A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance

Zhang, Hao; Shi, Yuxin; Yang, Xueran; Zhou, Ruguang

doi:10.1016/j.ribaf.2021.101482

Cited by 55 publications

(19 citation statements)

References 31 publications

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“…The existing literature on SME credit risk prediction from the perspective of SCF has developed relevant prediction models (Wang, Ding, et al, 2020). Zhang et al (2021) used a firefly algorithm to improve the SVM model under the background of SCF. 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.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A credit risk assessment based on SCF considers the financial and operational features of CEs and SMEs and relevant supply chain information (Bendig et al, 2017). The relevant literature studies the credit risk prediction of SMEs through matched samples of CEs and SMEs to obtain higher prediction performance and provide support for the practice of SCF (Sang, 2021; Zhang et al, 2021; Zhu et al, 2019). However, this kind of literature does not discuss the incremental effect of supply chain information on enterprise credit risk prediction.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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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“…In the equipment manufacturing industry (C39), compared with the C26 industry, the number of SME boards in C39 industry is more than that of special treatment enterprises (ST-enterprises) (Zhang et al, 2021), so we focus on manufacturing industry. However, from the current situation of China's logistics development, there are still some common problems, which affect the realization of the sustainable development of China's supply chain.…”

Section: Sampling Proceduresmentioning

confidence: 99%

“…Fujian Star-net Communication Data resource: White paper on the development of blockchain financial applications/ the official websites/CSMAR database This paper integrated the participating enterprises under the BC-driven SCF mode, then establish the index system according to the basic structure requirements of the above credit risk assessment index system. Because most indicators come directly from China's CSMAR database (H. Zhang et al, 2021), all the financial data in the sample are mainly from CSMAR database, and the non-financial data are obtained through the company's annual report and related news reports.…”

Section: Rihai Communication St Chenxingmentioning

confidence: 99%

Construction of Credit Risk Assessment Index System of SMEs based on Blockchain-driven Supply Chain Finance

Xiao¹,

Salleh²,

Cheng³

2022

IJAREMS

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blockchain-driven supply chain finance (BC-driven SCF) can effectively integrate the credit resources of upstream and downstream enterprises and enhance the financing capacity of all enterprises in the chain, taking the core enterprise as the center and the blockchain technology as the platform, which plays an important role in the upgrading of the industrial chain. In order to accurately and objectively evaluate the SMEs' credit risk based on BC-driven SCF, we need to establish a scientific and complete credit risk assessment index system, formulate strict design ideas and select appropriate design method. By analyzing the formation mechanism of blockchain-driven supply chain finance model, and using relevant theoretical and empirical methods, this paper puts forward a credit risk assessment index system suitable for the supply chain financial environment. Finally, 27 three-level indicators, 13 two-level indicators and 5 primary indicators were established in index system. This conclusion provides a reference for the application of blockchain in supply chain to reduce the credit risk. At the same time, the selected indicators are more comprehensive, which provides a strong basis for the subsequent construction of credit risk assessment model

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“…With the diversification of corporate financial data and the continuous advancement of intelligent classification methods, a lot of research on intelligent financial classification methods has been carried out in the field of corporate finance, and many excellent results have been also achieved [ 10 ]. Zhang et al [ 11 ] used the firefly algorithm to optimize the support vector machine (SVM) algorithm and developed a new type of firefly support vector machine (FA-SVM) algorithm. The credit risk of supply chain finance was evaluated and verified, and the results showed that the algorithm they proposed has high accuracy in the financial field and can accurately find groups with lower credit.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model

2022

Computational Intelligence and Neuroscience

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An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.

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A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance

Cited by 55 publications

References 31 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

Construction of Credit Risk Assessment Index System of SMEs based on Blockchain-driven Supply Chain Finance

An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model

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