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@article{188209,
        author = {Dr M K Jayanthi Kannan and Akanksha Dhote},
        title = {MedSureAI: Transforming Health Insurance with Real-Time Predictive Analytics and Autonomous Decision Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1183-1197},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188209},
        abstract = {Health insurance fraud poses a major financial and operational challenge for insurers due to high claim volumes, manual verification, and sophisticated fraudulent patterns. This research proposes an integrated, AI-driven fraud detection system combining feature engineering, class imbalance correction, ensemble machine learning, and real-time decision automation. The methodology includes provider-level data aggregation from multiple claim sources, preprocessing using scalable pipelines, and training using Random Forest and XGBoost classifiers enhanced with SMOTE oversampling. Extensive evaluation using ROC–AUC, confusion matrices, and precision–recall analysis demonstrates strong predictive capability. A secure Streamlit-based dashboard is deployed to allow real-time prediction and automated triage of suspicious providers. The results demonstrate that the proposed system can effectively identify high-risk claims and significantly improve the efficiency of fraud detection in health insurance workflows.},
        keywords = {Insurance, fraud detection, machine learning, Random Forest, XGBoost, SMOTE, Real-Time Decision Support, Claim Prediction, AI in Healthcare, Automated Risk Assessment, MedSureAI, Transforming Health Insurance.},
        month = {December},
        }