The Use of Artificial Intelligence in Combating Financial Crimes and Money Laundering in International Trade A Data-Driven Analysis (2010–2024)

This research paper evaluates the transformative impact of artificial intelligence (AI) in combating financial crimes and money laundering within international trade from 2010 to 2024. The primary objectives are to assess the development and effectiveness of AI-driven algorithms in detecting illicit transactions, analyze the role of machine learning in real-time monitoring and predictive analytics, and investigate regulatory and ethical challenges that constrain AI’s full potential in financial crime prevention. Employing a mixed-methods approach, the study integrates qualitative insights from case studies of major financial institutions and multinational corporations with quantitative analyses of AI adoption metrics, detection rates, and financial crime trends, drawing on data sourced from leading regulatory bodies such as the Financial Action Task Force, World Bank, and International Monetary Fund. Key findings indicate that AI, particularly through machine learning and predictive analytics, has significantly enhanced the accuracy and efficiency of anti-money laundering (AML) frameworks, reducing false positives and improving real-time detection of suspicious activities. Notable improvements include a 20% reduction in false positives at HSBC, a 25% increase in illicit activity detection at JPMorgan Chase, and substantial fraud loss reduction at PayPal. However, persistent challenges such as regulatory fragmentation, data privacy concerns, ethical dilemmas, and the adaptive tactics of financial criminals continue to hinder optimal AI deployment. The study underscores the need for strengthened regulatory harmonization, robust data governance, and continuous innovation in AI-driven compliance solutions. It recommends fostering cross-border collaboration and updating AI systems to counter evolving financial crime methodologies.