An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business
The reasonable credit scoring model must have strong default identification ability, which means the credit scoring can effectively distinguish between defaulting and nondefaulting customers. The premise to determine the credit score of small enterprises is to determine the weight of indicators. Thi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/1551937 |
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| _version_ | 1832560853003534336 |
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| author | Xuepeng Bai Zhichong Zhao |
| author_facet | Xuepeng Bai Zhichong Zhao |
| author_sort | Xuepeng Bai |
| collection | DOAJ |
| description | The reasonable credit scoring model must have strong default identification ability, which means the credit scoring can effectively distinguish between defaulting and nondefaulting customers. The premise to determine the credit score of small enterprises is to determine the weight of indicators. This paper studies 3,045 Chinese small business loans, and two novel weighting methods “Wilks’ Lambda method” and “AUC value method” are proposed, The greater the weight they meet, the greater the ability of default identification. The five weighting methods of “Wilks’ lambda method,” “AUC value method,” “G1 method,” “entropy method,” and “mean square variance method” are compared. An important contribution of the paper is to discover that Wilks’ Lambda method is the most effective method for small business. |
| format | Article |
| id | doaj-art-13e00f9c5050497ba7461eff8715d565 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-13e00f9c5050497ba7461eff8715d5652025-02-03T01:26:34ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/1551937An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small BusinessXuepeng Bai0Zhichong Zhao1School of Management Science and EngineeringSchool of Management Science and EngineeringThe reasonable credit scoring model must have strong default identification ability, which means the credit scoring can effectively distinguish between defaulting and nondefaulting customers. The premise to determine the credit score of small enterprises is to determine the weight of indicators. This paper studies 3,045 Chinese small business loans, and two novel weighting methods “Wilks’ Lambda method” and “AUC value method” are proposed, The greater the weight they meet, the greater the ability of default identification. The five weighting methods of “Wilks’ lambda method,” “AUC value method,” “G1 method,” “entropy method,” and “mean square variance method” are compared. An important contribution of the paper is to discover that Wilks’ Lambda method is the most effective method for small business.http://dx.doi.org/10.1155/2022/1551937 |
| spellingShingle | Xuepeng Bai Zhichong Zhao An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business Discrete Dynamics in Nature and Society |
| title | An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business |
| title_full | An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business |
| title_fullStr | An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business |
| title_full_unstemmed | An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business |
| title_short | An Optimal Credit Scoring Model Based on the Maximum Default Identification Ability for Chinese Small Business |
| title_sort | optimal credit scoring model based on the maximum default identification ability for chinese small business |
| url | http://dx.doi.org/10.1155/2022/1551937 |
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