Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-284
Identifying Financial Fraud in Listed Companies by Integrating Financial Text and Unstructured Data
1 Lecturer, Pingdingshan Polytechnic College, Pingdingshan, 467001, China, E-mail:
pengdongdp@outlook.com (corresponding author).
2 Lecturer, Management School, Henan University of Urban Construction,
Pingdingshan, 467036, China.
Project Management
Received November 22, 2025; revised February 13, 2026; accepted February 20, 2026
Available online March 7, 2026
Abstract: To effectively identify Financial Fraud (FF) in Listed Companies (LC), the study combined financial, non-financial, and unstructured data to develop an initial set of financial indicators. To further enhance the fraud detection model’s efficacy, the indications were screened using chi-square tests and correlation coefficients. Convolutional Neural Networks (CNN) and bidirectional long short-term memory networks were merged in the study’s model development, and an attention mechanism was added to emphasize important details. The outcome revealed that the average accuracy of the research design identification model was 97.48%, which was much higher than that of the comparison models (82.56%, 88.17%, 90.13%, and 91.17%). In addition, the maximum precision of this model was 98.49% and the average recall rate was 97.19%, both of which were superior to those of the comparison models. In summary, models that integrate multi-source data are better able to identify financial fraud in LC and provide strong technical support for maintaining the normal order of the securities market.
Keywords: Unstructured data, financial fraud, identification, BiLSTM, CNN, chi-square test.
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Citation: Peng, D. and Yang, L. (2026). Identifying Financial Fraud in Listed Companies by Integrating Financial Text and Unstructured Data. Journal of Engineering, Project, and Production Management, 16(2), 2025-284.
