Artificial Intelligence and Big Data Applications in Credit Risk Management: A Systematic Review

Abstract

Disruptive technologies such as artificial intelligence and big data analytics have transformed the way banks operate. Administrators are trying to find artificial intelligence and big data solutions that are suitable for the bank's risk credit management functions. A systematic study of the literature on this field will clarify the importance of artificial intelligence and big data in credit risk management. The aim of this paper is to provide a comprehensive review of research in the field of AI and big data applications in credit risk management using a bibliographic, intellectual network, and conceptual analysis of published studies during the period 1998 to 2021. The research results show that in the above period there are one hundred and ninety-two studies on the application of artificial intelligence and big data in credit risk management. Studies have used different methods as well as models to assess credit risk. In particular, group learning methods, deep learning, supervised training, feature or attribute selection using genetic algorithms show better predictive results than traditional methods.