AI-Driven Fraud Prevention in U.S. Payment Systems: Reducing Costs Across Wire, ACH, and Book Transfers

Fraud prevention and detection methods based on traditional rules are less effective due to rising false positives and long response times caused by the fast evolution of fraudsters’ strategies. This paper provides a comprehensive analysis of Artificial Intelligence (AI) techniques for fraud detection and prevention, including supervised and unsupervised learning, deep learning, reinforcement learning, and privacy-preserving federated learning, along with AI system architecture and real-time risk scoring and integration methods for existing financial systems, and applications in wire, ACH and book transfers. The AI-enhanced benefits on the operations and the fraud loss value of the challenges and case studies of JPMorgan Chase, The Clearing House RTP, and the Fed’s new FedNow service are presented. Regulatory and ethical challenges are discussed in terms of explanation, fairness, and compliance with the existing US financial regulations and legislations. The future of fraud monitoring in the US economy is foreseen as driven by trends, including generative AI, integration with blockchain technology, and multimodal analytics. Overall, AI is proposed as a revolutionary tool to make fraud prevention scalable, flexible, and cost-efficient.