aceArtificial Intelligence Techniques for Financial Fraud Detection: A Bibliometric Analysis of Scientific Development and Future Directions
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…
A review of 2200 indexed articles shows AI fraud-detection research expanding quickly and moving toward deep learning, ensemble methods, graph neural networks and explainable AI.
Evidence
- Peer-reviewedJournal of Risk and Financial Management2026-10-16
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Truvace Impact Record TRV-2026-1757, v1: “Artificial Intelligence Techniques for Financial Fraud Detection: A Bibliometric Analysis of Scientific Development and Future Directions.” Truvace, 2026-10-17. /record/TRV-2026-1757 (accessed at citation time). sha256 87a86e4c73f77e4a…
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