Application of artificial intelligence in automated management systems of financial institutions

Abstract

The modern financial system operates in conditions of significant uncertainty, digital transformation and growing data volumes. For banks and financial institutions, reliable financial risk management is becoming a key task, which requires the introduction of intelligent data analysis and forecasting technologies. One of the most promising areas for solving the problem is the use of artificial intelligence technologies that can automate risk assessment, find anomalies and increase the accu-racy of management decisions. A conceptual architecture of an intelligent analytical system has been developed that integrates machine learning algorithms to assess cus-tomer creditworthiness and identify potential defaults. The object of the study is au-tomated systems for managing financial risks of banking institutions. The subject of the study is algorithms, models and methods of artificial intelligence used to analyze, forecast and minimize financial risks. The paper analyzes modern approaches to the use of ensemble methods and deep neural networks to build risk assessment models. Experimental modeling based on the generated data was carried out, which confirmed the forecasting accuracy of more than 85%, which indicates the effectiveness of inte-grating AI models into financial ICS. The results of the study prove that the introduc-tion of intelligent algorithms in banking systems allows to increase the accuracy of risk assessment, reduce decision-making time and ensure the adaptability of analytical models to changes in the external environment. The practical value of the work lies in the possibility of using the developed model as a basis for building an intelligent risk management module in domestic banks.