BANKING RISK FORECASTING BASED ON ARTIFICIAL INTELLIGENCE

Abstract

This scientific article analyzes the role and economic efficiency of artificial intelligence technologies in risk forecasting and management within the banking system. The study scientifically examines the possibilities of using machine learning, neural networks, big data, and predictive analytics methods to identify credit risk, liquidity risk, operational risk, and fraud risks. It also highlights the advantages of AI-based algorithms in the rapid and accurate assessment of risks compared to traditional methods in the modern banking system. The article analyzes the experience of artificial intelligence technologies used in international banking practice, including the risk management systems of JPMorgan Chase, HSBC, and other major financial institutions. During the study, the possibilities of digital transformation processes in the banking sector of Uzbekistan, automation of banking databases, and the introduction of artificial intelligence technologies were assessed. Research results indicate that AI-based forecasting systems are crucial for early detection of banking risks, reducing the share of non-performing loans, and strengthening financial stability. At the same time, the article develops scientific and practical proposals for developing digital infrastructure, strengthening cybersecurity, and training qualified IT specialists for the effective use of artificial intelligence in the banking system of Uzbekistan