A hybrid XGBoost-MLP model for credit risk assessment on Digital Supply Chain Finance

Li, Y., Stasinakis, C. and Yeo, W. M. (2022) A hybrid XGBoost-MLP model for credit risk assessment on Digital Supply Chain Finance. Forecasting, 4(1), pp. 184-207. (doi: 10.3390/forecast4010011)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stasinakis, Professor Charalampos and Li, Yixuan and Yeo, Dr Wee Meng

Creator Roles

Li, Y.: Conceptualization, Methodology, Software, Validation, Data curation, Writing – original draft, Writing – review and editing
Stasinakis, C.: Conceptualization, Writing – original draft, Writing – review and editing, Supervision, Project administration
Yeo, W. M.: Conceptualization, Writing – original draft, Writing – review and editing, Supervision

• Authors: Li, Y., Stasinakis, C., and Yeo, W. M.
• College/School: College of Social Sciences; College of Social Sciences > Adam Smith Business School > Accounting and Finance; College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Forecasting
• Publisher: MDPI
• ISSN: 2571-9394
• ISSN (Online): 2571-9394
• Published Online: 29 January 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Forecasting 4(1):184-207
• Publisher Policy: Reproduced under a Creative Commons License