Big data applications in banking credit risk audits: Sampling through machine learning

Banking is one of the industries where big data analytics applications are widely used. Banks have precious data in quantity and quality, thanks to customer information and transaction records obtained through online and offline channels. It will be very beneficial to process those data via machine learning algorithms and exploit them effectively in decision-making processes. Banks can use big data applications to audit credit risks, one of the most significant banking risks. In this study, information about big data applications in credit risk audits of banks is provided, and how these can be used in audit sampling is explained through machine learning models. Different classification models, such as decision trees and random forests, were applied to corporate customer data obtained from a private bank, and their results were compared. As a result of the study, the random forest model showed the best performance. In addition, it has been concluded that big data analytics and machine learning applications significantly contribute to the sample selection of credit risk audits.