Article: Study on accurate perception for enterprise financial risk based on stacking ensemble learning Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.6 pp.19 - 28 Abstract: To address the issues of high Matthews correlation coefficient, high Brier scores and low accuracy of perception in traditional methods, an accurate perception method for enterprise financial risk based on Stacking ensemble learning is proposed. Using the improved SMOTE to obtain a balanced data set of enterprise financial data, and based on the Boruta algorithm to screen the features related to financial risk in the balanced data set, using stacking ensemble learning technology and combining the prediction results of four base models to train a meta model, the obtained features are input into the trained meta model to obtain risk prediction results. Based on the prediction results, financial risks are classified into five levels to achieve enterprise financial risk perception. The experimental results show that the Matthews correlation coefficient of this method has never been lower than 0.90, the minimum Brier score is only 0.071 and the maximum perception accuracy is 98.36%. Inderscience Publishers - linking academia, business and industry through research

Title: Study on accurate perception for enterprise financial risk based on stacking ensemble learning

Authors: Wenlu Chu

Addresses: Health Management Department, Zhengzhou Health College, Zhengzhou, Henan, China

Abstract: To address the issues of high Matthews correlation coefficient, high Brier scores and low accuracy of perception in traditional methods, an accurate perception method for enterprise financial risk based on Stacking ensemble learning is proposed. Using the improved SMOTE to obtain a balanced data set of enterprise financial data, and based on the Boruta algorithm to screen the features related to financial risk in the balanced data set, using stacking ensemble learning technology and combining the prediction results of four base models to train a meta model, the obtained features are input into the trained meta model to obtain risk prediction results. Based on the prediction results, financial risks are classified into five levels to achieve enterprise financial risk perception. The experimental results show that the Matthews correlation coefficient of this method has never been lower than 0.90, the minimum Brier score is only 0.071 and the maximum perception accuracy is 98.36%.

Keywords: enterprise financial risk; risk perception; SMOTE; Boruta algorithm; stacking ensemble learning.

DOI: 10.1504/IJCAT.2026.153746

International Journal of Computer Applications in Technology, 2026 Vol.78 No.6, pp.19 - 28

Received: 13 Aug 2025
Accepted: 28 Nov 2025
Published online: 22 May 2026 *

Title: Study on accurate perception for enterprise financial risk based on stacking ensemble learning

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