ANALYSIS OF THE APPLICATION FEATURES OF PROBABILISTIC AND STATISTICAL METHODS OF DATA PROCESSING FOR FORECASTING AND MODELING CRISIS SITUATIONS

This article explores the role of probabilistic and statistical methods in crisis management. It provides an analysis of the most effective approaches, including machine learning, Monte Carlo simulations, time series analysis, probabilistic graphical models, and natural language processing. The study examines the advantages and limitations of each method, as well as their potential applications in modelling crisis situations, risk assessment, and decision-making. The article also outlines the prospects for integrating these approaches with modern technologies to enhance the effectiveness of managerial decisions under complex conditions.Problem. In today’s world, traditional approaches to crisis management are often insufficient due to the complexity of analysing large volumes of data and the rapid pace of situational change. This necessitates the implementation of innovative tools that enable real-time analysis, scenario   modelling, and risk assessment.The purpose of this work the aim of this study is to analyze the effectiveness of probabilistic-statistical methods in crisis management, to investigate their advantages and disadvantages, and to identify the prospects for applying these approaches in various fields to improve the decision-making process in crisis situations. Methods of Research. The study employed a comprehensive approach combining theoretical analysis, empirical observations, and comparative analysis. Current trends in the application of probabilistic-statistical methods were examined. The comparative analysis enabled the evaluation of the effectiveness of various approaches in crisis management. The empirical part included the analysis of time series and textual data to model risks under conditions of uncertainty. This comprehensive approach provided a thorough assessment of the role of probabilistic-statistical methods in crisis management.Results. The results of the study demonstrated that probabilistic-statistical methods are effective tools for crisis management. In particular, these methods enable: modeling scenarios of event development under crisis conditions; assessing levels of risk and uncertainty; optimizing managerial decisions under time constraints; integrating modern technologies such as artificial intelligence and big data to enhance the accuracy of analysis.