Use of modern financial and economic analysis methods for forecasting business profitability

Abstract

Relevance of the study is determined by the need to improve approaches to forecasting business profitability in the context of heightened economic turbulence, digital transformation, and external instability caused by wartime risks. Traditional analytical models lose effectiveness due to their inability to account for rapidly changing internal and external factors, which highlights the importance of integrating digital technologies and adaptive forecasting tools. The purpose of the study is to scientifically substantiate the advantages of using modern financial and economic analysis methods as a tool to enhance the accuracy of business profitability forecasting and to develop practical approaches for ensuring stable performance and sustainable development under conditions of uncertainty and risk. Research methodology is based on the application of systems analysis, econometric modeling, factor analysis, and the use of integrated performance indicators. The study also involves digital data processing tools, analytical software, and scenario forecasting models. Results. It has been established that the application of modern analysis methods significantly improves the accuracy of financial forecasts. The effectiveness of econometric models and real-time digital platforms has been proven. It has been revealed that the integration of ERP, BI, and ML solutions facilitates the automation of forecasting processes and reduces the influence of subjective factors in management decisions. Scientific novelty lies in the combination of traditional financial instruments with digital analytical systems to create adaptive profitability models capable of functioning in unstable market environments. Conceptual foundations for transitioning to digital forecasting tailored to the sector-specific needs of Ukrainian enterprises have been proposed. Conclusions. It has been generalized that adapting modern financial and economic analysis methods is a key factor in strengthening business resilience. Barriers to the implementation of analytical technologies have been identified, including a lack of qualified personnel, fragmented information infrastructure, and limited access to quality data. Prospects for further research include the development of hybrid forecasting models that combine artificial intelligence tools, integrated financial indicators, and algorithms for automatic adjustment under conditions of uncertainty and constrained resources.