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Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises

Author

Listed:
  • Nana Chai
  • Baofeng Shi
  • Bin Meng
  • Yizhe Dong

Abstract

This paper aims to design a novel AFCM-SMOTENC-APRIORI model to mine the default feature attributes of small enterprises. It can overcome the problem that the data characteristics of “small defaulting small enterprises and large non-defaulting small enterprises†make it difficult to mine the defaulting feature attributes of existing small enterprises. We used 1,231 small enterprise credit data from a city commercial bank in China to make an empirical analysis. We found that 23 feature attributes are strongly associated with default and 87% of the association rules are the same between the extended data and the original data mining. It shows that the data mining results with SMOTE-NC are highly consistent with the results of the original data mining, and the model is robust and reliable. It can be used as a reference for the credit risk identification of small enterprises in commercial banks.

Suggested Citation

  • Nana Chai & Baofeng Shi & Bin Meng & Yizhe Dong, 2023. "Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises," SAGE Open, , vol. 13(2), pages 21582440231, April.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:2:p:21582440231165224
    DOI: 10.1177/21582440231165224
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    References listed on IDEAS

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