Abstract
Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF). In this paper, 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 are selected as the empirical sample, and the important features of credit risk assessment in DSCF are automatically selected through the feature selection of the XGBoost model in the first stage, then followed by credit risk assessment through the MLP in the second stage. Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model. From the perspective of DSCF, the results show that the inclusion of digital features improves the accuracy of credit risk assessment in SCF.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The hybrid method of XGBoost-multi-layer perceptron (MLP) is observed to have optimal performance, which contributes to the enhancement and development of the theory of enterprise risk assessment models in the Digital SCF (DSCF) environment, and provides new ideas to improve the accuracy of enterprise credit risk prediction
The study on DSCF features is conducted by comparing the assessment results with and without DSCF features; we find that the credit risk assessment of firms is better when their DSCF features are taken into account
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. We use 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 as the sample, and select the important feature automatically through XGBoost at the first stage, compare the performance of MLP and other machine learning models in credit risk assessment. The hybrid method of XGBoost-MLP is observed to have optimal performance, which contributes to the enhancement and development of the theory of enterprise risk assessment models in the DSCF environment, and provides new ideas to improve the accuracy of enterprise credit risk prediction. Feature selection plays an important role in credit risk assessment, and selecting the most appropriate features as indicators for credit risk assessment analysis helps to improve the accuracy of the model This extends the application of traditional credit risk assessment indicator systems and provides strong evidence for banks and other financial institutions to make sound financing decisions.
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