Research on Risk Assessment of Internal Audit Information in Enterprises through Data Mining


Lu Xia, Tiantian Wang, Journal of Information Processing Systems Vol. 21, No. 6, pp. 575-584, Dec. 2025  

https://doi.org/10.3745/JIPS.04.0361
Keywords: Audit Information, Data Mining, Deep Learning, Risk Identification
Fulltext:

Abstract

This paper initially segmented the audit information samples using a clustering algorithm. Subsequently, a backpropagation neural network (BPNN) algorithm enhanced by the beetle antennae search (BAS) algorithm was used for risk assessment. Financial report data was crawled from listed companies for a case analysis to assess the impact of the number of clustering centers on the clustering algorithm and the effect of the activation function type on the improved BPNN algorithm. Additionally, the audit information risk identification performance of the support vector machine (SVM), traditional BPNN, sparse autoencoder-BPNN, and the improved BPNN algorithm was compared. The findings revealed that the clustering algorithm demonstrated optimal sample division performance when utilizing two clustering centers. Moreover, the improved BPNN algorithm exhibited superior performance under the sigmoid activation function, outperforming both SVM and traditional BPNN algorithms.


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Cite this article
[APA Style]
Xia, L. & Wang, T. (2025). Research on Risk Assessment of Internal Audit Information in Enterprises through Data Mining. Journal of Information Processing Systems, 21(6), 575-584. DOI: 10.3745/JIPS.04.0361.

[IEEE Style]
L. Xia and T. Wang, "Research on Risk Assessment of Internal Audit Information in Enterprises through Data Mining," Journal of Information Processing Systems, vol. 21, no. 6, pp. 575-584, 2025. DOI: 10.3745/JIPS.04.0361.

[ACM Style]
Lu Xia and Tiantian Wang. 2025. Research on Risk Assessment of Internal Audit Information in Enterprises through Data Mining. Journal of Information Processing Systems, 21, 6, (2025), 575-584. DOI: 10.3745/JIPS.04.0361.