Affiliation:
1. Northwest A&F University, Yangling, Shaanxi, China
2. Dalian Maritime University, Liaoning, China
3. University of Edinburgh, UK
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.
Funder
Key Project of National Natural Science Foundation of China
National Natural Science Foundation of China
Tang Scholar Program of Northwest A&F University
Graduate Science and Technology Innovation Project of College of Economics & Management, Northwest A&F University
Social Science Foundation of Shaanxi Province
Subject
General Social Sciences,General Arts and Humanities