Improving the predictability of business failure of supply chain finance clients by using external big dataset

Abstract

Purpose – The purpose of this paper is to help the financial institutions improve the predictability of business failure of supply chain finance (SCF) clients with the use of external big data set. Design/methodology/approach – A prediction model for the business failure of SCF clients was built upon different theoretical perspectives. Logistic regression method was deployed to test the model. Findings – The authors develop a model that illustrates several key determinants to predict the probability of business failure of SCF clients based on several theoretical perspectives. The results show that taxable sales revenue, frequency of making value added tax (VAT) payment, number of counterparty for VAT invoice issuance, frequency of VAT invoice issuance and firm age are negatively correlated with business failure of SCF clients while the VAT paid and industry clockspeed are positively correlated with their business failure. Practical implications – This paper shows how financial institutions can effectively leverage the external information sources through “unconventional” predictor variables in order to reduce the credit risks associated with business failure of SCF clients. Originality/value – This paper is one of the first to focus on the potential use of financial big data set from external sources to improve of predictability of financial institutions on the business failure of SCF clients. In addition, this paper is a pivotal study on the financial client risk assessment based on taxpaying behaviors, tax amount, firm and industry characteristics.

Supply chain finance: From traditional to supply chain credit rating

“…Indeed, taking the firm as a single independent entity, instead of a part of a supply chain, the risk assessed could be overestimated. In addition, Zhao et al (2015;p.1683) state that "financial institutions can effectively leverage the external information sources through "unconventional" predictor variables in order to reduce the credit risks associated with business failure of SCF clients." Therefore, the financial rating model, based only on quantitative data, needs to be enriched with qualitative variables and a broader supply chain perspective, especially to improve the capability to predict the company's probability of default.…”

“…There have been attempts to also consider operations and supply chain information to assess the risk of a company (e.g., Bendig et al, 2017), and financial institutions are looking for new advanced analytics to detect potential business failures (Zhao et al, 2015). While preliminary studies have been done, a true supply chain-oriented credit rating model to be used by financial institutions is still missing (Wang, 2010;Su and Lu, 2015).…”

Big Data Analysis as a Source of Companies’ Competitive Advantage: A Review

“…Volume is associated with the amount of information, which is almost unlimited (enormous) and gathered from diverse sources, e.g. business and individual transactions, social media, sensor data (Watson & Marjanovic, 2013;Zhao et al, 2015;Hofacker, Malthouse, & Sultan, 2016;He, Wang, & Akula, 2017). What is more, the data is exchanged among devices and stored with the usage of such technologies as Hadhoop, High Performance Computing Cluster (HPCC) and Hadapt (He, Wang, & Akula, 2017).…”

“…Banks also analyse mass information to reduce potential fraud and ensure compliance with supervisory regulations (Srivastava & Gopalkrishnan, 2015;Saxena & Al-Tamimi, 2017). For example, Zhao et al (2015) prepared a model for financial units to be better prepared for the potential failure of customers. This scheme was based on external Big Data and used to increase the predictability of the potential future negative effects.…”

“…social media, GPS, etc.) and the subsequent analysis of this with the aim of achieving better results (Shanmuganathan, 2014;Wu, Zhu, Wu, & Ding, 2014;Zhao Yeung, Huang, & Song, 2015;Del Vecchio, Mele, Ndou, & Secundo, 2018).…”