THE ROLE OF BIG DATA IN FORECASTING ECONOMIC CRISES

Modern economic crises develop with high speed and complexity, which requires new methods of forecasting and early risk detection. The use of big data technologies makes it possible to analyze complex economic processes in real time, identify hidden patterns of crises and predict possible macroeconomic shocks. The application of machine learning, neural network models and processing of high-frequency financial data significantly increases the accuracy of forecasting crisis scenarios, allowing to take proactive measures. The paper explores the role of big data in volatility detection, examining the main sources of information, analytics techniques and limitations of algorithmic predictions. Successful examples of using big data by central banks and financial regulators are analyzed, as well as the prospects of integrating predictive models into economic policy.