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Construction and Empirical Study of a Financial Risk Early Warning Model for Enterprises

In: Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025)

Author

Listed:
  • Mei Zhang

    (Xi’an Eurasia University)

Abstract

This study aims to develop and validate a financial risk early warning model for enterprises based on a deep neural network. By extracting key financial and non-financial indicators, the model leverages deep learning algorithms to predict financial risk in enterprises. Data from selected Chinese listed companies from 2015 to 2020 were processed with feature selection and standardization before being fed into the deep neural network model. Cross-validation and multiple evaluation metrics were used to assess model performance. Experimental results demonstrate that the model performs excellently in terms of accuracy, precision, and recall, showing high capability in financial risk identification. The study identifies debt-to-asset ratio and net profit margin as significant influencing factors. This model provides an effective tool for financial risk early warning, with substantial practical implications for enterprise risk management and decision-making.

Suggested Citation

  • Mei Zhang, 2025. "Construction and Empirical Study of a Financial Risk Early Warning Model for Enterprises," Advances in Economics, Business and Management Research, in: Huaping Sun & Hang Luo & Vilas Gaikar & Natālija Cudečka-Puriņa (ed.), Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025), pages 85-91, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-734-2_10
    DOI: 10.2991/978-94-6463-734-2_10
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