Strengthening U.S. financial industry defenses against terrorism financing: A machine learning to anti-money laundering systems

Terrorism financing remains a severe threat to the stability of the U.S. financial system, necessitating continuous advancements in anti-money laundering (AML) strategies. This paper explores the integration of machine learning (ML) into AML systems to enhance the detection and prevention of illicit financial activities linked to terrorism financing. While ML-driven AML solutions offer improved accuracy and adaptability, they also present challenges, such as regulatory compliance, model explainability, adversarial attacks, and data privacy concerns. The paper examines the risks posed by adversarial ML tactics, where criminals manipulate transaction patterns to evade detection, and highlights the ethical concerns surrounding biased AML models that may disproportionately target specific demographics. Furthermore, the paper underscores the need for explainable AI (XAI) to ensure regulatory transparency and proposes the adoption of federated learning to enhance data privacy without compromising detection capabilities. Ultimately, this research advocates for a balanced approach that strengthens financial defenses against terrorism financing while ensuring compliance, fairness, and privacy protection in ML-driven AML systems.