Title: Optimisation of corporate financial risk early warning system based on blockchain and machine learning algorithm

Authors: Jiang Wang

Addresses: Chongqing Business Vocational College, Chongqing, 401131, Chongqing, China

Abstract: This paper applied blockchain and ML algorithm to the company's FR early warning system. This paper compared the effects of back propagation neural network (BPNN) and support vector machine (SVM) algorithm on corporate FR early warning. The experimental results showed that the average prediction accuracy of FR based on BPNN and SVM was 92.08% and 94.48% respectively according to the data of the sample company a year ago. According to the data of sample companies two years ago, the average prediction accuracy of FR based on BPNN and SVM was 89.56% and 91.96% respectively. Therefore, the application of SVM to the company's FR early warning could effectively improve the accuracy of FR prediction.

Keywords: corporate finance; risk warning; machine learning; ML; support vector machine; SVM; back propagation neural network; BPNN; blockchain technology.

DOI: 10.1504/IJCSYSE.2026.151396

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.318 - 326

Received: 07 Dec 2023
Accepted: 18 Jan 2024
Published online: 26 Jan 2026 *

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