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.

Introduction

The Smart Health Insurance Claim Guidance and Rejection Prediction System is a web-based platform designed to simplify health insurance claim assessment and help users understand why a claim may be approved, rejected, or require further review.

Traditional claim processing requires checking many factors such as policy validity, coverage limits, waiting periods, pre-existing conditions, medical codes, hospital-network status, documentation, and billing accuracy. Manual evaluation can be time-consuming and often provides limited explanations to policyholders.

The proposed system addresses these issues through an automated, rule-based claim evaluation engine that combines policy, clinical, financial, documentation, and fraud-related checks. It provides:

• Role-based access for patients, hospitals, and administrators. insured, waiting periods, pre-existing conditions, ICD-10/CPT compatibility, documentation, hospital-network status, billing anomalies, and claim evaluation, policy analysis, fraud indicators, explainability, and role-based web workflows, it aims to reduce repetitive manual screening and give patients, hospitals, and administrators clearer information about potential claim issues. It is intended

• Automated claim evaluation based on policy and claim conditions.

• Approval/rejection probabilities with confidence information.

• SHAP-style explanations showing which factors influenced the prediction.

• Fraud and anomaly detection using clinical-code mismatches, billing abnormalities, and unusual claim frequency.

• Policy-document analysis for identifying coverage conditions, exclusions, waiting periods, co-payments, and sub-limits.

• Claim history, bulk uploads, dashboards, and administrative analytics.

Literature and Research Gap

Previous research has explored healthcare fraud detection, insurance cost prediction, suspicious-claim detection, explainable AI, policy summarization, compliance analysis, and claim-text analysis using machine learning, deep learning, NLP, and anomaly-detection techniques. However, these capabilities are generally developed as separate solutions.

The identified research gap is the lack of an integrated platform that combines policy validation, financial and clinical checks, documentation verification, fraud indicators, explainable claim prediction, and user-oriented guidance. The proposed system attempts to address this gap through a single multi-role platform.

System Architecture

The system follows a client-server architecture with three main modules:

1. Patient Module – claim submission, claim history, prediction results, policy viewing, and profile management.

2. Hospital Module – claim processing and bulk claim uploads.

3. Administrator Module – claim review, fraud detection, reporting, analytics, patient management, and system settings.

The frontend uses React and TypeScript, while the backend uses Express/Node.js and REST-style APIs. The prototype currently uses mock/in-memory data and browser storage, with the architecture allowing future integration with databases such as MySQL or PostgreSQL.

Methodology

The claim assessment follows a structured pipeline:

Claim and policy data → Policy validation → Clinical-code checking → Documentation checking → Network and billing verification → Rejection scoring → Explainability → Fraud analysis → Recommendation

The evaluation engine calculates a deterministic rejection score based on factors such as coverage, sum insured, waiting periods, pre-existing conditions, ICD-10/CPT compatibility, documentation, hospital-network status, billing anomalies, and previous claim frequency.

The resulting probabilities are interpreted as:

• Likely Approved: approval probability ≥ 72%

• Needs Review: intermediate probability

• Likely Rejected: approval probability ≤ 38%

The system also generates feature-level explanations, allowing users to understand which factors contributed toward approval or rejection.

Conclusion

The Smart Health Insurance Claim Guidance and Rejection Prediction System was developed to make preliminary health-insurance claim assessment more transparent and structured for patients, hospitals and administrators. By combining policy validation, financial and clinical checks, documentation review, fraud indicators and explainable, feature-level predictions within a single role-based platform, the system shows that claim data can be used for more than status tracking — it can help identify, and explain, why a claim may face difficulty. Functional and scenario-based validation confirmed that the implemented decision logic behaves as intended across twelve representative scenarios, providing a solid and extensible foundation for future machine-learning-based enhancement.

References

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Copyright

Copyright © 2026 Katakam Nithin, Banam Varshini Priyamvada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.