scholarly journals Supply Chain Finance Credit Risk Evaluation Method Based on Self-Adaption Weight

2015 ◽  
Vol 03 (07) ◽  
pp. 13-21 ◽  
Author(s):  
Yueliang Su ◽  
Nan Lu
2018 ◽  
Vol 10 (10) ◽  
pp. 3699 ◽  
Author(s):  
WeiMing Mou ◽  
Wing-Keung Wong ◽  
Michael McAleer

Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Baitong Chen ◽  
Xinzhong Bao ◽  
Kun Xu

In view of problems such as lack of dynamism, limited research subjects, and lack of future development trends in previous studies, the paper takes small and microenterprises (SMEs) as research objects under the background of e-commerce supply chain finance. Based on the perspective of dynamic rewards and punishments, credit rewards and punishment value and time weights are embedded in the static evaluation results obtained by the traditional TOPSIS method. The Grey relative analysis method is used to reflect the development trend of enterprises’ credit and to build the traditional TOPSIS model and the credit risk evaluation model of e-commerce supply chain finance of SMEs by the improved TOPSIS method based on the dynamic perspective of rewards and punishments. Finally, the model is applied to SMEs credit risk evaluation of e-commerce supply chain finance to verify the feasibility and rationality of the model.


2021 ◽  
Vol 275 ◽  
pp. 01061
Author(s):  
Zeping Tong ◽  
Shuo Yang

Agriculture is a basic industry that supports the construction and development of the national economy and plays an important role in promoting rural revitalization. And in the current post-COVID-19 era, agricultural SMEs have difficulty in obtaining the favours of financial institutions in normal lending due to their weak credit guarantee capabilities and high credit management costs. Difficulty in financing has become a bottleneck problem that plagues the development of enterprises and restricts the development of agricultural modernization. How to evaluate and control its credit risk is not only a major way to solve the financing difficulties of agricultural SMEs, but also the basis for the stable development of supply chain financial services. This paper analyzes three typical financing modes of agricultural SMEs from the perspective of supply chain finance, and takes the agricultural SMEs in the New OTC Market as an example to construct a Logistic model, and uses factor analysis to effectively predict the credit risk of supply chain finance. The results show that the operational efficiency factors, growth factors and related core corporate profitability of agricultural SMEs financing enterprises significantly affect their credit risk. After testing, the model is highly accurate in predicting the financing risks of agricultural SMEs.


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