AI- Driven Anti-Money Laundering Systems for Cybersecurity Resilience in U.S. Financial Infrastructure: A Framework for Real-Time Threat Detection, Regulatory Compliance and National Security

Abstract

The recent avalanche of digitalization of the financial industry in the United States has increased the complexity of cyber-enabled money laundering as well as its prevalence, which is a significant level of threat to national security and regulatory stability. More than 300 million dollars of money laundered can be detected only after the fact whereas traditional Anti-Money Laundering (AML) systems, in many cases, are based on the static rule-based framework, which fails to be responsive to threats in real-time. The paper outlines an AI-based system that will promote the resilience of cybersecurity, regulatory standards, and real-time adversarial detection in the American financial system. The framework is based on machine learning, natural language processing (NLP), and predictive analytics to combine anomaly detection, behavioral modeling, and automated compliance reporting to minimize false positives and enhance detect accuracy. The mixed-method design, which involves the use of expert interviews, institutional surveys, and a simulation, based on which the study is conducted, assesses the effectiveness of AI-enhanced AML systems in the context of major indicators of accuracy in detection, speed of response, and efficiency in compliance. Results indicate that AI-based AML systems enhance early-warning systems significantly, enhance inter-institutional intelligence disclosure, and create consistency with regulatory requirements such as the Bank Secrecy Act (BSA) and FinCEN regulations. Moreover, the AI-based approach improves national security by reducing the risks of illegal finance and online terrorism. The study highlights the strategic necessity of incorporating AI governance and transparency systems to guarantee accountability, minimize the bias of the algorithms, and maintain the trust of the population. In the end, it is a framework that can be used by policymakers, regulators, and financial institutions to strike a balance between innovation and compliance in the dynamic environment of digital finance.