who could read each document, identify the relevant data points, and manually enter them into the company's systems.

The company had a strong existing investment in AWS infrastructure and needed any solution to integrate seamlessly with their cloud environment. They also needed the system to work within their established compliance workflows, not replace them. The goal was to accelerate document processing while maintaining the accuracy and auditability that insurance regulation demands.

The Solution

BetterBrain built a document processing pipeline on AWS, leveraging SageMaker for model deployment and management. The pipeline starts with OCR to digitise scanned documents, producing clean text from even poor-quality scans. NLP models then handle entity extraction, pulling out policy numbers, dates, coverage amounts, named parties, and other structured fields from unstructured text.

Classification models determine each document's type, whether it is a new policy application, an endorsement, a claims form, or regulatory correspondence, and route it to the appropriate processing workflow. Custom data processing pipelines transform the extracted data into the formats required by the company's downstream systems.

The entire system integrates with the company's existing compliance workflows. Extracted data is flagged for human review at configurable confidence thresholds, ensuring that low-confidence extractions do not slip through unchecked. The architecture is designed to scale horizontally on AWS, handling volume spikes during renewal seasons or after catastrophic events without degradation in processing speed or accuracy.

The Number

• 80% faster document processing across all document types
• Significant reduction in manual review hours
• Scalable architecture on AWS handles volume spikes gracefully
• Seamless integration with existing compliance workflows
• Foundation for broader document automation across the enterprise