Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network

2022 ◽  
Author(s):  
Jingfeng Zhao ◽  
Bo Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Supply Chain ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Credit Risk Assessment ◽  
Supply Chain Finance

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.

scholarly journals A credit risk assessment model of borrowers in P2P lending based on BP neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255216
Author(s):  
Zhengwei Ma ◽  
Wenjia Hou ◽  
Dan Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Bp Neural Network ◽  
Assessment Model ◽  
P2p Network ◽  
Risk Assessment Model ◽  
Credit Risk Assessment ◽  
P2p Lending ◽  
Lm Algorithm

Peer-to-Peer (P2P) lending provides convenient and efficient financing channels for small and medium-sized enterprises and individuals, and therefore it has developed rapidly since entering the market. However, due to the imperfection of the credit system and the influence of cyberspace restrictions, P2P network lending faces frequent borrower credit risk crises during the transaction process, with a high proportion of borrowers default. This paper first analyzes the basic development of China’s P2P online lending and the credit risks of borrowers in the industry. Then according to the characteristics of P2P network lending and previous studies, a credit risk assessment indicators system for borrowers in P2P lending is formulated with 29 indicators. Finally, on the basis of the credit risk assessment indicators system constructed in this paper, BP neural network is built based on the BP algorithm, which is trained by the LM algorithm (Levenberg-Marquardt), Scaled Conjugate Gradient, and Bayesian Regularization respectively, to complete the credit risk assessment model. By comparing the results of three mentioned training methodologies, the BP neural network trained by the LM algorithm is finally adopted to construct the credit risk assessment model of borrowers in P2P lending, in which the input layer node is 9, the hidden layer node is 11 and output layer node is 1. The model can provide practical guidance for China and other countries’ P2P lending platforms, and therefore to establish and improve an accurate and effective borrower credit risk management system.

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