Application of machine learning methods to identify suspicious actions of employees related to violation of the procedures of a credit institution

Abstract

The article presents the results of the application of machine learning methods to identify suspicious actions of employees related to a violation of the procedures of a credit institution, specifically, the theft of funds from customer accounts and cards and abuse of the motivation system. The stages of data preprocessing within the considered task are analyzed. Among the considered classification algorithms, which are not sensitive to class imbalance, the method with the best value of hyperparameters was chosen. Next, the most informative features were highlighted, for which the best values of hyperparameters were selected and the optimal values of the probability thresholds of attributing an object to fraud were found. The proposed technology can be used separately or as part of an anti-fraud system for routine (for example, once a month) detection of illegitimate actions of employees of a credit institution related to the theft of funds from customer accounts and cards and abuse of the motivation system. A software tool in Python was developed that allows solving the task of detecting internal fraud based on the proposed technology.