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Predicting Business Risks of Commercial Banks Based on BP-GA Optimized Model

Author

Listed:
  • Qilun Li

    (University of New South Wales)

  • Zhaoyi Xu

    (Hunan University)

  • Xiaoqin Shen

    (Wuhan University)

  • Jiacheng Zhong

    (Rajamangala University of Technology Krungthep)

Abstract

To further explore the influence path of internet finance on the risk prevention and management of commercial banks, the backpropagation neural network optimization algorithm was used to predict the risk value and the change of the risk level of commercial banks under the background of internet environment was empirically studied and analyzed. The results showed that the maximum size of genetic algebra and the number of individuals significantly impacted the algorithm’s optimization performance when the genetic algorithm was used for parameter optimization. Through continuous attempts, the prediction effect was the best when the genetic algebra was 62, and the individual number was 45. The training network showed that the test set’s fitting degree was 96.07%, and the prediction error was 0.84%, which was much better than those before optimization. When the predicted risk value was more significant than 0.39, the bank should be vigilant and strengthen risk prevention. The development of internet finance can reduce commercial banks’ business risk levels, reduce their dependence on traditional business, and decrease commercial banks’ business risk levels. It can be seen that commercial banks can effectively improve risk management ability and efficiency promoted by technological development, so the level of business risk they undertake can be reduced.

Suggested Citation

  • Qilun Li & Zhaoyi Xu & Xiaoqin Shen & Jiacheng Zhong, 2022. "Predicting Business Risks of Commercial Banks Based on BP-GA Optimized Model," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1423-1441, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-020-10088-0
    DOI: 10.1007/s10614-020-10088-0
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    References listed on IDEAS

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    Cited by:

    1. Lirong Gan & Wei-han Liu, 2024. "Option Pricing Based on the Residual Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1327-1347, April.

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