Role of the Statistical Analysis in the Financial Management System Under Uncertainty

Abstract

The article is devoted to the problem of using the methods of statistical analysis in the financial management processes in the conditions of uncertainty. A review of theoretical studies showed that efficiency of decisions pertaining to the financial management depended on the quality of uncertainty assessment. The appropriate method for assessment of alternative financial instruments of investment involves a broad range of mathematical tools based on the probability theory and main provisions of investment analysis. The role of statistical analysis as a principal tool within the financial management system in the conditions of economic and political uncertainty was explored. Present-day global challenges, especially economic crises, military conflicts, volatility of markets, and inflation risks, necessitate analytical approaches capable to support well-informed financial decisions. Statistical analysis allows to reveal the patterns in the dynamics of financial indicators, to identify risks, to predict incomes and expenditures, and to improve the adaptability of financial strategy at company level to changes in the external business environment. The emphasis is placed on using statistical models in scenario assessment and optimization of cost structure in business environments with high uncertainty. Examples of practical applications of statistical methods in budgeting and liquidity management, and in analyses of investment attractiveness are given. It is concluded that statistical approaches introduced in the financial management, apart from reducing the impact of subjective factors, ensure flexibility and resilience of the financial policy at company level in the conditions of unstable business environment. Statistical analysis is a critically important component in the system for support of management decisions, because it helps minimize the losses and maximize the efficiency of resource utilization. Future studies will focus on exploring the feasibility of using machine learning and artificial intelligence in a combination of statistical analysis for forecasting of financial risks and optimization of management decisions.