Intelligent Identification and Early Warning Model forCorporate Financial Risk Based on Deep Learning

In the rapidly evolving landscape of computational sciences, the integration of advanced modeling techniques for financialrisk assessment has become increasingly pivotal. This study addresses the pressing need for sophisticated analyticalframeworks capable of capturing the multifaceted nature of corporate financial risk, aligning with the interdisciplinary focus ofcontemporary computer science research. Traditional econometric models often fall short in encapsulating the dynamic andinterconnected financial ecosystems of modern enterprises. These conventional approaches typically lack the adaptability anddepth required to model the stochastic behaviors and systemic interdependencies inherent in financial networks. To overcomethese limitations, we introduce a novel methodological framework that synergizes deep learning with structured dynamicmodeling to enhance financial risk identification and early warning systems. Our approach, termed RiskFlowNet, employsa generative latent factor model incorporating structured Kronecker-based factorization within a recurrent matrix evolutioncontext. This design facilitates efficient temporal memory capture and systemic cross-exposure analysis in high-dimensionalcorporate networks. Complementing this, we develop an Adaptive Cross-Sector Embedding strategy that robustly encodesdomain-specific structures of corporate finance, including sectoral hierarchies and market co-movements. This strategy enablesthe model to adapt to shifts in economic regimes and capital flow reallocations, ensuring context-sensitive parameterization andassimilation of non-stationary behaviors. Experimental results demonstrate the framework’s superiority in predictive accuracyand robustness over traditional models, highlighting its potential as a transformative tool in computational finance. Thiswork not only advances the field of financial risk modeling but also exemplifies the application of cutting-edge computationaltechniques to complex, real-world problems, resonating with the core objectives of interdisciplinary computer science research.