Sarcouncil Journal of Economics and Business Management

Abstract: Suspicious Activity Reports (SARs) serve as a fundamental tool in identifying and addressing financial crimes such as money laundering, fraud, and terrorist financing within U.S. financial institutions. As globalization and transaction complexity increase, traditional methods often fall short in adapting to evolving fraud patterns, leading to substantial financial losses for institutions and customers, while also eroding consumer trust. This paper explores how Artificial Intelligence (AI), particularly machine learning and natural language processing, enhances SAR effectiveness by improving detection accuracy, enabling real-time monitoring, and automating narrative generation. Under the regulatory framework of the Bank Secrecy Act (BSA), AI technologies empower institutions to meet compliance standards while reducing operational costs and false positives. However, challenges such as model explainability, data quality constraints, algorithmic bias, and the persistence of false-positive alerts highlight the limitations of AI-driven systems and the need for robust governance and human oversight. Case studies from JPMorgan Chase, HSBC, and Bank of America illustrate both the transformative potential and practical constraints of AI in fraud detection, SAR quality, and customer experience. The findings underscore AI’s role in revolutionizing financial compliance while emphasizing the importance of balancing innovation with transparency, accountability, and regulatory alignment.