APPLICATION OF ARTIFICIAL INTELLIGENCE IN AUTOMATED FINANCIAL RISK MANAGEMENT SYSTEMS

Abstract

The article is aimed at studying the use of artificial intelligence in automated financial risk management systems to improve the accuracy, efficiency, and effectiveness of managerial decision-making in the financial sector. The study consists in a comprehensive study of the theoretical and methodological foundations of the use of artificial intelligence in management systems, analysis of modern approaches to the classification and assessment of financial risks using machine learning algorithms, formation of the architecture of the decision support system based on forecasting models, as well as evaluation of the effectiveness of the built models based on the results of simulation modelling. Within the framework of the study, a model predicting the credit risk of bank customers has been developed, which allows assessing solvency based on historical data and modern machine learning methods. The research method is modelling using machine learning tools, including neural networks and ensemble learning techniques, as well as data analysis using platforms to visualise results and evaluate the performance of models. Particular attention is paid to data preparation, selection of appropriate features, evaluation of model accuracy, and construction of interpreted visualisations such as SHAP graphs, ROC curves, etc. The result of the study was the creation of an effective model for predicting credit risk, which demonstrates a sufficiently high level of classification accuracy and the ability to adapt to changes in input conditions.