Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach

Chengru Ju; Xiaowen Ma; Boyang Dong

doi:10.70393/616a736d.323639

OPEN ACCESS |Research Article ||18 March 2025

Real-time Cross-border Payment Fraud Detection Using Temporal Graph Neural Networks: A Deep Learning Approach

Chengru Ju

Columbia University

jerryli4399@gmail.com

Public Administration, Columbia University, New York City, NY, USA.

Xiaowen Ma

University of Rochester

Master of Science in Marketing Analytics, University of Rochester, NY, USA.

Boyang Dong

University of Chicago

maxxxlee090@gmail.com

Master of Science in Financial Mathematics, University of Chicago, IL, USA.

* Corresponding Author¹: Chengru Ju, E-Mail: jerryli4399@gmail.com

Publication

Accepted 2025 March 10 ; Published 2025 March 18

Academic Journal of Sociology and Management, 2025, 3(2), 3005-5040.

Abstract

The rapid expansion of digital payments across borders has led to increased risks in the financial system, especially in the fraud process. Traditional methods show limitations in capturing the spatial-temporal patterns inherent in crossing borders. This paper presents a novel Temporal Graph Neural Network (TGNN) approach for real-time financial fraud detection. The proposed system includes a combination of physical-spatial features and a dynamic graph system designed to model structural changes. The architecture employs a multi-head attention mechanism optimized for cross-border payment characteristics, enabling efficient capture of temporal dependencies and spatial correlations in transaction networks. The experiments carried out on two large-scale real-world databases show the effectiveness of our method. The model achieved 99.24% accuracy on Dataset-A (2.8 million transactions) and 98.76% on Dataset-B (1.5 million transactions), outperforming existing methods. The framework maintains robust performance under varying transaction volumes while reducing false positive rates by 37% compared to baseline models. Real-world deployment validates the model's effectiveness in detecting sophisticated fraud patterns while maintaining low computational overhead. The plan shows significant improvements in both detection accuracy and efficiency, making it suitable for use in cross-border payments.

Keywords

Cross-border Payment Fraud , Temporal Graph Neural Networks , Deep Learning , Real-time Fraud Detection .

Metadata

DOI:

10.70393/616a736d.323639

ARK:

ark:/40704/AJSM.v3n2a01

Pages: 1-12

References: 32

Disciplines: Business

Subjects: Finance

Cite This Article

APA Style

Ju, C., Ma, X. & Dong, B. (2025). Real-time cross-border payment fraud detection using temporal graph neural networks: a deep learning approach. Academic Journal of Sociology and Management, 3(2), 1-12. https://doi.org/10.70393/616a736d.323639

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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