METHODOLOGICAL APPROACHES TO ASSESSING BORROWERS’ CREDITWORTHINESS USING ARTIFICIAL INTELLIGENCE
Alexander M. Vyzhitovich1,2,3, Alexander D. Kirillov1
1Novosibirsk State University of Economics and Management, Novosibirsk, Russian Federation
2Russian Academy of National Economy and Public Administration under the President of the Russian Federation, Siberian Institute of Management, Novosibirsk, Russian Federation
3Institute of Economics and Industrial Engineering of the Siberian Branch of Russian Academy of Sciences
Keywords: artificial intelligence, credit institutions, digital transformation, neural networks, automation, risks, regulatory policy
Abstract
The article examines the impact of digital transformation on the banking sector through the lens of the introduction of artificial intelligence technologies. The scientific novelty lies in the substantiation of the author’s methodological approach to assessing borrowers’ creditworthiness, based on the integration of explicable AI (XAI) algorithms and analysis of behavioral patterns from open digital sources. Barriers and risks of implementation have been identified, and an algorithm for minimizing regulatory restrictions has been proposed.
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