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A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks

Author

Listed:
  • Yanxi Wu

    (School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China)

  • Liping Wang

    (Wuhan Maritime Communication Research Institute, Wuhan 430079, China)

  • Hongyu Li

    (Henan Costar Group Co., Ltd., Nanyang 473000, China)

  • Jizhao Liu

    (School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
    National-Local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology, Hefei 230601, China)

Abstract

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.

Suggested Citation

  • Yanxi Wu & Liping Wang & Hongyu Li & Jizhao Liu, 2025. "A Deep Learning Method of Credit Card Fraud Detection Based on Continuous-Coupled Neural Networks," Mathematics, MDPI, vol. 13(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:819-:d:1602881
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    References listed on IDEAS

    as
    1. Katherine J. Barker & Jackie D'Amato & Paul Sheridon, 2008. "Credit card fraud: awareness and prevention," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 15(4), pages 398-410, October.
    2. Jay Raval & Pronaya Bhattacharya & Nilesh Kumar Jadav & Sudeep Tanwar & Gulshan Sharma & Pitshou N. Bokoro & Mitwalli Elmorsy & Amr Tolba & Maria Simona Raboaca, 2023. "RaKShA : A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions," Mathematics, MDPI, vol. 11(8), pages 1-27, April.
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