The impact of artificial intelligence on risk management in the operational activities of financial institutions

Abstract

The purpose of the study was to determine the impact of artificial intelligence (AI) on the quality of management decisions in the process of risk assessment and forecasting. For this purpose, the role of AI in risk management was analysed, and the practices of using AI in risk management were studied. The study results confirmed that the introduction of AI significantly improves the speed and quality of risk management decisions. It was found that with the help of machine learning algorithms that use numerous variables to analyse the creditworthiness of customers, financial institutions are more efficient in credit scoring. The algorithms allow banks to reduce default rates and at the same time improve the quality of their loan portfolio by making assessments more informed. In addition, machine learning technologies are used to quickly identify suspicious activities or abnormal patterns of customer behaviour, reduce the number of fraudulent transactions, improve customer security and reduce the cost of identifying and eliminating such threats. Another result of the study was the confirmation of the effectiveness of automating routine processes, such as updating risk registers and generating reports, which can significantly reduce operating costs and speed up management decision-making. Importantly, the use of AI not only improves the accuracy of risk forecasting and decision-making, but also contributes to the personalisation of services for customers, which increases their loyalty and satisfaction. Together with the implementation of compliance systems, AI technologies ensure compliance with legal requirements and increase transparency in financial transactions, which reduces the likelihood of non-compliance with regulatory standards and minimises the risks involved. The findings indicated that the introduction of AI for risk management requires not only technological optimisation, but also a deep review of ethical standards, transparency of algorithms and adaptation of regulatory mechanisms, which will ensure both increased efficiency and trust in such systems.