Improving the predictability of business failure of supply chain finance clients by using external big dataset
| Date | 19 October 2015 |
| DOI | https://doi.org/10.1108/IMDS-04-2015-0161 |
| Published date | 19 October 2015 |
| Pages | 1683-1703 |
| Author | Xiande Zhao,KwanHo Yeung,Qiuping Huang,Xiao Song |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
Improving the predictability of
business failure of supply chain
finance clients by using external
big dataset
Xiande Zhao, KwanHo Yeung, Qiuping Huang and Xiao Song
School of Business Administration,
South China University of Technology, GuangZhou, China
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 predictionmodel for the business failureof 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.
Keywords Business failure, Financial big data, Industry organization perspective,
Organization ecology perspective, Organization studies perspective, Supply chain finance
Paper type Research paper
Introduction
Managing financial client risk is more challenging than ever in the financial industry in
China as many financialinstitutions have proactivelyoffered innovative financialservice
solutions to both SMEs (which are potentially risky) and core firms (i.e. powerful supply
chain players) to increase profits and market reputation. One remarkable financial
service solution is known as supply chain finance (SCF), which is a short-term credit to
optimize the cash flows and working capitals of collaborating firms within a specified
supply chain.While the SCF is highly perceived bythe industry as a promising business,
it comes with significant risks as the SCF involves the commitmentof multiple parties in
a SCF contract on timely fulfillment of obligations,as well as the complex assessment of
creditworthiness and authenticity of supply chain transaction data (Shenzhen
Development Bank Limited Company (SDB) and China Europe International Business
School, 2009). Given that the SCF is not secured by fixed collateral, the financial
institutions are vulnerable to significant risks when the opportunistic behaviors and
supply chain disruption (i.e. negative deviation from the normal course of supply chain
activities because of unexpected fatal incidents) arise from the business failure of SCF
Industrial Management & Data
Systems
Vol. 115 No. 9, 2015
pp. 1683-1703
©Emerald Group Publis hing Limited
0263-5577
DOI 10.1108/IMDS-04-2015-0161
Received 30 April 2015
Revised 19 August 2015
8 September 2015
Accepted 11 September 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1683
Business
failure of
SCF clients
clients. As a result,more and more financial institutions look for new advancedanalytics
to detect the SCF clients’business failure, which refers to a situation where the SCF
clients cease to exist due to the inability to absorb negative shocks and respond to all
kinds of supply chain environmental changes (Amankwah-Amoah and Debrah, 2010;
Watson and Everett, 1993), in the early stage.
Improving the predictability of business failure of SCF clients is critical to the
success of SCF business as it makes financial institutions possible to introduce
preventive strategies, thus improving the overall non-performing SCF loan ratio
effectively. Given that the new financial regulatory requirements in China highlight
governance, risk exposure and transparent data analysis (Li et al., 2014), it becomes
imperative for financial institutions in China to develop relevant prediction models.
In this paper, we develop a logistic regression model to demonstrate how the financial
institutions can utilize the external big data set to improve the predictability of SCF
clients’business failure. Our paper provides a pivotal solution to the financial
institutions which require a careful selection of SCF clients through innovative and cost
effective financial client risk assessment.
The rest of the paper is organized as follows. The second section, highlights the SCF
risk management inbig data era in China. The third section,demonstrates the criticality
of big data for the financial industry in the knowledge economy era. The fourth section,
provides an overview of business failure from different perspectives. The fifth section,
highlights the development of the proposed hypotheses. The sixth section, illustrates the
research methodology and the hypotheses results. It is followed by a discussion of
management implications and a conclusion in last two sections, respectively.
SCF risk management in big data era in China
SCF is to provide downstream buyers or upstream suppliers with a short-term credit to
improve their working capital and unlock financial liquidity of their collaborating
supply chain partners in order to avoid the resilience of supply chain (Wuttke et al.,
2013; Zhang, 2010). In current China, most SCF clients are SMEs and supported by core
firms (i.e. powerful supply chain partners). From the risk management perspective, SCF
is a multiple-party financing arrangement and is repaid through the cash conversions
of current assets (e.g. accounts receivable, inventory and prepayment) being financed
by SCF (Wuttke et al., 2013; Zhang, 2010). As the SCF involves the commitment of
multiple parties on timely fulfillment of obligations, it requires the financial institutions
to conduct a comprehensive assessment of SCF clients with the careful consideration of
various risk dimensions, including the repayment abilities derived from the conversion
of current assets being financed by the SCF financial institution into sales revenue,
the authenticity of supply chain transaction data, the supply chain effectiveness
(as reflected in supply chain partnership), the business environmental dimension and
the operational dimension (SDB and China Europe International Business School,
2009). As such, the SCF financial institutions needs to rely much on a sophisticated
model to determine the creditworthiness of SCF clients. Otherwise, the SCF financial
institutions will suffer from significant losses when the opportunistic behaviors and
supply chain disruption arise from the unexpected business failure of SCF clients.
While the early detection of business failure of SCFclients is important to the success
of SCF, the financial institutions in China face hindrances in developing sophisticated
models due to the complex credit environment and the issues of less accountable and
transparent corporate governance in China.In addition, as most financial institutions are
accustomed to controlling risks through traditional security (e.g. fixed collaterals) and
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