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Workshop on Mining Data for Financial Applications

MIDAS 2019: Mining Data for Financial Applications pp 32–46Cite as

Representation Learning in Graphs for Credit Card Fraud Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11985))

Abstract

Representation learning in graphs has proven useful for many predictive tasks. In this paper we assess the feasibility of representation learning in a credit card fraud setting. Data analytics has been successful in predicting fraud in previous research. However, the research field has focused on techniques which require tedious and expensive hand-crafting of features. In addition, existing works often ignore information related to the network of transactions. Representation learning in graphs tackles both of these challenges. First, it provides the possibility to tap into the relational and structural aspects of the transaction network and leverage these in a predictive model. Second, it featurizes the graph without the need for manual feature engineering. This work contributes to the literature by being the first to explicitly and extensively show how fraud detection modeling can benefit from representation learning. We discern three different approaches in this paper: traditional network featurization, an inductive representation learning algorithm and a transductive representational learner. Through extensive experimental evaluation on a real-world dataset we show that state-of-the-art representation learning in graphs outperforms traditional graph featurization.

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Acknowledgement

We acknowledge the support given by the Research Fund - Flanders (FWO) as Aspirant (Rafaël Van Belle).

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Authors and Affiliations

  1. Research Center for Information Systems Engineering, KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium

    Rafaël Van Belle, Sandra Mitrović & Jochen De Weerdt

Authors
  1. Rafaël Van Belle
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  2. Sandra Mitrović
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  3. Jochen De Weerdt
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Corresponding author

Correspondence to Rafaël Van Belle .

Editor information

Editors and Affiliations

  1. UniCredit, Milan, Italy

    Valerio Bitetta

  2. UniCredit, Rome, Italy

    Ilaria Bordino

  3. UniCredit, Milan, Italy

    Andrea Ferretti

  4. UniCredit, Rome, Italy

    Francesco Gullo

  5. UniCredit, Milan, Italy

    Stefano Pascolutti

  6. ENEA Portici Research Center, Portici, Italy

    Giovanni Ponti

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Van Belle, R., Mitrović, S., De Weerdt, J. (2020). Representation Learning in Graphs for Credit Card Fraud Detection. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds) Mining Data for Financial Applications. MIDAS 2019. Lecture Notes in Computer Science(), vol 11985. Springer, Cham. https://doi.org/10.1007/978-3-030-37720-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-37720-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37719-9

  • Online ISBN: 978-3-030-37720-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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