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Abnormal recognition of corporate financial data based on deep belief network

Author

Listed:
  • Xi Lun
  • Xiangyang Zhang
  • Yining Wang
  • Tian Wang

Abstract

In view of the traditional enterprise financial data exception recognition methods, such as low recognition precision and long recognition time, a deep belief network is put forward. Based on the depth of the enterprise's financial data anomaly identification method, the distributed data collection method, selection of enterprise financial data mining, and correlation analysis are adopted, according to the financial data sample information entropy, to divide the financial data flow. According to the extraction results, use the deep belief network to build a financial data anomaly recognition model. The financial data of enterprises are input into the abnormal identification model of financial data to identify the status of financial data. Experimental results show that this method has higher recognition accuracy and shorter recognition time.

Suggested Citation

  • Xi Lun & Xiangyang Zhang & Yining Wang & Tian Wang, 2023. "Abnormal recognition of corporate financial data based on deep belief network," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 45(2), pages 135-147.
    • Handle: RePEc:ids:ijisen:v:45:y:2023:i:2:p:135-147
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