Bibliometric Analysis on Money Laundering Fraud Detection Using Generative Artificial Intelligence Models

Money laundering remains a major challenge for financial institutions, particularly as traditional rule-based systems struggle to detect evolving fraud patterns. Recent advances in generative artificial intelligence (AI) offer promising directions, yet the extent and focus areas of existing research remain unclear. This study conducts a descriptive bibliometric analysis to map the research landscape connecting generative AI and anti-money laundering (AML) detection. A total of 748 peer-reviewed publications from Scopus and 54 from Web of Science were analyzed to identify key themes, leading contributors, co-Authorship structures, keyword cooccurrence patterns, and citation trends. Using VOSviewer, the study visualizes research clusters and highlights emerging areas such as deep learning, synthetic data generation, and graphbased fraud detection. The analysis reveals growing scholarly attention to AI-driven AML methods but limited coverage of real-Time and institution-specific applications. The findings clarify current research directions, expose gaps in the literature, and define opportunities where generative AI can further support AML innovation.