Anomaly Detection with Machine Learning and Graph Databases in Fraud Management

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 pro...

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Bibliographic Details
Published inInternational journal of advanced computer science & applications Vol. 9; no. 11
Main Authors Magomedov, Shamil, Pavelyev, Sergei, Ivanova, Irina, Dobrotvorsky, Alexey, Khrestina, Marina, Yusubaliev, Timur
Format Journal Article
LanguageEnglish
Published West Yorkshire Science and Information (SAI) Organization Limited 2018
Subjects
Algorithms
Anomalies
Data analysis
Fraud
Machine learning
Outliers (statistics)
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2018.091104