A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks
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| Udgivet i: | Mathematics vol. 13, no. 5 (2025), p. 819 |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3176335893 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13050819 |2 doi | |
| 035 | |a 3176335893 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Wu, Yanxi |u School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; <email>wuyx22@lzu.edu.cn</email> | |
| 245 | 1 | |a A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Regression analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Brain research | ||
| 653 | |a Credit card fraud | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Support vector machines | ||
| 653 | |a International finance | ||
| 653 | |a Time series | ||
| 653 | |a Credit card processing | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Wang, Liping |u Wuhan Maritime Communication Research Institute, Wuhan 430079, China; <email>wlp722@126.com</email> | |
| 700 | 1 | |a Li, Hongyu |u Henan Costar Group Co., Ltd., Nanyang 473000, China; <email>lihongyu202801@126.com</email> | |
| 700 | 1 | |a Liu, Jizhao |u School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; <email>wuyx22@lzu.edu.cn</email>; National-Local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Hefei 230601, China | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 5 (2025), p. 819 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176335893/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176335893/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176335893/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |