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Application of Artificial Intelligence Data Mining Algorithm in Enterprise Management Risk Assessment

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  • Juntao Zhu

    (Zhengzhou Railway Vocational and Technical College, China)

Abstract

For governmental and non-governmental enterprises to tackle risk management with conviction, enterprise management risk assessment (EMRA) is required. This work proposes a research methodology based on an AI-based data mining algorithm (MSVM+EFCNN) for evaluating enterprise-related risks. Initially, all the possible risk assessment indexes of the enterprise are established using a large variety of identification parameters. Then, the data mining algorithms are trained by considering the previous data for building an EMRA model. At last, the current conditions are analyzed using a cluster of risk indicators, and the risk index is identified via the EMRA model. The support vector machine is used for classification purposes, and the fuzzy-based convolutional neural network is enhanced with a genetic algorithm for creating the enterprise risk assessment. The results obtained after keen analysis and experimentation indicate that the data mining algorithms used in this work can evaluate the enterprise-related risks effectively.

Suggested Citation

  • Juntao Zhu, 2024. "Application of Artificial Intelligence Data Mining Algorithm in Enterprise Management Risk Assessment," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:igg:jisscm:v:17:y:2024:i:1:p:1-19
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    References listed on IDEAS

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    1. Adel A. Ahmed & Sharaf J. Malebary & Waleed Ali & Omar M. Barukab, 2023. "Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology," Mathematics, MDPI, vol. 11(3), pages 1-20, January.
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