Risk Assessment of Corporate Financial Internal Control and Audit Matters: Data Mining

Authors

  • Shu Ren Anyang University, Anyang City, Henan 455000, China

Abstract

Internal financial audits within enterprises are beneficial for planning the operations of enterprises and enhancing their market competitiveness. In this study, the random forest (RF) algorithm was combined with the back-propagation neural network (BPNN) algorithm to improve financial risk recognition and auditing accuracy. The RF algorithm was used to screen the main financial risk indicators, and the BPNN algorithm was employed to identify the financial risk. The RF algorithm calculated the importance of financial risk indicators in the simulation experiment. Then, the performance of the RF algorithm, traditional BPNN algorithm, and the improved BPNN algorithm was compared. The results showed that the RF algorithm effectively screened out the characteristics of important financial risk indicators. Compared with the single RF algorithm and the single BPNN algorithm, the
improved BPNN algorithm had better recognition accuracy and shorter recognition time.

Keywords: financial risk, audit, data mining, random forest

Cite As

S. Ren, "Risk Assessment of Corporate Financial Internal Control and Audit Matters: Data Mining",
Engineering Intelligent Systems, vol. 33 no. 6, pp. 733-737, 2025.

 

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Published

2025-11-01

Issue

Vol. 33 No. 6 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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