Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network

Boyu Liu, Longrui Wu () and Shengdong Mu
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Boyu Liu: School of Innovation and Entrepreneurship, Hubei University of Economics, Wuhan 430205, China
Longrui Wu: School of Computer Science and Technology, Tongji University, Shanghai 201804, China
Shengdong Mu: Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, China

Mathematics, 2024, vol. 12, issue 24, 1-16

Abstract: The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought about a serious loss of financial institutions’ ability to do business. In small sample data environments, traditional fraud detection methods based on prototype network models struggle with the loss of time-series features and the challenge of identifying the uncorrected sample distribution in the metric space. In this paper, we propose a credit card fraud detection method called the Time-Series Attention-Boundary-Enhanced Prototype Network (TABEP), which strengthens the temporal feature dependency between channels by incorporating a time-series attention module to achieve channel temporal fusion feature acquisition. Additionally, nearest-neighbor boundary loss is introduced after the computation of the prototype-like network model to adjust the overall distribution of features in the metric space and to clarify the representation boundaries of the prototype-like model. Experimental results show that the TABEP model achieves higher accuracy in credit card fraud detection compared to five existing baseline prototype network methods, better fits the overall data distribution, and significantly improves fraud detection performance. This study highlights the effectiveness of open innovation methods in addressing complex financial security problems, which is of great significance for promoting technological advancement in the field of credit card security.

Keywords: small sample; boundary-enhanced prototype network; credit card; fraud recognition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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