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

Nana Chai, Baofeng Shi, Bin Meng and Yizhe Dong

SAGE Open, 2023, vol. 13, issue 2, 21582440231165224

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.

Keywords: feature attributes; association rule; small enterprise; AFCM-SMOTENC-APRIORI algorithm; imbalanced data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:2:p:21582440231165224

DOI: 10.1177/21582440231165224

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