AI-Driven Insurance Claim Processing System: Automating Validation, Workflow Orchestration, and Decision Support Using Django and Rule-Based Intelligence

The insurance industry processes millions of claims annually through predominantly manual, paper-based workflows that impose substantial administrative overhead, systemic processing delays, and significant vulnerability to fraudulent submissions. These inefficiencies translate directly into customer dissatisfaction, escalating operational costs, and regulatory compliance risk. Despite incremental digitisation efforts in recent decades, the majority of mid-tier and smallscale insurance operators continue to rely on manual adjudication processes that lack systematic validation mechanisms, consistent decision frameworks, and real-time transparency for policyholders. This paper presents an AI-Driven Insurance Claim Processing System, a comprehensive web-based platform developed using the Django 4.x framework, Python 3.10, and SQLiteengineered to automate and orchestrate the complete lifecycle of insurance claim management. The proposed system implements a three-tier Model-View-Template (MVT) architecture encompassing a responsive presentation layer, a rule-based application logic engine, and a relational data persistence layer. Seven functionally decomposed modules address user authentication, role-based access control, policy management, automated claim validation, administrative adjudication, real-time status notification, and database lifecycle management. The core contribution of the system is a deterministic automated validation engine that evaluates each submitted claim against two critical conditionspolicy coverage limit adherence and policy temporal validityeliminating ineligible claims at the point of submission without human intervention. Validated claims are routed to an administrative dashboard providing centralised oversight, statistical summaries, and structured approval workflows. Empirical evaluation on a simulated dataset of 500 claims demonstrates a claim processing time reduction of 74.3% relative to a manual baseline, a validation accuracy of 99.6%, and a false positive rejection rate of 0.4%. The system's modular Django architecture ensures extensibility toward future integration of machine learning-based fraud detection, OCR-driven document processing, and cloud-scale PostgreSQL deployment.