Artificial Intelligence Techniques for Financial Fraud Detection: A Bibliometric Analysis of Scientific Development and Future Directions

Abstract

This study examines the development and intellectual structure of artificial intelligence (AI)-based financial fraud detection through a bibliometric analysis of 2200 Scopus- and Web of Science-indexed journal articles published between 1992 and 2026. The literature has expanded rapidly, with 89.6% of publications appearing since 2018 and an annual growth rate of 29.4%. Research is concentrated in Computer Science and Engineering, whereas Business and Economics account for 9.2% of classified publications. Credit card and payment fraud constitutes the largest research domain (32.0%), followed by banking, loan, and transaction fraud (14.3%), insurance fraud (12.2%), and accounting and financial statement fraud (9.9%). Neural networks and deep learning, ensemble and boosting methods, and classical supervised classifiers are the dominant methodological families. The field has progressively shifted from traditional data-mining and classical machine-learning approaches toward deep learning, ensemble methods, graph neural networks, and explainable AI. Science mapping shows that the intellectual structure remains strongly centered on payment fraud, while accounting and insurance fraud form more distinct research fronts. Author productivity is highly dispersed, with 88.9% of authors contributing a single publication, while international collaboration varies considerably across leading countries. By integrating performance analysis, science mapping, fraud-domain classification, bibliometric laws, and publication-growth analysis, the study provides a comprehensive assessment of the field’s development, intellectual structure, and emerging research directions.