Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques

Liukai Wang; Fu Jia; Lujie Chen; Qifa Xu

doi:10.1007/s10479-022-04518-5

Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques

Liukai Wang, Fu Jia^*, Lujie Chen, Qifa Xu

^*Corresponding author for this work

Management

Research output: Contribution to journal › Article › peer-review

Abstract

Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The 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. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

Original language	English
Pages (from-to)	1-33
Number of pages	33
Journal	Annals of Operations Research
Volume	331
Early online date	31 Jan 2022
DOIs	https://doi.org/10.1007/s10479-022-04518-5
Publication status	Published - 1 Dec 2023

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10.1007/s10479-022-04518-5

Cite this

@article{08f4492e17d64f2ebafe26c4ff3bb9e3,

title = "Forecasting SMEs{\textquoteright} credit risk in supply chain finance with a sampling strategy based on machine learning techniques",

abstract = "Exploring the value of multi-source information fusion to predict small and medium-sized enterprises{\textquoteright} (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs{\textquoteright} credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs{\textquoteright} credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs{\textquoteright} managers, investors, and market regulators.",

author = "Liukai Wang and Fu Jia and Lujie Chen and Qifa Xu",

year = "2023",

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doi = "10.1007/s10479-022-04518-5",

language = "English",

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journal = "Annals of Operations Research",

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AU - Wang, Liukai

AU - Jia, Fu

AU - Chen, Lujie

AU - Xu, Qifa

PY - 2023/12/1

Y1 - 2023/12/1

N2 - Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The 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. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

AB - Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The 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. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

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