Wang, Shaohuang
2022.
Green financial health risk early monitoring of commercial banks based on neural network model in a small sample environment [RETRACTED].
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: | Schools > Business (Including Economics) |
| Additional Information: | This article has been retracted by the publisher. Please see retraction notice at https://doi.org/10.1155/2023/9870646 for more details. |
| Publisher: | Hindawi |
| ISSN: | 1687-9805 |
| Related URLs: | |
| Date of First Compliant Deposit: | 5 October 2022 |
| Date of Acceptance: | 6 September 2022 |
| Last Modified: | 14 Jul 2026 12:42 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/153090 |
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