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A new fusion neural network model and credit card fraud identification

Author

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  • Shan Jiang
  • Xiaofeng Liao
  • Yuming Feng
  • Zilin Gao
  • Babatunde Oluwaseun Onasanya

Abstract

Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affect fraud identification. A credit card fraud identification model based on neural networks was constructed, and in-depth discussions and research were conducted. First, the layers of neural networks were deepened to improve the prediction accuracy of the model; second, this paper increase the hidden layer width of the neural network to improve the prediction accuracy of the model. This article proposes a new fusion neural network model by combining deep neural networks and wide neural networks, and applies the model to credit card fraud identification. The characteristic of this model is that the accuracy of prediction and F1 score are relatively high. Finally, use the random gradient descent method to train the model. On the test set, the proposed method has an accuracy of 96.44% and an F1 value of 96.17%, demonstrating good fraud recognition performance. After comparison, the method proposed in this paper is superior to machine learning models, ensemble learning models, and deep learning models.

Suggested Citation

  • Shan Jiang & Xiaofeng Liao & Yuming Feng & Zilin Gao & Babatunde Oluwaseun Onasanya, 2024. "A new fusion neural network model and credit card fraud identification," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-21, October.
    • Handle: RePEc:plo:pone00:0311987
    DOI: 10.1371/journal.pone.0311987
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    1. Farha Akhter Munmun & Sumi Khatun, 2022. "A Hybrid Method: Hierarchical Agglomerative Clustering Algorithm with Classification Techniques for Effective Heart Disease Prediction," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 7(7), pages 56-60, July.
    2. Xiqun (Michael) Chen & Zhiheng Li & Li Li & Qixin Shi, 2014. "A Traffic Breakdown Model Based on Queueing Theory," Networks and Spatial Economics, Springer, vol. 14(3), pages 485-504, December.
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