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.

Shamil Magomedov, Sergei Pavelyev, Irina Ivanova, Alexey Dobrotvorsky, Marina Khrestina and Timur Yusubaliev. “Anomaly Detection with Machine Learning and Graph Databases in Fraud Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.11 (2018). http://dx.doi.org/10.14569/IJACSA.2018.091104

@article{Magomedov2018,
title = {Anomaly Detection with Machine Learning and Graph Databases in Fraud Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091104},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091104},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Shamil Magomedov and Sergei Pavelyev and Irina Ivanova and Alexey Dobrotvorsky and Marina Khrestina and Timur Yusubaliev}
}