Azure Document Intelligence for Insurance Data Extraction

Insurance companies manage vast volumes of complex claims, policy forms, and invoices across multiple languages and file formats. Processing these documents manually causes massive administrative bottlenecks and increases error rates.

This project delivers an automated Intelligent Document Processing (IDP) pipeline built on Azure Document Intelligence and LLM Integration. The pipeline automatically reads scanned files, extracts structured key-value data, validates accuracy through a automated retry loop, and distributes jobs to Python desktop robots for downstream execution—reserving human intervention strictly for exceptional edge cases.

Key Features & Core Mechanics

Multi-Format OCR & Azure Document Intelligence Reads structured and unstructured insurance claim forms (e.g., CMS-1500, invoices, policy sheets) across multiple languages using Azure Computer Vision APIs.

LLM & Prompt Engineering Integration Connects Large Language Models to contextualize extracted text, parse complex table fields, and accurately structure unstructured claims data.

Smart Automated Validation & Retry Loop Cross-checks read data against defined rules. If initial validation fails, the system automatically triggers high-resolution re-scans or alternative OCR strategies before flagging for human review.

Orchestration via Azure Service Bus Queues successfully validated data through Azure Service Bus to distribute processing tasks seamlessly across Python automation scripts.

End-to-End UI & File Workflow Integration Features custom user interface hooks for manual exception handling and automated file generation workflows for CRM and ERP systems.

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Process & Architecture

Document Ingestion & Reading

Scanned PDFs, claim forms, and invoices are received and processed through Azure Computer Vision OCR.

Validation & Automated Retry

Extracted text is checked for completeness. If valid, it is routed immediately; if validation fails, higher-quality image reprocessing kicks in.

Queueing & Robot Execution

Clean data is pushed to Azure Service Bus, distributing tasks to Python desktop robots to complete downstream records creation, with human intervention only triggered for flagged exceptions.

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What This Project Achieved

By building an end-to-end intelligent document pipeline, we replaced manual paper processing with a fast, self-correcting system. The combination of Azure AI, LLM parsing, and automated retry logic means the system catches errors on its own before a human ever needs to intervene. Insurance teams can process claims in seconds rather than days, drastically cutting administrative overhead while keeping data accuracy at an enterprise standard.