A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance

Yixuan Li, Charalampos Stasinakis and Wee Meng Yeo
Additional contact information
Yixuan Li: Department of Accounting and Finance, Adam Smith Business School, University of Glasgow, Room 302, 11 Southpark Terrace, Glasgow G12 8LG, UK
Charalampos Stasinakis: Department of Accounting and Finance, Adam Smith Business School, University of Glasgow, University Avenue, West Quadrangle, Gilbert Scott Building, Glasgow G12 8QQ, UK
Wee Meng Yeo: Department of Management, Adam Smith Business School, University of Glasgow, University Avenue, West Quadrangle, Gilbert Scott Building, Glasgow G12 8QQ, UK

Forecasting, 2022, vol. 4, issue 1, 1-24

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.

Keywords: supply chain finance; credit risk assessment; machine learning; XGBoost-MLP (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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