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Risk Identification Model of Enterprise Strategic Financing Based on Online Learning

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Advanced Hybrid Information Processing (ADHIP 2022)

Abstract

The current financing risk identification model only identifies according to the financial situation of enterprises. The limitations of risk indicators affect the accuracy of identification, and have great limitations for enterprises in different industries. In view of the above problems, this paper will study and build an enterprise strategic financing risk identification model based on online learning. After analyzing the causes of enterprise strategic financing risk, the financing risk identification index system is designed. Based on the structure of impulse neural network, the risk identification model is constructed by using online learning algorithm. The model test results show that the minimum identification accuracy of the model is 92.4%, which has a good risk identification effect for enterprises in different industries.

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Correspondence to Xiang Zou .

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Jiangsu University “Qinglan Project” Outstanding Young Backbone Teacher Training Project; Wuxi Commercial Vocational and Technical College provincial and ministerial-level and above subject cultivation project (KJXJ21602); Wuxi Commercial Vocational and Technical College doctoral research start-up project (RS18BS03); Wuxi Commercial Vocational and Technical College “Eagle Program” training project (RS21CY04).

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zou, X., Yuan, H. (2023). Risk Identification Model of Enterprise Strategic Financing Based on Online Learning. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-28787-9_52

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  • DOI: https://doi.org/10.1007/978-3-031-28787-9_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28786-2

  • Online ISBN: 978-3-031-28787-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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