A Hybrid, Privacy-Preserving AI Framework for 340B Program Fraud Detection: Moving Beyond Rule-Based Audits with Federated Learning and Graph Neural Networks

Abstract

The 340B Drug Pricing Program, critical for supporting safety-net healthcare providers, faces significant challenges from fraud, waste, and abuse, particularly through drug diversion and duplicate discounts. Current program integrity relies heavily on static, rule-based audits, which are increasingly ineffective against novel and sophisticated, collusive fraud schemes. This paper proposes a novel, multi-layered artificial intelligence (AI) framework designed to move beyond this reactive paradigm. The framework combines: (1) unsupervised transactional anomaly detection, utilizing Autoencoders and Isolation Forests to identify entity-level outliers; and (2) relational fraud detection, using an inductive Graph Neural Network (GNN), GraphSAGE, to model the complex relationships between covered entities, contract pharmacies, and providers to uncover collusive networks. Crucially, this hybrid model is designed within a privacy-preserving architecture. We propose the use of Federated Learning (FL) and Differential Privacy (DP) to train the anomaly detection modules collaboratively, allowing participating entities to build robust, global models without centralizing or exposing sensitive HIPAA-protected patient data. The framework integrates Explainable AI (XAI) methodologies (SHAP and GNNExplainer) for regulatory auditability and a concept drift detection component with a Human-in-the-Loop (HITL) active learning loop to ensure long-term adaptability to evolving fraud tactics. This approach provides a blueprint for a proactive, adaptive, and privacy-compliant system to safeguard 340B program integrity.