Automated Insurance Claims Processing Through
AI-Powered Document Intelligence

The insurer’s claims process required employees to manually review documents submitted by policyholders, extract relevant information, verify coverage, and route claims to the appropriate processing workflows.

The volume and variety of documentation made the process difficult to scale and created long processing times, repeated verification of policy and claimant information, manual data-entry errors, delays caused by incomplete or inconsistent documentation, limited visibility into individual claims, and a high operational workload for claims adjusters.

The challenge was to automate high-volume, standardized processing while preserving appropriate human oversight for exceptions and sensitive claims decisions.

Rootstack implemented an AI-powered claims document processing pipeline covering the complete flow from document submission to structured claims data and downstream processing: document intake → classification → extraction → policy validation → business rules → claim routing → downstream systems.

The platform automatically received claim-related documentation through the insurer’s existing digital channels and associated files with the corresponding claims. It supported different document types, including claim forms, medical or repair invoices, receipts, proof of loss, identification documents, photographs, supporting evidence, and policy-related documentation.

An AI-based classification engine identified the type and purpose of each document using its content and available context. OCR digitized information from scanned and image-based documents, while an LLM interpreted the extracted content and identified relevant claims information such as claim and policy numbers, claimant details, dates, invoice numbers, service descriptions, amounts, deductibles, and provider information.

The extracted data was then compared against information retrieved from the insurer’s policy administration system. Validation rules checked policy status, coverage type and dates, claimant information, applicable deductibles, required documentation, and claim-specific business rules. A dedicated rules layer separated deterministic business decisions from AI-based document interpretation, providing greater control over critical claims validations.

Rootstack used n8n to orchestrate document ingestion, classification, OCR processing, LLM extraction, policy data retrieval, validation, exception handling, claims routing, and data publication. Based on configured rules, claims could be routed to straight-through processing, standard adjuster review, specialized teams, manual investigation, or exception handling.

The solution also implemented a human-in-the-loop model. Documents with low extraction confidence, inconsistent policy information, missing documentation, or other exceptions were automatically routed to claims specialists, allowing AI to handle standardized high-volume processing while humans focused on complex cases.

Integration with policy administration systems, claims management platforms, CRM systems, document repositories, and downstream workflow applications was achieved through REST APIs, allowing structured claims information to move automatically between the document-processing pipeline and the systems used by claims teams.

Implemented over 7 months through incremental deployment by claim type, the solution provided 24/7 availability, achieved an estimated 65% reduction in initial claims processing time, and reduced manual data entry by an estimated 75%. More than 90% of claims were automatically classified, administrative workload was reduced by an estimated 50%, and 70–80% of claims were routed automatically. The solution also automated the detection of incomplete documentation, improving the consistency and efficiency of the claims intake process.