This study explores the landscape of fraud detection in the banking sector through a comprehensive bibliometric analysis. Using Bibliometrix and VOSviewer, an open-source tool for conducting bibliometric analysis, this study examined a corpus of scholarly articles from the Scopus database to identify key trends, influential publications, prominent authors, and leading research institutions in the field. The analysis focused on publication trends over time, citation patterns, and collaborative networks among researchers. Key findings highlight the predominance of machine learning and artificial intelligence techniques in recent fraud detection research, the significant contributions from researchers in China and India, and the growing importance of interdisciplinary collaboration. The study provides a detailed visualization of the intellectual structure of fraud detection research, offering insights into emerging trends and potential areas for future study. By mapping the existing body of knowledge, this study aims to guide researchers, practitioners, and policymakers in developing effective strategies to combat fraud in the banking sector.
Fajar et al. (Sat,) studied this question.