Abstract

Artificial Intelligence (AI) has emerged as a transformative tool in combating financial crime, enabling financial institutions and regulatory bodies to identify, monitor, and prevent fraudulent activities with greater efficiency. As financial crimes become increasingly sophisticated through digital payment systems, online banking platforms, and cross-border transactions, traditional detection mechanisms face significant limitations. This study examines the role of AI in preventing financial crime by evaluating its applications in fraud detection, anti-money laundering (AML), risk assessment, transaction monitoring, and predictive analytics. Drawing upon existing literature and industry practices, the research highlights how machine learning algorithms, anomaly detection systems, and real-time analytics improve the accuracy and speed of identifying suspicious financial activities while reducing false positives. The study also discusses challenges associated with AI adoption, including data privacy concerns, regulatory compliance, algorithmic bias, and implementation costs. Findings suggest that AI can significantly strengthen financial security frameworks when combined with human expertise, effective governance, and robust regulatory oversight. The research contributes to the growing body of knowledge on secure financial technologies and digital payments by demonstrating the strategic value of AI-driven solutions in mitigating financial crime risks. Future directions include the integration of explainable AI, generative AI, and collaborative intelligence-sharing frameworks to enhance global financial crime prevention efforts.