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Credit Risk Assessment
- Citation Author(s):
-
DongqiYang
Nanjing UniversityBinqingXiaoNanjing University - Submitted by:
- Dongqi Yang
- Last updated:
- Sun, 06/23/2024 - 03:55
- DOI:
- 10.21227/n048-7q52
- Data Format:
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Abstract
This study utilizes the annual loan ledger data obtained from a commercial bank located in Jiangsu Province, China, which is called ChinaZJB. The ChinaZJB dataset consists of 1,329 valid samples of SMEs after merging the non-financial behavioral information and soft information on credit rating with the financial information, loan information, and non-financial basic information found in the annual loan ledger data. Among them, 108 SMEs have default records, while 1,221 SMEs have no default records, resulting in an imbalanced ratio of approximately 1:11.To check the robustness of the proposed model, five datasets from the UC Irvine (UCI) machine-learning repository, that is, the Polish 1, Polish 2, Polish 3, Australian, and Taiwan credit datasets, were used for robustness checks in this study.
This study utilizes the annual loan ledger data obtained from a commercial bank located in Jiangsu Province, China, which is called ChinaZJB. The ChinaZJB dataset consists of 1,329 valid samples of SMEs after merging the non-financial behavioral information and soft information on credit rating with the financial information, loan information, and non-financial basic information found in the annual loan ledger data. Among them, 108 SMEs have default records, while 1,221 SMEs have no default records, resulting in an imbalanced ratio of approximately 1:11.
To check the robustness of the proposed model, five datasets from the UC Irvine (UCI) machine-learning repository, that is, the Polish 1, Polish 2, Polish 3, Australian, and Taiwan credit datasets, were used for robustness checks in this study.
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experimental purpose
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experimental purpose
experimental purpose