Cardiff University, Cardiff CF10 3AT, UK.
J Environ Public Health. 2022 Sep 22;2022:4613088. doi: 10.1155/2022/4613088. eCollection 2022.
Financial innovations emerge in an endless stream, and it is difficult for the regulatory measures and efforts of banks in various countries and the credit risk management level of commercial banks themselves to adapt to the increasingly complex risk environment faced by banks. In the process of building GFR (green financial risk) mixed governance model, the division of powers and responsibilities of governance subjects should be effectively defined. Therefore, it is very necessary to comprehensively and systematically study and grasp the characteristics, performance, and causes of commercial banks' GFR and build an early-warning model of commercial banks' GFR to comprehensively monitor the risks of banks, so as to reduce risks and avoid crises. Therefore, this paper uses the forward three-layer BPNN (BP neural network) technology to establish a real-time warning model of commercial banks' GFR. IL (input layer) to HL (hidden layer) adopts Sigmoid function, while HL to OL (output layer) function adopts linear function Purelin function. The results show that the test result of this method is greatly improved compared with the traditional method, and the correct rate is increased from 81.27% to 94.38%. It shows that the model in this paper has achieved a good warning effect of GFR for commercial banks.
金融创新层出不穷，各国银行的监管措施和努力以及商业银行自身的信用风险管理水平难以适应银行日益复杂的风险环境。在建立绿色金融风险（GFR）混合治理模式的过程中，应有效界定治理主体的权力和责任分工。因此，全面、系统地研究和掌握商业银行 GFR 的特点、表现及其成因，构建商业银行 GFR 预警模型，对银行风险进行全面监测，降低风险，避免危机，是非常必要的。为此，本文采用前向三层 BP 神经网络（BP 神经网络）技术，建立商业银行 GFR 的实时预警模型。IL（输入层）到 HL（隐藏层）采用 Sigmoid 函数，而 HL 到 OL（输出层）函数采用线性函数 Purelin 函数。结果表明，与传统方法相比，该方法的测试结果有了很大的提高，准确率从 81.27%提高到 94.38%。这表明本文中的模型对商业银行的 GFR 实现了良好的预警效果。
