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Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment
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 bui...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Hindawi
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526570/ https://www.ncbi.nlm.nih.gov/pubmed/36193407 http://dx.doi.org/10.1155/2022/4613088 |
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author | Wang, Shaohuang |
author_facet | Wang, Shaohuang |
author_sort | Wang, Shaohuang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9526570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95265702022-10-02 Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment Wang, Shaohuang J Environ Public Health Research Article 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. Hindawi 2022-09-22 /pmc/articles/PMC9526570/ /pubmed/36193407 http://dx.doi.org/10.1155/2022/4613088 Text en Copyright © 2022 Shaohuang Wang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Shaohuang Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title | Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title_full | Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title_fullStr | Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title_full_unstemmed | Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title_short | Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment |
title_sort | green financial health risk early monitoring of commercial banks based on neural network model in a small sample environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9526570/ https://www.ncbi.nlm.nih.gov/pubmed/36193407 http://dx.doi.org/10.1155/2022/4613088 |
work_keys_str_mv | AT wangshaohuang greenfinancialhealthriskearlymonitoringofcommercialbanksbasedonneuralnetworkmodelinasmallsampleenvironment |