Graph neural networks for financial fraud detection: a review

Abstract

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.

Graphical abstract

Keywords

financial fraud detection / graph neural networks / data mining

Dawei Cheng is an associate professor with the Department of Computer Science and Technology, Tongji University, China. Before that, he was a postdoctoral associate at MoE Key Laboratory of Artificial Intelligence, Department of Computer Science, Shanghai Jiao Tong University, China. He received the PhD degree in computer science from Shanghai Jiao Tong University, China. His research fields include graph learning, big data computing, data mining, and machine learning

Yao Zou is a master’s student majoring in computer science at Tongji University, China. Her research interests include graph machine learning in the financial field, graph fraud detection algorithms, and data mining

Sheng Xiang is a PhD candidate in the Australian Artificial Intelligence Institute, School of Computer Science, University of Technology Sydney (UTS), Australia. He received his BSc degree from Shanghai Jiao Tong University, China. His research interests include graph machine learning in finance, graph generative algorithms, bipartite graph processing, and dynamic graph analytics

Changjun Jiang received the PhD degree from the Institute of Automation, Chinese Academy of Sciences, China in 1995. He is currently the leader of the Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, China. He is an academician of Chinese Academy of Engineering, China and an IET Fellow and an Honorary Professor with Brunel University London, UK. He has been the recipient of one international prize and seven prizes in the field of science and technology

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2022YFB4501704), the National Natural Science Foundation of China (Grant No. 62102287), and the Shanghai Science and Technology Innovation Action Plan Project (Nos. 22YS1400600 and 22511100700).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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