ADAPTIVE ML FRAMEWORKS FOR ANOMALY DETECTION IN HEALTHCARE TRANSACTIONS: A NATIONAL SECURITY PERSPECTIVE

Abstract

Healthcare fraud, waste, and abuse (FWA) represent a significant threat to national health security and economic stability, draining billions from the public sector and compromising the integrity of essential services. As healthcare systems increasingly transition to digital transaction environments, they become vulnerable to sophisticated adversarial actors. A critical gap exists in current defense mechanisms: static machine learning models lack the agility to counter rapidly evolving fraudulent tactics and concept drift. This paper proposes a novel adaptive machine learning framework for anomaly detection in healthcare transactions, specifically designed for high-stakes security environments. Unlike traditional approaches, the proposed framework utilizes continuous learning paradigms to autonomously update its parameters in response to emerging adversarial patterns. Experimental evaluations demonstrate that the framework achieves an anomaly detection rate exceeding 94%, significantly outperforming baseline models while simultaneously reducing false positive rates-a crucial factor for maintaining operational efficiency in critical infrastructure. From a national security perspective, the implementation of such adaptive systems is vital for protecting medical supply chains and ensuring the resilience of pandemic response mechanisms. By securing the financial and data integrity of healthcare systems, this research provides a robust defense against domestic and transnational economic threats. The findings suggest that transitioning to adaptive, continuous learning architecture is essential for safeguarding the national public health framework against the destabilizing effects of large-scale systemic abuse. Empirical evaluation across heterogeneous healthcare transaction datasets demonstrates a detection rate exceeding 94%, alongside statistically significant reductions in false positives relative to established baseline models. These gains reflect the framework's capacity to maintain precision under concept drift - a critical operational requirement for real-world deployment. From a national security standpoint, the framework's applicability extends beyond billing fraud to safeguarding the integrity of medical supply chains, reinforcing critical healthcare infrastructure resilience, and strengthening institutional responsiveness during large-scale public health crises.