Title: Enterprise financial risk early warning method based on PCA and SVM algorithms

Authors: Yanya Cao; Gechun Pei

Addresses: College of Architecture, Xinxiang Vocational and Technical College, Xinxiang, 453006, China ' College of Architecture, Xinxiang Vocational and Technical College, Xinxiang, 453006, China

Abstract: Aiming at the problems of low relevance and high false alarm rate of enterprise financial risk early warning, an enterprise financial risk early warning method based on PCA and SVM algorithm is proposed. Firstly, the sensitivity optimisation principal component analysis method is introduced, and the representative index is selected according to the threshold value to establish the index system. Then, support vector machine is introduced to store the data in the sample space, and the indicators are divided into positive and negative indicators. Finally, combined with FCM clustering algorithm, the early-warning decision function is constructed to realise the early-warning of enterprise financial risk. The experimental results show that the correlation of this method is higher than 0.915, the false alarm rate is lower than 2%, and the Matthews correlation coefficient is up to 1.00.

Keywords: principal component analysis; PCA; support vector machine; SVM; corporate financial risks; risk warning; FCM clustering algorithm.

DOI: 10.1504/IJBIDM.2025.143923

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.1/2, pp.1 - 18

Received: 20 Nov 2023
Accepted: 02 May 2024
Published online: 14 Jan 2025 *

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