Design of Financial Risk Warning System in Digital Economy Based on Deep Neural Network

다국어 초록 (Multilingual Abstract)

Financial risk assessment in the digital economy demands an adaptive, data-driven approach that integrates computational intelligence with economic stability analysis. Traditional risk prediction models rely on static indicators and predefined economic thresholds, limiting their generalization ability across evolving market conditions. In this study, we propose Warning System-Evidential Stability Analysis (WS-ESA), a deep neural network-based framework designed to optimize financial risk warnings through dynamic investment stability permutations. The system constructs a multi-phase risk evaluation pipeline where past investment patterns, break-even points, and market downturns are systematically encoded as training features. Unlike conventional machine learning models, our approach leverages evidential stability permutations that iteratively refine hidden-layer weight updates based on stochastic investment scenarios. The network continuously restructures its feature space to capture high-variance market uncertainties and investment stability transitions by employing a permutation-driven learning mechanism. The training phase integrates supervised and permutation-enhanced reinforcement learning strategies, enabling the detection of latent financial anomalies. Identified risk states are dynamically classified into early warning signals, market stability scores, and return-based risk projections, enhancing predictive accuracy beyond static models. The proposed WS-ESA system establishes a novel risk modeling paradigm synthesizing deep learning with financial evidence permutation, providing an adaptive and resilient warning mechanism for digital economy frameworks.