Title: Enterprise hidden financial information extraction method based on data source dependency

Authors: Jingyi Li

Addresses: School of Accounting, Jiaozuo University, Jiaozuo, 454000, China

Abstract: The significance of hidden financial information extraction research lies in the discovery of corporate financial fraud. In order to address the shortcomings of traditional methods such as low recall and precision, and long time overhead, therefore an enterprise hidden financial information extraction method based on data source dependency is proposed. By determining the redundancy of enterprise financial information through data source dependency relationships, the data source dependency relationships are cleaned, and the cleaned data is input into the RDCNN-CRF model to achieve information label classification. Combined with the label classification results, an ordered long-short-term memory-multi-head attention mechanism neural network model is constructed, and the processed data is input into this model. The model output is the result of extracting hidden financial information. The experimental results show that the mean recall rate and precision rate of the proposed method are 97.92% and 98.07%, and the maximum time consumption is 0.82 s.

Keywords: data source dependency; enterprise; hidden financial information; information extraction; redundancy; RDCNN-CRF model.

DOI: 10.1504/IJRIS.2025.148710

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.5, pp.317 - 325

Received: 05 Jun 2023
Accepted: 12 Jul 2023
Published online: 21 Sep 2025 *

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