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

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.