Explainable Risk Controls for Digital Health Payments: SHAP-Constrained Gradient Boosting with Policy-Based Access, Audit Trails, and Chargeback Mitigation

Abstract

The rapid expansion of digital health payments has introduced new opportunities for efficiency, accessibility, and innovation in healthcare financing. However, this evolution also brings heightened exposure to fraud, data misuse, and systemic vulnerabilities that can undermine trust in digital health ecosystems. Ensuring that risk controls are not only effective but also explainable is increasingly vital for fostering accountability and regulatory compliance. This study explores a framework that integrates explainable machine learning, particularly SHAP-constrained gradient boosting, with layered governance mechanisms such as policy-based access control, audit trails, and chargeback mitigation. The objective is to balance predictive accuracy with interpretability, providing healthcare providers, regulators, and financial intermediaries with transparent insights into payment risk patterns. By embedding explainability into fraud detection and transaction monitoring, stakeholders can enhance decision-making, ensure fairness, and strengthen patient and provider trust. Furthermore, the inclusion of auditability and traceability supports compliance with evolving data protection regulations, while policy-driven access management reduces insider threats. Chargeback mitigation mechanisms provide an additional safeguard for consumers and healthcare organizations, reducing financial losses and disputes. Together, these risk controls contribute to a secure, transparent, and resilient digital health payment infrastructure. The paper highlights the potential of explainable, policy-driven systems to redefine risk management in healthcare finance and to foster sustainable digital adoption.