Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network
The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a com...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/6926216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849399034373996544 |
|---|---|
| author | Yufeng Mao Zongrun Wang Xing Li Chenggang Li Hanning Wang |
| author_facet | Yufeng Mao Zongrun Wang Xing Li Chenggang Li Hanning Wang |
| author_sort | Yufeng Mao |
| collection | DOAJ |
| description | The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments. |
| format | Article |
| id | doaj-art-9dc144d50c01416587f1cb29f08af3b4 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-9dc144d50c01416587f1cb29f08af3b42025-08-20T03:38:26ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2021-01-01202110.1155/2021/69262166926216Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural NetworkYufeng Mao0Zongrun Wang1Xing Li2Chenggang Li3Hanning Wang4Business School, Central South University, Changsha 410083, ChinaBusiness School, Central South University, Changsha 410083, ChinaSchool of Management, Nanchang University, Nanchang 330031, ChinaNew Structure Finance Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaSchool of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, ChinaThe low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments.http://dx.doi.org/10.1155/2021/6926216 |
| spellingShingle | Yufeng Mao Zongrun Wang Xing Li Chenggang Li Hanning Wang Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network Discrete Dynamics in Nature and Society |
| title | Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network |
| title_full | Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network |
| title_fullStr | Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network |
| title_full_unstemmed | Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network |
| title_short | Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network |
| title_sort | construction and application of the online finance credit risk rating model based on the artificial neural network |
| url | http://dx.doi.org/10.1155/2021/6926216 |
| work_keys_str_mv | AT yufengmao constructionandapplicationoftheonlinefinancecreditriskratingmodelbasedontheartificialneuralnetwork AT zongrunwang constructionandapplicationoftheonlinefinancecreditriskratingmodelbasedontheartificialneuralnetwork AT xingli constructionandapplicationoftheonlinefinancecreditriskratingmodelbasedontheartificialneuralnetwork AT chenggangli constructionandapplicationoftheonlinefinancecreditriskratingmodelbasedontheartificialneuralnetwork AT hanningwang constructionandapplicationoftheonlinefinancecreditriskratingmodelbasedontheartificialneuralnetwork |