Anomaly detection with machine learning and graph databases in fraud management

Abstract

In this paper, the task of fraud detection using the methods of data analysis and machine learning based on social and transaction graphs is considered. The algorithms for feature calculation, outlier detection and identifying specific sub-graph patterns are proposed. Software realization of the proposed algorithms is described and the results of experimental study of the algorithms on the sets of real and synthetic data are presented.