Abstract: This paper explores the transformative role of Artificial Intelligence (AI) in enhancing anti-money laundering (AML) frameworks, with a particular focus on real-time transaction monitoring within the U.S. financial system. Traditional AML systems, often limited by static rule-based mechanisms and high false-positive rates, are increasingly ineffective in addressing the complexity of modern financial crimes. AI offers scalable, efficient, and adaptive solutions that can detect suspicious activity in real time, reduce operational costs, and improve compliance accuracy. The study examines the evolution of AML regulations, the integration of AI technologies such as machine learning and natural language processing, and the strategic benefits and challenges faced by financial institutions. It also highlights critical considerations around data quality, ethical governance, and regulatory alignment. Key recommendations include investment in AI infrastructure, regulatory collaboration, and workforce development. Finally, the paper identifies future research areas including quantum computing, federated learning, and cross-border AML cooperation. By leveraging AI responsibly, financial institutions can strengthen their resilience and maintain integrity in an increasingly complex global financial landscape.
Keywords: Artificial Intelligence, Anti-Money Laundering, Transaction Monitoring, Financial Compliance and Regulatory Technology.
Title: Enhancing Real-Time Transaction Monitoring through Al- Driving AML Frameworks for U.S financial system
Author: Adedayo Idowu Sunday, Agama Omachi, Kehinde Daniel Abiodun, Shereef Olayinka Jinadu, Esther Alaka
International Journal of Interdisciplinary Research and Innovations
ISSN 2348-1218 (print), ISSN 2348-1226 (online)
Vol. 13, Issue 3, July 2025 - September 2025
Page No: 32-46
Research Publish Journals
Website: www.researchpublish.com
Published Date: 14-July-2025
