Green financial health risk early monitoring of commercial banks based on neural network model in a small sample environment

Wang, Shaohuang 2022. Green financial health risk early monitoring of commercial banks based on neural network model in a small sample environment. Journal of Environmental and Public Health 2022 , 4613088. 10.1155/2022/4613088

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Business (Including Economics)
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/
Publisher:	Hindawi
ISSN:	1687-9805
Date of First Compliant Deposit:	5 October 2022
Date of Acceptance:	6 September 2022
Last Modified:	05 May 2023 07:33
URI:	https://orca.cardiff.ac.uk/id/eprint/153090

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