The Use of Artificial Intelligence in the Financial Risk Management of Banks and Insurance Companies

Abstract

The modern financial sector is facing growing risks due to globalisation, digitalisation and an increase in the number of fraudulent transactions. Traditional approaches to financial risk management in banks and insurance companies no longer provide the necessary speed and accuracy of analysis, which increases the relevance of the introduction of innovative technologies. Artificial intelligence demonstrates significant potential in the field of risk management, making it possible to increase the efficiency of risk forecasting, automate decision-making processes and minimise financial losses. Objective. The study is aimed at analysing the possibilities of using artificial intelligence to improve forecasting accuracy, detect fraudulent schemes and optimise credit and insurance risk management. The paper also discusses the main challenges and limitations associated with the introduction of artificial intelligence into the financial risk management system. Methods. The study uses a comprehensive approach that includes an analysis of scientific literature, a statistical method for assessing the effectiveness of artificial intelligence in financial risk management, and comparative analysis methods to identify the advantages and disadvantages of artificial intelligence compared to traditional risk management systems. Practical cases and analysis of the work of leading banks and insurance companies made it possible to illustrate the real possibilities of applying artificial intelligence in the financial sector. Results. According to the study, artificial intelligence improves the efficiency of financial risk management through the use of machine learning algorithms, big data analytics, and automated fraud detection systems. The introduction of such technologies enables financial institutions to respond more quickly to market changes, improve the quality of borrower and insurer assessment, and optimise internal processes. At the same time, a number of challenges have been identified, including regulatory restrictions, ethical issues regarding the use of personal data, and potential technical shortcomings of algorithms.