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Judgement method of enterprise financial data abnormality based on high-order dynamic Bayesian network

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  • Lili Wang

Abstract

This paper proposes a judgement method of enterprise financial data anomaly based on high-order dynamic Bayesian network. Firstly, the enterprise financial data is divided into normal data and abnormal data, and the original training samples are classified to obtain the data classification results. Input the classification results into the enterprise financial data management platform based on cloud computing to improve the efficiency of data anomaly judgement. The high-order dynamic Bayesian network is used to initialise and modify the network, and the chromosome coding method is used to realise the abnormal judgement of enterprise financial data. The experimental results show that the method has a higher accuracy rate of anomaly judgement, and a lower miss rate and error rate.

Suggested Citation

  • Lili Wang, 2023. "Judgement method of enterprise financial data abnormality based on high-order dynamic Bayesian network," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 44(3), pages 369-379.
    • Handle: RePEc:ids:ijisen:v:44:y:2023:i:3:p:369-379
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