INTEGRATION OF FINANCIAL RISK ASSESSMENT METHODS USING ARTIFICIAL INTELLIGENCE AND BIG DATA

Abstract

It has been established that the integration of artificial intelligence (AI) methods and big data technologies makes it possible to increase the accuracy of financial risk forecasting. The effectiveness of using neural network models for analyzing credit risk and market volatility has been evaluated. It was revealed that the use of hybrid machine learning algorithms reduces the likelihood of errors in the classification of problem assets. The main approaches to building early warning systems based on the analysis of unstructured data are described. The possibilities of adapting the developed methods to the conditions of the financial market of Uzbekistan are considered.