Title: An enterprise financial credit risk measurement method based on differential evolution algorithm

Authors: Lixia Du; Xin An

Addresses: Department of Economic Management, Xinzhou Teachers University, Xinzhou, 034000, Shanxi, China ' College of Economics and Management, Handan University, Handan, 056005, Hebei, China

Abstract: In order to reduce the time cost and risk misjudgement rate of financial information risk measurement, this paper proposes a new enterprise financial credit risk measurement method based on differential evolution algorithm. Firstly, after preprocessing the enterprise financial credit risk data and determining the location of the clustering centre, a differential evolution automatic clustering model is constructed. Secondly, according to the clustering results, the differential evolution algorithm is used to measure the basic process of enterprise financial credit risk. Finally, the improved differential evolution algorithm is used for iterative measurement to achieve enterprise financial credit risk data measurement. The experimental results show that the time cost of the proposed method for enterprise financial credit risk measurement can be controlled within 0.4 s, and the error rate is not more than 1% under the condition of 1,000 data.

Keywords: differential evolution algorithm; corporate finance; credit risks; measurement method.

DOI: 10.1504/IJITM.2025.144106

International Journal of Information Technology and Management, 2025 Vol.24 No.1/2, pp.67 - 77

Received: 14 Jun 2022
Accepted: 23 Sep 2022
Published online: 28 Jan 2025 *

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