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The Risk Management of Commercial Banks¡ª¡ªCredit-Risk Assessment of Enterprises

Author

Listed:
  • Na Luo
  • Jiayi Yang
  • Yuanfeng Zhu
  • Yu Zhang

Abstract

With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises¡¯ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks¡¯ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.

Suggested Citation

  • Na Luo & Jiayi Yang & Yuanfeng Zhu & Yu Zhang, 2016. "The Risk Management of Commercial Banks¡ª¡ªCredit-Risk Assessment of Enterprises," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(9), pages 69-77, September.
    • Handle: RePEc:ibn:ijefaa:v:8:y:2016:i:9:p:69-77
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    References listed on IDEAS

    as
    1. Muriel Perez, 2006. "Artificial Neural Networks And Bankruptcy Forecasting : A State Of The Art," Post-Print halshs-00522129, HAL.
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    More about this item

    Keywords

    risk management; enterprise credit risk assessment; GRNN neural network; machine learning;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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