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Credit Card Fraud Detection through Parenclitic Network Analysis

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  • Massimiliano Zanin
  • Miguel Romance
  • Santiago Moral
  • Regino Criado

Abstract

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.

Suggested Citation

  • Massimiliano Zanin & Miguel Romance & Santiago Moral & Regino Criado, 2018. "Credit Card Fraud Detection through Parenclitic Network Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, May.
  • Handle: RePEc:hin:complx:5764370
    DOI: 10.1155/2018/5764370
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    References listed on IDEAS

    as
    1. Massimiliano Zanin & David Papo & Miguel Romance & Regino Criado & Santiago Moral, 2016. "The topology of card transaction money flows," Papers 1605.04938, arXiv.org.
    2. Zanin, Massimiliano & Papo, David & Romance, Miguel & Criado, Regino & Moral, Santiago, 2016. "The topology of card transaction money flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 134-140.
    3. Wang, Bing & Tang, Huanwen & Guo, Chonghui & Xiu, Zhilong, 2006. "Entropy optimization of scale-free networks’ robustness to random failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 591-596.
    4. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
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    Citations

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    Cited by:

    1. Catayoun Azarm & Erman Acar & Mickey van Zeelt, 2024. "On the Potential of Network-Based Features for Fraud Detection," Papers 2402.09495, arXiv.org, revised Feb 2024.
    2. Noemí DeCastro-García & Ángel Luis Muñoz Castañeda & David Escudero García & Miguel V. Carriegos, 2019. "Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm," Complexity, Hindawi, vol. 2019, pages 1-16, February.
    3. Bofei Xiao & Bo Lei & Wei Lan & Bin Guo, 2022. "A blockwise network autoregressive model with application for fraud detection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1043-1065, December.
    4. Iglesias Pérez, Sergio & Moral-Rubio, Santiago & Criado, Regino, 2021. "A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    5. Sergio Iglesias Perez & Regino Criado, 2022. "Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs," Mathematics, MDPI, vol. 11(1), pages 1-24, December.

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