A data-driven and network-aware approach for credit risk prediction in supply chain finance

Abstract

<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Research limitations/implications</jats:title><jats:p>The main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Practical implications</jats:title><jats:p>The proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>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.</jats:p></jats:sec>

Reduce supply chain financing risks through supply chain integration: dual approaches of alleviating information asymmetry and mitigating supply chain risks

“…It is undeniable that scholars have paid attention to SCR focusing on logistics, but empirical research on SCFR focusing on capital flow is insufficient (Zhu et al, 2019;Lu et al, 2022). Although there lacks studies linking them together for empirical test, scholars often mention the possible impact of SCR on SCFR when conducting other relevant studies (Gelsomino et al, 2016;Fayyaz et al, 2020). SCR affects SCF activities and exacerbates SCFR (Jia et al, 2020), because SCR weakens solvency, leads to failure repayment, reduces credit level and impacts performance (Donnelly et al, 2014;Kouvelis and Zhao, 2018;Nguema et al, 2022), which are all factors that cause SCFR.…”

“…These results reflect the important role of customer integration in stabilizing sales collection ability and ensuring solvency. As Song et al (2020) and Yu et al (2021) stressed, customer integration helps SMEs to better understand customers' needs, obtain demand-side information, identify and reflect to market changes to reduce liquidity risks and mitigate SCFR (Wuttke et al, 2016;Fayyaz et al, 2020).…”

“…Although there lacks studies linking them together for empirical test, scholars often mention the possible impact of SCR on SCFR when conducting other relevant studies (Gelsomino et al. , 2016; Fayyaz et al. , 2020).…”

“…For prediction, Zhu et al (2019) analyzed the use of combining random subspaces and multiple boosting methods to improve the accuracy of credit risk prediction for listed SMEs. Fayyaz et al (2020) proposed a data-driven mathematical model to analyze the network attributes of cooperative enterprises in SCF networks and predict their credit risks. For mitigation, Wang et al (2019) proposed a model to mitigate SCFR based on the Internet of things.…”

“…proposed that SCR affects SCF activities and brings financing risks. For example, supplyside risks hinder the supply to other enterprises, causing interruptions and liquidity risks, thus result in failure repayment Fayyaz et al (2020). found that controllable demand-side risks guarantee SMEs to meet market demands and promote sales collection to strengthen financial capability and ensure solvency.…”