Fraud Detection Research Trends: A Bibliometric Analysis

Abstract

This study examines the development and intellectual structure of fraud detection research through a bibliometric analysis. Using data extracted from a major scientific database and analyzed with bibliometric visualization tools, the study maps publication trends, influential contributors, and thematic evolution within the field. The findings reveal that fraud detection research is strongly centered on machine learning and increasingly shaped by advances in deep learning, neural networks, and data-driven approaches. At the same time, the field has expanded beyond traditional financial contexts into broader digital ecosystems, including cybersecurity, blockchain, and data privacy. The analysis also highlights a clear shift from conventional statistical methods toward more adaptive and complex models capable of handling large-scale and interconnected data. In addition, emerging themes such as predictive analytics, risk management, and decentralized finance indicate a growing orientation toward real-world application and decision-making. Overall, the study provides a comprehensive overview of the research landscape, identifies key trends and gaps, and offers directions for future research, particularly in integrating technological innovation with practical, ethical, and system-level considerations.