ABSTRACT:
Growing complexity of financial crimes such as money laundering and banking frauds has made the use of cutting-edge technological measures a necessity. Artificial Intelligence (AI) has become an imperative tool in combating these illegal acts by improving detection and prevention rates of financial institutions. This research delves into the use of AI in anti-financial crime, with specific reference to its use in transaction monitoring, anomaly detection, and regulatory compliance. The main reason for this research is the increasing inadequacy of conventional rule-based systems that are unable to keep pace with the changing modus operandi of money launderers. AI-based systems utilize machine learning, natural language processing, and deep learning to process huge amounts of financial data in real-time, detecting unusual patterns and minimizing false positives. The approach is based on an extensive literature review, case studies, and examination of AI-based anti-money laundering (AML) systems deployed in different financial institutions. Results show that AI increases fraud detection accuracy, enhances operational efficiency, and decreases compliance costs. Machine learning algorithms are capable of adjusting to emerging laundering methods, thus supporting a proactive response to preventing financial crime. Nevertheless, due to its benefits, the use of AI in AML also has to deal with some challenges, including data privacy issues, regulatory conformity, and algorithmic bias. Practical implications of the study identify AI capability in enhancing compliance procedures, simplifying fraud investigations, and minimizing risks for banks and financial regulators. Also, knowledge derived from AI-based AML solutions can inform policymakers in streamlining rules and promoting a safer financial system. Through presenting a comprehensive overview of AI effects on financial security, this research adds to the increasing discussion about AI implementation in banking and how it can counteract financial crime threats.