Research on Commercial Bank Risk Early Warning Model Based on Dynamic Parameter Optimization Neural Network
Based on the background of big data, it is necessary to study the dynamic parameter optimization of the commercial bank risk model neural network. Several customer information attribute groups that have an impact on loan customer rating are selected, and the existing customer data are used to train...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/9754428 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Based on the background of big data, it is necessary to study the dynamic parameter optimization of the commercial bank risk model neural network. Several customer information attribute groups that have an impact on loan customer rating are selected, and the existing customer data are used to train the network model of the attribute group and customer default rate, so that it can predict the customer’s default rate according to the newly entered loan customer information and then predict whether the customer defaults. Based on a neural network model, this article constructs the credit risk early warning model of science and technology bank, makes an empirical test, and puts forward relevant countermeasures and suggestions to control the credit risk of bank. This article establishes a warning model of commercial banks by using a neural network. Taking the bank as an empirical sample, the constructed neural network model is used. Finally, the error of the model is small and the early warning results are satisfactory. The experimental results show that the proposed risk early warning model can accurately predict the customer default rate, so as to warn the defaulting customers. In the whole process, there are few human intervention factors and a high degree of intelligence, which reduces the operational risk. |
|---|---|
| ISSN: | 2314-4785 |