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Unsupervised Machine Learning for Card Payment Fraud Detection

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Risks and Security of Internet and Systems (CRiSIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12026))

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Abstract

Credit card fraud is one of the most common cybercrimes experienced by consumers today. Machine learning approaches are increasingly used to improve the accuracy of fraud detection systems. However, most of the approaches proposed so far have been based on supervised models, i.e., models trained with labelled historical fraudulent transactions, thus limiting the ability of the approach to recognise unknown fraud patterns. In this paper, we propose an unsupervised fraud detection system for card payments transactions. The unsupervised approach learns the characteristics of normal transactions and then identify anomalies as potential frauds. We introduce the challenges on modelling card payment transactions and discuss how to select the best features. Our approach can reduce the equal error rate (EER) significantly over previous approaches (from \(11.2\%\) to \(8.55\% ERR\)), for a real-world transaction dataset.

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Author information

Authors and Affiliations

  1. Newcastle University, Newcastle upon Tyne, UK

    Mario Parreno-Centeno, Mohammed Aamir Ali, Yu Guan & Aad van Moorsel

Authors
  1. Mario Parreno-Centeno
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  2. Mohammed Aamir Ali
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  3. Yu Guan
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  4. Aad van Moorsel
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Corresponding author

Correspondence to Mario Parreno-Centeno .

Editor information

Editors and Affiliations

  1. ReDCAD, University of Sfax, Sfax, Tunisia

    Slim Kallel

  2. IMT Atlantique, Lab-STICC, Université Bretagne Loire, Rennes, France

    Frédéric Cuppens

  3. IMT Atlantique, Lab-STICC, Université Bretagne Loire, Rennes, France

    Nora Cuppens-Boulahia

  4. ReDCAD, University of Sfax, Sfax, Tunisia

    Ahmed Hadj Kacem

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Parreno-Centeno, M., Ali, M.A., Guan, Y., Moorsel, A.v. (2020). Unsupervised Machine Learning for Card Payment Fraud Detection. In: Kallel, S., Cuppens, F., Cuppens-Boulahia, N., Hadj Kacem, A. (eds) Risks and Security of Internet and Systems. CRiSIS 2019. Lecture Notes in Computer Science(), vol 12026. Springer, Cham. https://doi.org/10.1007/978-3-030-41568-6_16

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  • DOI: https://doi.org/10.1007/978-3-030-41568-6_16

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