The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network

Wu, Yingli; Li, Xin; Liu, Qingquan; Tong, Guangji

doi:10.1007/s10614-021-10137-2

Cited by 41 publications

(22 citation statements)

References 34 publications

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“…Sample data tests of 39 SMEs in the computer and electronic communication manufacturing industry revealed that this method is better than a library for SVM. Wu et al (2021) used a genetic algorithm to optimize the back propagation neural network under the background of agricultural SCF and tested the data of listed Chinese enterprises. The new algorithm achieved better prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the upstream and downstream enterprises disclosed by some target enterprises are anonymous, the sample of the target enterprises is excluded. (2) According to the previous literature (Wang, Ding, et al, 2020; Wu et al, 2021; Zhu et al, 2019), to ensure that the financial information of upstream and downstream enterprises can be collected, retain only upstream or downstream enterprises as samples of listed enterprises. (3) By analyzing the distribution of the target enterprises screened by the above steps, the target enterprises, whose number of downstream enterprises is not 0, are selected as the research object according to the standard of the largest number of target enterprises.…”

Section: Research and Designmentioning

confidence: 99%

“…(2) According to the previous literature (Wang, Ding, et al, 2020;Wu et al, 2021;Zhu et al, 2019), to ensure that the financial information of upstream and downstream enterprises can be collected, retain only upstream or downstream enterprises as samples of listed enterprises.…”

Section: Data and Variablesmentioning

confidence: 99%

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Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Yao

Zhou

et al. 2022

Expert Systems

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The spread of enterprise credit risk in the supply chain may lead to large‐scale bankruptcy and credit crises, which are related to national economic and social stability and financial system security. Therefore, enterprise credit risk in the supply chain context is not only a concern for banking financial institutions, credit rating agencies and enterprise managers but also the focus of governments. This article develops a DTE‐DSA (decision tree [DT] ensemble model using the differential sampling rate, Synthetic Minority Oversampling Technique [SMOTE] and AdaBoost) prediction framework integrating supply chain information to predict enterprise credit risk. The empirical test shows that using supply chain information can significantly improve the prediction score. The DTE‐DSA model has the best prediction effect in dealing with class imbalance problems. Compared with single classifier models—such as logistic regression, k‐nearest neighbours, support vector machine, DT and DT using the SMOTE—as well as ensemble models—such as extremely randomized trees, random forest, rotation forest, extreme gradient boosting, gradient boosting DT and DT ensemble model using AdaBoost—the DTE‐DSA model not only has the best prediction score but also has a more stable performance. The comprehensive use of supply chain information and the DTE‐DSA model can result in the highest prediction score, with an area under the curve of 0.9016 and a Kolmogorov–Smirnov statistic of 0.7369. Further analysis of the variables of importance enhances the interpretability of the model and obtains relevant management insights.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Research and Designmentioning

confidence: 99%

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Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Yao

Zhou

et al. 2022

Expert Systems

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“…Credit risks and operational risks caused by information asymmetry make it difficult for financial institutions and SMEs to determine whether their choice optimize their returns [29,30]. Therefore, financial institutions might choose to accept or decline the applications of SMEs' factoring business by considering credit risks and operational risks, and SMEs may choose to comply to maximize their profits or not.…”

Section: Problem Descriptionmentioning

confidence: 99%

Evolutionary Game Analysis of Blockchain Technology Preventing Supply Chain Financial Risks

Sun

2021

JTAER

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Because of the risks existing in supply chain finance, taking accounts receivable factoring business as the research object, this paper uses the evolutionary game method to analyzes the factors affecting the decision-making of the participants in supply chain finance, constructs an evolutionary game model between small and medium-sized enterprises and financial institutions, and analyzes the mechanism of blockchain to solve the financial risks of the supply chain by comparing the changes of evolutionary stability strategies before and after the introduction of blockchain technology. This paper aims to reduce financing risks by analyzing the mechanism of blockchain technology in supply chain finance. It is found that, firstly, blockchain technology can reduce the credit risk of financial institutions and solve financing problem. Credit risk plays a decisive role in whether financial institutions accept financing business decisions. Blockchain technology can reduce the operational risk of financial institutions and improve the business income of financial institutions. Secondly, the strict regulatory environment formed by blockchain technology makes the default behavior of small and medium-sized enterprises and core enterprises in a high-risk state at all times. No matter the profit distribution proportion that small and medium-sized enterprises can obtain through collusion, they will not choose to default, which effectively solves the paradox that small and medium-sized enterprises cannot obtain loans from financial institutions despite the increased probability of compliance. Then, the evolutionary game between financial institutions and small and medium-sized enterprises is balanced in that financial institutions accept business applications, small and medium-sized enterprises abide by the contract, and the convergence effect is better. Therefore, blockchain technology not only reduces the financing risk of financial institutions but also helps to solve the financing problems of small and medium-sized enterprises.

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“…Backpropagation neural network is currently the most widely researched and applied artificial neural network in various fields. It embodies the most essential part of artificial neural network theory and application [ 8 ]. The technological innovation capability of an enterprise is affected by many factors.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation Model of Agricultural Enterprise Technology Innovation Based on GA-BP Neural Network

Kang

Zhao

2022

Computational Intelligence and Neuroscience

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With the continuous development of my country’s economy and society, how to effectively evaluate the technical innovation performance of agricultural enterprises has become the focus of research. This paper firstly processes and analyzes the technical innovation performance data of agricultural enterprises, and then processes and converts the technical innovation performance data of agricultural enterprises; then, through the analysis of the technical innovation performance data of agricultural enterprises, the key characteristics of the technical innovation performance of agricultural enterprises are excavated. Finally, a performance evaluation model based on agricultural enterprise technology innovation is proposed, and the validity of the model is verified with examples.

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The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network

Cited by 41 publications

References 34 publications

Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

Evolutionary Game Analysis of Blockchain Technology Preventing Supply Chain Financial Risks

Performance Evaluation Model of Agricultural Enterprise Technology Innovation Based on GA-BP Neural Network

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