Multiview Graph Learning for Small‐ and Medium‐Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Abstract

In recent years, supply chain finance (SCF) is exploited to solve the financing difficulties of small- and medium-sized enterprises (SMEs). SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME.

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

“…Supply chain finance has increased the modest and moderate business sector's interest rate businesses from an enterprise resource planning standpoint. Banking and investment firms have a crucial role in managing risk across the whole loan process, from preloan to postloan [7]. Risk in the distribution chain refers to the likelihood that vendors will face a corporate setting that could harm their commercial well-being.…”

Network Evolutionary Game‐Based Diffusion Mechanism regarding the Nonperformance of Farmers in Agricultural Supply Chain Finance

“…Liu and Yan evaluated the credit risk of farmers by using the fuzzy analytic hierarchy process (FAHP) [31]. Wang et al predicted the credit risks of SMEs in supply chain finance with a multiview learning method [32]. Wu et al explored risk assessment methods by using a genetic algorithm (GA) and backpropagation neural network (BPNN) to reduce credit risks of agricultural supply chain finance [33].…”

Optimal Financing Decision in a Contract Food Supply Chain with Capital Constraint

“…Finance. Supply chain finance is an effective way to solve the financing problems of SMEs [2][3][4][5][6]. It transforms the uncontrollable risks of a single enterprise into the controllable risks of the entire supply chain, effectively constructs a benign industrial ecology for banks, core enterprises, and SMEs, and promotes the interactive development of capital and industry in the real trade situation through the effective control of cash flow, information flow, and logistics.…”

“…Supply chain finance refers to two or more organizations and external service providers in a supply chain that work together to create the value of all participating companies by planning, steering, and controlling the flow of financial resources at an interorganizational level [1,2]. Meanwhile, it is an effective way to solve the financing problem of smalland medium-sized enterprises (SMEs) [3][4][5][6], which is widely used in the manufacturing industry. We learn that the characteristics of family farms are similar to those of SMEs, so supply chain finance may be one of the feasible ways to solve their financing problem.…”