Abstract
The article presents the results of applying machine learning techniques to detect fraudulent banking transactions. The market of antifraud systems was studied. Ensemble methods for solving classification problem as well as dimensionality reduction techniques were examined. The proposed analysis procedure is based on the selection of the best machine learning model and the identification of the most significant features for detecting fraud. Results-based recommendations can be used in financial institutions as well as in other organizations, where it is required to identify and prevent entities’ fraudulent actions that pose a threat to the functioning of business processes and electronic systems. The proposed fraud detection methodology was implemented on the cloud-based analytical platform Statistical Analysis System (SAS) Viya.
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