Robotic Process Automation System For Insurance Claims

Challenge

Our client is a large insurance company processing thousands of claims every month. Each claim includes multiple supporting documents — accident reports, repair estimates, medical certificates, and customer statements. Traditionally, these documents had to be reviewed manually by claims officers, significantly slowing down claim resolution and increasing operational costs.

The company needed an intelligent system to automate data extraction and validation across various document types — PDFs, scanned images, and structured forms — while fully complying with strict data privacy and security regulations such as GDPR and HIPAA-like standards.

Solution

We developed an AI-powered data extraction application that automatically processes insurance claims, extracts key information, validates it against internal rules, and routes structured data into the client’s existing claim management system.

The system is designed to handle diverse document layouts and languages, achieving high accuracy through a combination of OCR, NLP, and retrieval-augmented validation.

Multi-Format Document Ingestion

The application supports a wide range of input formats — including PDFs, scanned images, and digital claim forms.

Uploaded documents are automatically classified and preprocessed before extraction begins.

AI-Based OCR + NLP Pipeline

To ensure robust text recognition, the app combines Google Document AI and Azure Document Intelligence, both optimized for printed and handwritten text.

The extracted text is then processed by GPT-4o and Gemini 2.5 Pro, enabling semantic understanding and context-aware entity recognition. The models are fine-tuned to detect insurance-specific terminology such as claim numbers, policy IDs, accident types, repair categories, and medical condition descriptions.

RAG-Powered Data Validation

Extracted data is automatically cross-checked against an internal knowledge base of insurance codes, rules, and historical claims.

This layer is powered by Retrieval-Augmented Generation (RAG), using PostgreSQL + pgvector for semantic similarity search. The system verifies data consistency, flags anomalies, and ensures compliance before the information is approved for export.

Secure Data Export

Validated data is structured in JSON or CSV and transmitted through REST APIs directly into the client’s claim management platform. All Personally Identifiable Information (PII) is masked during preprocessing, and the system enforces end-to-end encryption for data at rest and in transit.

Technology Highlights

• Backend: Python (FastAPI) microservices for modular orchestration of extraction and validation.
• Vector Search: PostgreSQL + pgvector for embedding-based comparison with rules and historical cases.
• LLM Integration: GPT-4o for semantic extraction; Gemini 2.5 Pro for QA and validation flows.
• Deployment: Flexible deployment options — Azure, Google Cloud, or on-premises infrastructure.
• Monitoring: Logging, error detection, and feedback loops for continuous model retraining and accuracy improvement.

Results

The AI-powered extraction app transformed the company’s claims handling workflow:

• 70% faster processing time — reducing claim assessment from days to hours.
• Lower operational costs due to reduced manual document review.
• Higher accuracy and consistency in claim validation and data entry.
• Scalable architecture that adapts easily to new document formats and regulations.

Next Steps

Following the success of the implementation, the client is expanding the solution to include:

• Fraud detection, by cross-referencing claims with historical and third-party databases.
• Customer-facing portal, allowing policyholders to upload documents and receive instant pre-check feedback.

The result is an enterprise-grade, AI-driven extraction platform that combines OCR precision, LLM intelligence, and RAG validation — helping insurers process claims faster, more accurately, and with full regulatory compliance.