Risk assessment of supply chain finance with intuitionistic fuzzy information

2016 ◽  
Vol 31 (3) ◽  
pp. 1967-1975 ◽  
Author(s):  
Chun-Lian Zhang
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Supply Chain Finance

Research on supply chain risk assessment with intuitionistic fuzzy information

2016 ◽  
Vol 30 (6) ◽  
pp. 3367-3372 ◽  
Author(s):  
Shenghan Zhou ◽  
Chen Hu ◽  
Yue Xie ◽  
Wenbing Chang
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Fuzzy Information ◽  
Supply Chain Risk ◽  
Intuitionistic Fuzzy

scholarly journals Risk Evaluation and Control of Supply Chain Finance

2018 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Ying Yu Du ◽  
Jun Bin Zhong ◽  
Zi Yue Su ◽  
Xin Peng Yang ◽  
Yi Lan Yao
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Key Factors ◽  
Supply Chain Risk ◽  
Analytic Hierarchy ◽  
Industry Chain ◽  
Supply Chain Finance ◽  
Financial Index ◽  
And Control ◽  
Hierarchy Process

As an effective way of enterprises financing, supply chain finance has attracted much attention in recent years. However, since supply chain finance has some problems like long financing period, numerous stakeholders and complex effects, banks are at a higher risk carrying out this kind of service. The purpose of this paper is to explore the key factors in supply chain finance risk assessment and study the effective mode of risk elevation. Based on the existing literature and research, this paper uses Z-score to standardize the financial index of 344 medium-sized enterprises in automotive industry chain from October, 2016 to October, 2017 and build a model of supply chain risk assessment and control basing on analytic hierarchy process, principal components analysis and logistic regression analysis. Finally, we summarize how each index affects risk assessment and then analyze the reasons.

scholarly journals Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

2020 ◽  
Vol 16 (1) ◽  
pp. 155014772090363 ◽  
Author(s):  
Ying Liu ◽  
Lihua Huang
Keyword(s):  
Risk Assessment ◽  
Support Vector Machine ◽  
Supply Chain ◽  
Credit Risk ◽  
Financial Analysis ◽  
Learning Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Machine Model ◽  
Supply Chain Finance

Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach.

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

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Cong Wang ◽  
Fangyue Yu ◽  
Zaixu Zhang ◽  
Jian Zhang
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Credit Risk ◽  
Quantitative Result ◽  
Small Sample ◽  
Graph Learning ◽  
Credit Risk Assessment ◽  
Risk Assessment Models ◽  
Supply Chain Finance ◽  
Index Systems

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.

Sign in / Sign up

Export Citation Format

Share Document