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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Anomaly Detection with Machine Learning and Graph Databases in Fraud Management
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091104
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.
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.
Keywords: Data analysis; machine learning; graph database; fraud detection; anti-money laundering
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}
}
