MANAGEMENT OF THE BANK'S CREDIT RISK ON THE FINANCIAL MARKET: THEORETICAL ASPECT
Abstract
The article is devoted to the study of theoretical aspects of the credit risk management system of a banking institution on the financial market and the evaluation of the components of this system in modern conditions of socio-economic development. The relevance of the topic is emphasized by the fact that the profitability of a banking institution depends mainly on its credit activity, which creates credit risk. Emphasis is placed on the need to use a systematic approach to credit risk management. The main components of the credit risk management system are defined, namely subjects, object, security subsystems, management methods and tools, principles, management culture and code of conduct. The main principles on which the credit risk management process of a banking institution should be based are considered. It is noted that credit risk management is a dynamic process that consists of a series of sequential actions divided into stages, which, in turn, are closely related and influence each other. A three-level model of credit risk management is outlined and the advantages of using such a model are indicated. The main methods of managing the bank's credit risk are considered, which include diversification, limiting (restrictions), insurance, creation of reserves, credit security and regulations. Attention is focused on the integration of artificial intelligence (Artificial Intelligence) and machine learning (Machine Learning) in the banking sector in terms of credit risk management and it is determined that the main advantages of using such technologies are the ability to process huge amounts of data, including non-traditional data sources; using algorithms to study data, identify patterns and make decisions, which means better risk assessment by taking into account multiple factors; providing real-time analytics, allowing for faster and more accurate credit decisions; reducing people's bias in making credit decisions and increasing fairness; implementation of predictive analytics and risk reduction.
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