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Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

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  • Weiqiong Fu
  • Hanxiao Zhang
  • Fu Huang

Abstract

To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

Suggested Citation

  • Weiqiong Fu & Hanxiao Zhang & Fu Huang, 2022. "Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-18, January.
  • Handle: RePEc:plo:pone00:0262222
    DOI: 10.1371/journal.pone.0262222
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    References listed on IDEAS

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    1. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    2. V. Raja Sreedharan & V. Kamala & P. Arunprasad, 2019. "Supply chain risk assessment in pharmaceutical industries: an empirical approach," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 18(4), pages 541-571.
    3. Nakatani, Jun & Tahara, Kiyotaka & Nakajima, Kenichi & Daigo, Ichiro & Kurishima, Hideaki & Kudoh, Yuki & Matsubae, Kazuyo & Fukushima, Yasuhiro & Ihara, Tomohiko & Kikuchi, Yasunori & Nishijima, Asak, 2018. "A graph theory-based methodology for vulnerability assessment of supply chains using the life cycle inventory database," Omega, Elsevier, vol. 75(C), pages 165-181.
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