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

Abstract

In the era of the digital economy, financial management of publicly listed companies is increasingly influenced by large-scale, heterogeneous, and high-frequency data. Traditional statistical and econometric models often fail to capture nonlinear dependencies and temporal complexities inherent in such data, leading to suboptimal risk forecasting. This review paper explores the integration of deep learning and optimization techniques for enhancing risk forecasting in financial management. It focuses on advanced neural architectures such as recurrent neural networks, long short-term memory networks, convolutional neural networks, and transformer-based models, combined with optimization strategies including evolutionary algorithms, gradient-based tuning, and hybrid metaheuristic approaches. The study critically analyzes recent developments in enhanced deep learning networks tailored for financial risk prediction, including credit risk, market volatility, and liquidity risk. Furthermore, the role of big data analytics, feature engineering, and real-time processing in improving model robustness and forecasting accuracy is examined. The review identifies key challenges such as data imbalance, model interpretability, and computational complexity, while highlighting emerging trends such as explainable artificial intelligence and federated learning. The findings suggest that optimized deep learning frameworks significantly outperform traditional methods, offering improved predictive accuracy and decision support capabilities for financial stakeholders. This paper provides a comprehensive foundation for future research and practical implementation in risk-aware financial management systems.