Medical Insurance Claim Audit System

This paper has presented the Medical Insurance Claim Audit System, a web-based application that automates the complete medical insurance claim verification lifecycle through an integrated pipeline of OCR-based document processing, policy-driven rule validation, multi-criteria fraud detection, three-factor authentication, and timestamped audit logging. The system addresses a clear gap in the existing literature and tooling landscape by providing a unified, interpretable, and locally deployable claim auditing solution that does not require large-scale datasets, ML training pipelines, or cloud infrastructure. The implemented system correctly processes all three targeted document types, applies dynamic policy rules loaded from a dataset, assigns quantitative fraud scores across five anomaly categories, and produces deterministic claim decisions with structured justifications. Unit testing with Pytest confirmed correct system behavior across 100 test cases with 87% overall code coverage. The system effectively demonstrates that the integration of proven technologies—OCR, rule-based engines, and relational databases—within a coherent modular architecture can deliver practical claim auditing capability suitable for small to medium insurance processing contexts. Future enhancements may include: integration of machine learning classifiers to extend fraud detection beyond predefined rules; adoption of advanced OCR APIs to improve extraction accuracy on low-quality documents; migration to scalable database systems such as MySQL or PostgreSQL for production deployment; cloud-based deployment to enable concurrent access and improved availability; mobile application development for enhanced accessibility; implementation of encryption and role-based access control for strengthened security; and integration with hospital and insurance provider databases for real-time cross-verification of submitted claims.

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