Regression analysis of supply chain financial risk based on machine learning and fuzzy decision model

Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications

Guest editors: Varatharajan Ramachandran

Article type: Research Article

Authors: Xuan, Fan^{; *}

Affiliations: Dongguan Polytechnic, Guangdong, China

Correspondence: [*] Corresponding author. Fan Xuan, Dongguan Polytechnic, Guangdong, China. E-mail: fanxuan001258@163.com.

Abstract: Supply chain financial risk control has become the biggest obstacle restricting financial institutions from developing supply chain financial services. If financial institutions want to make a difference in the supply chain finance business, they need to implement strong control measures against supply chain finance risks. Based on the research of supply chain finance and risk related theories, this paper uses fuzzy preference relations and selects the main risk criteria to construct a risk evaluation index system. Moreover, this paper takes the development of J company’s supply chain finance business as a background to conduct an empirical analysis and proposes the risk management measures and development strategies of J company’s actual supply chain finance business. The combination of quantitative analysis and qualitative evaluation is more comprehensive and operational value for the research and management of supply chain financial risks. In addition, this paper uses the evaluation index system constructed in this paper to conduct a more comprehensive summary and analysis of the internal and external environment of risk influencing factors. The research results show that the performance of the model constructed in this paper is good.

Keywords: Machine learning, supply chain finance, risk regression, improved algorithm

DOI: 10.3233/JIFS-189523

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6925-6935, 2021