Author(s):
Shuping Luo, Ziying Zhang, Shuwen Yang, Huayan Cai

Doi: 10.7508/aiem.01.2023.10.13

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

The economic environment is constantly changing in the course of rapid economic development, with more opportunities and more risks for companies. In the unpredictable market environment and fierce business competition, it is particularly important to establish a flexible and efficient early warning mechanism for financial risks. The zeta model and the Fisher linear discriminant model proposed in the existing research results have both passed empirical tests and have been applied in practice for many years, but the effectiveness and stability of early warning still needs to be improved and the failure of early warning mechanisms has become the norm. The geometric growth of large volumes of structured and unstructured data requires a multidimensional, all-encompassing, high-velocity early warning mechanism for financial risks. The emergence of big data technology and artificial intelligence has provided ideas and conditions for the establishment of new models of financial early warning. This paper analyses the financial risk warning situation of Company X by constructing a BP neural network model to obtain the financial risk warning situation from 2016 to 2021 respectively, analyses the internal and external factors leading to the emergence of financial risks, and gives corresponding countermeasure suggestions in a targeted manner.

KEYWORDS:
Neural network model, financial risk, warning