scholarly journals Credit Risk Assessment of Supply Chain Financing with a Grey Correlation Model: An Empirical Study on China’s Home Appliance Industry

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaohan Huang ◽  
Jihong Sun ◽  
Xiaoyun Zhao
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Empirical Study ◽  
Credit Risk ◽  
Financial Risk ◽  
Assessment Process ◽  
Correlation Model ◽  
Grey Correlation ◽  
Home Appliance ◽  
The Government

Supply chain finance (SCF) plays an increasingly important role in global enterprise competition. The credit risk accompanying SCF has attracted the attention of the government, enterprises, and academia. However, with the absence of data and inaccurate information, traditional risk assessment methods are frequently failed to assess the credit risk in SCF, especially for small- and medium-sized enterprises (SMEs). In this study, a grey correlation model is introduced and applied to the SCF risk assessment process for 15 firms in the Chinese home appliance industry with 15 performance indicators that represent profitability, solvency, operational capability, and development capability. The empirical study displays the operability and effectiveness of the grey correlation model, which is superior to traditional methods in the supply chain financial risk assessment.

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 ANALIZA KONSOLIDACJI AKTYWÓW I ZOBOWIĄZAŃ W PROCESIE RESTRUKTURYZACJI GRUPY KAPITAŁOWEJ

2020 ◽  
Vol 20 (4) ◽  
pp. 241-251
Author(s):  
Eryk Bobowski
Keyword(s):  
Risk Assessment ◽  
Financial Risk ◽  
Repair Process ◽  
Parent Company ◽  
Assessment Process ◽  
Risk Assessment Process ◽  
Production Area ◽  
Level Of Risk

The article analyzes the effectiveness of assets and liabilities consolidation in the process of restructuring a capital group operating in the production area. The built model can help to prepare and carry out the repair process, as well as to use it in the financial risk assessment process. The key element is also to draw attention to the significant increase in the capital of the parent company, and as a consequence to the financial ability to implement new investments taking into account the higher level of risk.

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.

Prioritizing Key Supply Chain Risks Using the Risk Assessment Matrix and Shannon Fuzzy Entropy — with a Case Study in the Home Appliance Industry

2018 ◽  
Vol 17 (03) ◽  
pp. 333-351
Author(s):  
Arash Shahin ◽  
Arezou Kianersi ◽  
Azarakhsh Shali
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Supply Chain ◽  
Product Development ◽  
New Product Development ◽  
Political Stability ◽  
Fuzzy Entropy ◽  
Home Appliance ◽  
Assessment Matrix ◽  
Supply Chain Risks

The aim of this study is to present an approach for identifying and prioritizing risk factors of supply chain in the home appliance industry. First, the indexes related to the supply chain risk have been determined by literature review; and then the indexes have been refined by consulting experts; finally, the six main indexes including supplier, manufacturer, customer, environment, distributor and information as well as 32 subsidiary indexes for supply chain risks have been selected. In order to assess the indexes, a fuzzy questionnaire has been developed and distributed to 15 managers and employees of Snowa as one of the Entekhab Industrial Group corporate brands, the main home appliance manufacturing company in Iran. Research population included managers and experts of the company and the analysis approaches included risk-assessment matrix and Shannon fuzzy entropy. Findings indicated that environment, manufacturer and supplier indexes with the weights of 0.105, 0.102 and 0.095, respectively were prioritized as the top three risk factors in product development. Furthermore, the subsidiary indexes of raw material alteration, delay in supply of the demand, work force, after-sale services, competitors and lack of political stability were among the top risk factors of new product development.

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