Bibliometric Analysis of Fraud Detection in the Fintech Sector

Abstract

The rapid growth of financial technology (fintech) has transformed the delivery of financial services while simultaneously increasing exposure to various forms of digital fraud and cybersecurity threats. As fintech platforms continue to expand globally, fraud detection has emerged as a critical research area aimed at enhancing the security, reliability, and sustainability of digital financial ecosystems. This study conducts a bibliometric analysis to examine the intellectual structure, research trends, influential publications, and collaborative networks within the field of fraud detection in the fintech sector. Bibliographic data were collected from the Scopus database and analyzed using VOSviewer to visualize keyword co-occurrence, author collaboration, institutional collaboration, country collaboration, overlay visualization, and density mapping. The findings reveal that fintech serves as the central theme connecting major research areas such as fraud detection, artificial intelligence, machine learning, cybersecurity, blockchain, and risk management. Keyword analysis indicates that artificial intelligence and machine learning have become dominant technological approaches for detecting and preventing fraudulent activities in digital financial services. Overlay visualization demonstrates a shift from traditional concerns related to cybercrime, regulatory compliance, and financial risk management toward emerging topics such as blockchain security, financial inclusion, and AI-driven fraud detection systems. Citation analysis identifies studies on artificial intelligence, fintech security frameworks, and cybersecurity as the most influential contributions to the field. Furthermore, collaboration analysis shows that research activities are concentrated within a limited number of author groups, institutions, and countries, with India emerging as the leading contributor to international collaboration.