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A distributed deep neural network model for credit card fraud detection

Author

Listed:
  • Lei, Yu-Tian
  • Ma, Chao-Qun
  • Ren, Yi-Shuai
  • Chen, Xun-Qi
  • Narayan, Seema
  • Huynh, Anh Ngoc Quang

Abstract

This paper develops a distributed neural network model (DDNN) for detecting credit card fraud to federate credit card transaction data among different financial institutions. In addition, the convergence of the DDNN model is achieved by introducing a model optimization algorithm. The results demonstrate that (1) The use of a distributed model can avoid privacy leakage and data handling costs; (2) The DDNN model accelerates the convergence of the model through simultaneous computation of multiple clients; (3) The DDNN model detects credit card fraud better than multiple types of centralized models.

Suggested Citation

  • Lei, Yu-Tian & Ma, Chao-Qun & Ren, Yi-Shuai & Chen, Xun-Qi & Narayan, Seema & Huynh, Anh Ngoc Quang, 2023. "A distributed deep neural network model for credit card fraud detection," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323009194
    DOI: 10.1016/j.frl.2023.104547
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    References listed on IDEAS

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    1. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    2. Karpoff, Jonathan M., 2021. "The future of financial fraud," Journal of Corporate Finance, Elsevier, vol. 66(C).
    3. Spyridon Repousis & Petros Lois & Varvara Veli, 2019. "An investigation of the fraud risk and fraud scheme methods in Greek commercial banks," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 22(1), pages 53-61, January.
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