Аннотация
The article is devoted to a comprehensive analysis of the use of artificial intelligence in the financial and banking sector. The purpose of the article is to offer recommendations to the risk management of financial institutions and banks on the need for proactive loss prevention based on analysis and analytics using artificial intelligence and scientific justification for the operation of digital technologies. The methodology of digital transformation of socio-economic is used as the beginning of the process of knowledge of the essence of digital transformation, presented in the form of a system of theoretical postulates and restrictions taken into account in a complex of specific methods and techniques of scientific knowledge on the example of the financial and banking sector. Methods of analysis and systematization were used within the framework of this methodology. In the analysis of the literature, methods of deconstruction and amplification were used to clarify the essence and role of digital technologies, in particular, artificial intelligence technology, and to clarify the difference between the latter and robotic and hardware automation in order to identify potential dangers and the possibility of its most effective adaptation in the financial and banking sphere of activity. The novelty of the article is the consideration of techniques for using artificial intelligence in the financial and banking sector to increase the efficiency of its activities as a whole. The article analyzes the concept of “artificial intelligence”, the forecast of its development, including in Russia, the use of modern technologies of artificial intelligence and machine learning in the financial and banking sector, including risk management. Recommendations are proposed. In risk management, artificial intelligence can help identify fraud and credit risks with greater accuracy, complementing human intelligence with extensive analytics and pattern prediction. AI-driven risk management technologies help financial institutions and banks better understand the potential dangers and opportunities they face through data analysis. AI and risk management combine perfectly when unstructured data needs to be processed and evaluated.