Title: Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm

Authors: Danyan Zhong

Addresses: Accounting Department, Changzhou University Huaide College, Changzhou, 214500, China

Abstract: With the increasing share of e-commerce business, many companies are also facing credit risk issues of varying degrees. Aiming at the problem of credit risk assessment, this study proposes an improved multi-objective clustering algorithm to assess corporate credit risk. By comparing the conventional FMC algorithm and K-means algorithm, the performance of the proposed improved MOEC algorithm is analysed. It can be seen from the PR curves of the three algorithms that the AP values of the three algorithms are 0.9324, 0.9455, and 0.9972, respectively. In contrast, the improved MOEC algorithm has higher accuracy and stability. Tested on the UCI dataset, it was found that in dataset 3, the RI value of the improved MOEC algorithm was 0.97; in dataset 5, the NMI value of the improved MOEC algorithm was 0.96.

Keywords: e-commerce; corporate credit; clustering algorithm; multi-objective; risk assessment; finance.

DOI: 10.1504/IJCSYSE.2023.132916

International Journal of Computational Systems Engineering, 2023 Vol.7 No.2/3/4, pp.168 - 176

Received: 22 Nov 2022
Accepted: 12 Mar 2023
Published online: 16 Aug 2023 *

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