A Comprehensive Review of Risk Forecasting in Financial Management of Publicly Listed Companies Using an Enhanced Deep Learning Network within the Digital Economy

Abstract

The rapid evolution of the digital economy has significantly transformed financial management practices in publicly listed companies, necessitating advanced methodologies for accurate risk forecasting. Traditional statistical and econometric models often fail to capture nonlinear dependencies, temporal dynamics, and high-dimensional interactions present in financial data. This paper presents a comprehensive review of risk forecasting techniques with a particular focus on enhanced deep learning networks. The study explores the integration of advanced architectures such as recurrent neural networks, long short-term memory models, convolutional neural networks, and attention-based mechanisms for predicting financial risks including credit risk, market volatility, and bankruptcy probability. Furthermore, the role of big data analytics, cloud computing, and real-time financial streams in improving predictive accuracy is examined. The review highlights how hybrid and optimized deep learning frameworks outperform conventional approaches by leveraging feature representation learning and adaptive optimization techniques. Key challenges such as model interpretability, data quality, and computational complexity are also discussed. The findings suggest that enhanced deep learning models, when integrated with financial domain knowledge, offer significant improvements in forecasting accuracy and decision-making efficiency. This study provides a structured foundation for future research and practical implementation in intelligent financial risk management systems within digitally driven corporate environments.