Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network
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’ a...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/3894 |
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| author | Boyu Liu Longrui Wu Shengdong Mu |
| author_facet | Boyu Liu Longrui Wu Shengdong Mu |
| author_sort | Boyu Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b779a71ae734445b8223403c99f61bed |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b779a71ae734445b8223403c99f61bed2025-08-20T02:43:49ZengMDPI AGMathematics2227-73902024-12-011224389410.3390/math12243894Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype NetworkBoyu Liu0Longrui Wu1Shengdong Mu2School of Innovation and Entrepreneurship, Hubei University of Economics, Wuhan 430205, ChinaSchool of Computer Science and Technology, Tongji University, Shanghai 201804, ChinaCollaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, ChinaThe 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.https://www.mdpi.com/2227-7390/12/24/3894small sampleboundary-enhanced prototype networkcredit cardfraud recognition |
| spellingShingle | Boyu Liu Longrui Wu Shengdong Mu Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network Mathematics small sample boundary-enhanced prototype network credit card fraud recognition |
| title | Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network |
| title_full | Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network |
| title_fullStr | Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network |
| title_full_unstemmed | Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network |
| title_short | Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network |
| title_sort | research on small sample credit card fraud identification based on temporal attention boundary enhanced prototype network |
| topic | small sample boundary-enhanced prototype network credit card fraud recognition |
| url | https://www.mdpi.com/2227-7390/12/24/3894 |
| work_keys_str_mv | AT boyuliu researchonsmallsamplecreditcardfraudidentificationbasedontemporalattentionboundaryenhancedprototypenetwork AT longruiwu researchonsmallsamplecreditcardfraudidentificationbasedontemporalattentionboundaryenhancedprototypenetwork AT shengdongmu researchonsmallsamplecreditcardfraudidentificationbasedontemporalattentionboundaryenhancedprototypenetwork |