Deep Learning and Optimization Approaches for Intelligent Risk Forecasting in Financial Management within the Digital Economy

Abstract

In the rapidly evolving digital economy, financial risk forecasting has become an essential component for ensuring the sustainability and competitiveness of publicly listed companies. Traditional statistical and econometric approaches often fail to capture the nonlinear, high-dimensional, and temporal dependencies inherent in modern financial datasets. This study explores the integration of deep learning techniques with optimization strategies to enhance the accuracy and robustness of financial risk forecasting systems. By leveraging architectures such as recurrent neural networks, convolutional neural networks, and hybrid deep learning frameworks, the proposed approach enables effective modeling of complex financial patterns. Furthermore, optimization techniques, including metaheuristic algorithms and gradient-based tuning, are incorporated to refine model performance and convergence efficiency. The research highlights the importance of data preprocessing, feature engineering, and model interpretability in achieving reliable predictions. A comprehensive analysis demonstrates that the integration of deep learning and optimization significantly improves predictive accuracy compared to traditional methods. The study also emphasizes the practical implications of these models in supporting strategic financial decision-making, risk mitigation, and policy formulation. Overall, this paper contributes to the growing body of knowledge by presenting a structured framework for intelligent risk forecasting, addressing both methodological advancements and real-world applicability within the context of the digital economy.