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

DOI

10.2991/978-94-6463-734-2_10How to use a DOI?

Keywords

Enterprise financial risk; Deep neural network; Early warning model; Feature selection; Prediction

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mei Zhang
PY  - 2025
DA  - 2025/05/27
TI  - Construction and Empirical Study of a Financial Risk Early Warning Model for Enterprises
BT  - Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025)
PB  - Atlantis Press
SP  - 85
EP  - 91
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-734-2_10
DO  - 10.2991/978-94-6463-734-2_10
ID  - Zhang2025
ER  -