Explainable Outlier Detection Systems for Fraudulent Financial Activity in Health Insurance Pipelines
Keywords:
explainable AI, outlier detection, health insurance fraud, anomaly detection, graph machine learning, attention mechanisms, financial risk analytics, claims auditing, provider abuseAbstract
Digital health insurance pipelines include complicated financial transaction ecosystems with fraud, provider abuse, and billing errors. High-stakes insurance situations need accuracy and interpretability, but rule-based and black-box machine learning fraud detection technologies are too rigid. This project develops and implements explainable outlier detection algorithms for illegal health insurance financial behavior. Graph-based machine learning models, attention mechanisms, and representation learning capture claims, provider, beneficiary, and billing entity linkages in XAI-compatible anomaly detection systems. Auditors, actuaries, and compliance experts believe algorithmic fraud warnings after humanizing anomaly scores. Attention weights, graph centrality assessments, and counterfactual explanations improve regulatory compliance and decision responsibility, according to this study. Conceptual modeling and system-level analysis of outliers may increase health insurance ecosystem fraud detection accuracy and transparency, allowing sustainable, trustworthy, and scalable financial risk management.
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