Title: Corporate financial risk prediction model based on deep learning

Authors: Fei Xue

Addresses: Zhumadian Preschool Education College, Zhumadian, Henan, China

Abstract: The correlation between financial success and Corporate Social Responsibility (CSR) has been the subject of extensive research in recent years. While prior studies have explored this relationship, objectively assessing the effectiveness of CSR programs has remained a considerable challenge. However, advancements in research methodologies and the development of Environmental, Social and Governance (ESG) measurement criteria have facilitated more reliable evaluations. With the rapid advancement of artificial intelligence, Deep Learning (DL) techniques have been increasingly applied across various domains. The application of machine learning to ESG data analysis remains relatively limited. Consequently, deep learning methodologies have proven to be practical, efficient and effective approaches for predicting corporate financial performance based on ESG metrics. Through risk management optimisation, accuracy and recall rates have improved to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test data sets, respectively.

Keywords: KPI; key performance indicator; SDG; sustainable development goal; ROE; return on equity; SNNs; spiking neural networks; ANN; artificial neural network; deep learning; machine learning.

DOI: 10.1504/IJIPT.2025.150235

International Journal of Internet Protocol Technology, 2025 Vol.18 No.4, pp.186 - 198

Received: 24 Mar 2025
Accepted: 26 Apr 2025
Published online: 04 Dec 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article