Artificial Intelligence and Machine Learning in Fraud Detection: A Comprehensive Bibliometric Mapping of Research Trends and Directions

Abstract

This study presents a bibliographic analysis of emerging trends in applying artificial intelligence (AI) and machine learning (ML) to the detection and prevention of financial fraud and provides insights for future research. Bibliographic analysis on fraud data analysis helps researchers gain insight on research trends, research impact, and classification. Bibliometric analysis on fraud data analytics is helpful to researchers in getting insights on research trends, research impact and classification. However, research on fraud data analytics using machine learning is limited. The main objective of this quantitative analysis is to explore emerging trends in fraud data analytics and machine learning (ML) for financial crime detection and prevention. Bibliometric data has been collected from the Scopus database. One thousand four hundred eighty-three documents from the SCOPUS database have been analysed using VOSviewer. The data analysis divulges a growing interest in leveraging these technologies to strengthen financial crime detection. Fraud data analytics, Artificial Intelligence and Machine Learning are vital in identifying complex criminal patterns, strengthening companies in preventive vigilance, and ensuring fraud elimination. The study portrays the need for vigorous frameworks for the legislature, real-time analytics systems and more powerful tools and calls for integrating governments, financial institutions, and technology providers to strengthen prevention strategies and tackle financial crimes more effectively.