Application of artificial intelligence in automated financial risk management systems

Abstract

The purpose of the study is to apply artificial intelligence in automated financial risk management systems in order to increase the accuracy, effi-ciency and efficiency of managerial decision-making in the financial sector. Within the framework of the study, a model for forecasting the credit risk of bank customers has been developed, which allows assessing solvency based on historical data and modern machine learning methods. The object of the study is automated financial risk management systems that operate in the banking and financial sectors. The subject of the study is artificial intelligence algorithms and models that can be integrated into automated financial risk management systems in order to improve the accuracy of forecasts and iden-tify risky clients. The research method is modeling using machine learning tools, in particular neural networks and ensemble learning methods. The result of the study is the creation of an effective model for predicting credit risk, which demonstrates a high level of classification accuracy and the ability to adapt to changes in input conditions.