Smart Health Insurance Claim Guidance and Rejection Prediction System: An Explainable Approach to Preliminary Claim Assessment and Fraud-Risk Analysis

Abstract

Health insurance claim processing is difficult for both policyholders and administrative teams because a claim outcome depends on many interacting factors, such as policy validity, coverage limits, waiting periods, pre-existing conditions, clinical-code compatibility, documentation completeness, hospital-network status, billing accuracy and prior claim history. Policyholders frequently do not understand why a claim is rejected, while hospitals and insurers need a structured way to screen claims before detailed manual review. This paper presents a Smart Health Insurance Claim Guidance and Rejection Prediction System, a web-based application built with React, TypeScript, Vite, Node.js, Express and Tailwind CSS that offers preliminary, explainable claim guidance. A rule-based claim-evaluation engine examines policy, financial, clinical, documentation and fraud-related factors to generate an approval/rejection probability, a confidence score, feature-level explanations similar to SHAP attributions, probable rejection reasons, and fraud-risk flags. The system provides role-based workflows for patients, hospitals and administrators, along with policy-document analysis, claim history, bulk claim processing and model-analytics dashboards. Functional and scenario-based validation across twelve claim scenarios confirmed that the implemented decision logic behaves as designed. Since the prototype does not yet include a trained machine-learning model or a labelled real-world dataset, its performance indicators are treated as prototype dashboard values rather than validated experimental results. The system nevertheless demonstrates a practical, explainable and extensible approach to preliminary health-insurance claim guidance.