Abstract

The integration of predictive analytics into U.S. healthcare systems has introduced new opportunities and threats, particularly in the realms of fraud detection, resource allocation, and cybersecurity. Despite the proliferation of machine learning (ML) models in these domains, a persistent gap remains: the lack of transparency and interpretability in deployed algorithms has undermined stakeholder trust, regulatory compliance, and system resilience. This paper addresses this gap by proposing a conceptual framework for integrating explainable AI (XAI) techniques-specifically SHAP, LIME, and counterfactual explanations-into predictive modeling pipelines for fraud detection. Drawing from institutional theory and algorithmic accountability literature, we argue that explainability is not a technical afterthought but a socio-technical imperative in high-stakes domains such as healthcare. We synthesize insights from recent literature on financial information security (Mani, 2024), critical infrastructure protection through ML (Hasan et al., 2022), healthcare supply-chain resilience (Rasel et al., 2022), and predictive security analytics for digital health infrastructure (Hasan & Singh, 2023), highlighting their implications for healthcare fraud analytics. The proposed framework emphasizes modular explainability, adversarial robustness, and regulatory alignment as foundational principles. Our contributions include a novel theoretical framework for explainable analytics in healthcare, an integrated methodology bridging technical and organizational requirements, and implications for deploying trustworthy AI in healthcare operations. (Rudin, 2019; Mani et al., 2025).