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Volume 2018, Article ID 5764370, 9 pages
Research Article

Credit Card Fraud Detection through Parenclitic Network Analysis

1Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, Lisboa, Portugal
2Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
3Data, Networks and Cybersecurity Research Institute, Univ. Rey Juan Carlos, 28028 Madrid, Spain
4Department of Applied Mathematics, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
5Center for Computational Simulation, 28223 Pozuelo de Alarcón, Madrid, Spain
6Cyber Security & Digital Trust, BBVA Group, 28050 Madrid, Spain

Correspondence should be addressed to Miguel Romance; se.cjru@ecnamor.leugim

Received 15 December 2017; Accepted 17 April 2018; Published 22 May 2018

Academic Editor: Arturo Buscarino

Copyright © 2018 Massimiliano Zanin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.