article
Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques
Liukai Wanga, Fu Jiab✉, Lujie Chenc d✉, Qifa Xue
- aThe School of Economics and Management, University of Science and Technology Beijing, Beijing, China
- bThe York Management School, University of York, YO10 5DD, York, UK
- cThe International Business School, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China
- dThe International Business School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
- eThe School of Management, Hefei University of Technology, Hefei, 230009, China
- ✉Corresponding author
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Abstract
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TL;DRThe empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. (Semantic Scholar)

