MULTIDIMENSIONAL FINANCIAL RISK ASSESSMENT METHOD BASED ON HYBRID DEEP LEARNING

Abstract

This article addresses the challenge of providing a comprehensive assessment of financial risks for investors within the context of increasing dynamism and uncertainty in financial markets. The study substantiates the feasibility of employing advanced deep learning approaches for the intelligent analysis of risk factors emerging from the interplay of financial, macroeconomic, and behavioral drivers. It is demonstrated that traditional statistical and econometric models often fail to adequately capture the complex non-linear structure of financial processes, highlighting the urgent need for new methods focused on multidimensional data analysis and adaptive environmental responsiveness.

The authors propose a method for the multidimensional assessment of investors' financial risks based on hybrid deep learning models. This method integrates mechanisms for forming a consistent financial feature space, constructing latent representations of risk factors, and forecasting their dynamics. The approach is designed to combine diverse neural network architectures to more comprehensively reflect the temporal, structural, and contextual characteristics of financial data. Particular emphasis is placed on the integration of macroeconomic indicators and market conditions, which enhances the information density of the models and ensures a more realistic interpretation of risk processes.

The paper delineates the conceptual foundations for developing an integral financial risk indicator that synthesizes the results of multidimensional analysis and risk factor forecasting. The proposed approach facilitates adaptive parameter tuning of deep learning models in response to shifts in the financial environment, ensuring robust estimation and increased predictive value. Furthermore, the article details the algorithmic aspects of the method's implementation, establishing a framework for subsequent software development and experimental validation. The findings confirm the promise of hybrid deep learning models for the intelligent analysis of financial risks, as they provide a more holistic account of the interdependencies between financial metrics and external economic factors.