System approaches to financial and analytical modeling of management decisions using digital technologies

Abstract

The theoretical and practical principles of integrating financial analysis with the process of making managerial decisions in conditions of dynamic economic development are considered. The relevance of the study is due to the high level of uncertainty of the market environment, increased competition and the increased role of risks in the strategic development of enterprises. The purpose of the article is to substantiate the conceptual approach to the integration of classical financial and analytical tools with modern methods of predictive modeling and digital technologies to form an effective system of support for managerial decisions. To achieve the set goal, comparative analysis (to assess the strengths and weaknesses of traditional approaches to financial analysis), factor analysis (to identify the main determinants of changes in financial indicators), econometric methods (to build quantitative dependencies and make forecast calculations), scenario modeling (to take into account the variability of enterprise development in conditions of risk) were used. The scientific novelty of the article lies in the development of a conceptual model of financial analysis, which combines classical methods with tools of the digital economy. The introduction of Big Data technologies for processing large amounts of information, artificial intelligence algorithms and machine learning for forecasting financial results and generating development scenarios, as well as the use of interactive digital platforms to provide access to key indicators in real time are proposed. The practical significance of the results lies in the possibility of their application in the field of corporate governance to optimize financial decisions. The proposed approach creates the prerequisites for the formation of an integrated analytical system that contributes to increasing the efficiency of enterprise management and ensuring their financial stability in the long term.