A data-driven and network-aware approach for credit risk prediction in supply chain finance
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 1 July 2020
Issue publication date: 29 March 2021
Abstract
Purpose
The purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.
Design/methodology/approach
Based on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.
Findings
The findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.
Research limitations/implications
The main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.
Practical implications
The proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.
Originality/value
This study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.
Keywords
Citation
Rishehchi Fayyaz, M., Rasouli, M.R. and Amiri, B. (2021), "A data-driven and network-aware approach for credit risk prediction in supply chain finance", Industrial Management & Data Systems, Vol. 121 No. 4, pp. 785-808. https://doi.org/10.1108/IMDS-01-2020-0052
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited