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Feature Selection Approaches to Fraud Detection in e-Payment Systems

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E-Commerce and Web Technologies (EC-Web 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 278))

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Abstract

Due to the large amount of data generated in electronic transactions, to find the best set of features is an essential task to identify frauds. Fraud detection is a specific application of anomaly detection, characterized by a large imbalance between the classes, which can be a detrimental factor for feature selection techniques. In this work we evaluate the behavior and impact of feature selection techniques to detect fraud in a Web Transaction scenario. To measure the effectiveness of the feature selection approach we use some state-of-the-art classification techniques to identify frauds, using real application data. Our results show that the imbalance between the classes reduces the effectiveness of feature selection and that resampling strategy applied in this task improves the final results. We achieve a very good performance, reducing the number of features and presenting financial gains of up to 57.5% compared to the actual scenario of the company.

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Acknowledgment

This research was supported by the Brazilian National Institute of Science and Technology for the Web (CNPq grant numbers 573871/2008-6 and 477709/2012-5), MASWeb (grant FAPEMIG/PRONEX APQ-01400-14), EUBra-BIGSEA (H2020-EU.2.1.1 690116, Brazil/MCTI/RNP GA-000650/04), CAPES, CNPq, Fapemig and Universo OnLine Inc. (UOL).

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Authors and Affiliations

  1. Department of Computer Science (DCC), Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, 31270-901, Brazil

    Rafael Franca Lima & Adriano C. M. Pereira

Authors
  1. Rafael Franca Lima
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  2. Adriano C. M. Pereira
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Corresponding author

Correspondence to Rafael Franca Lima .

Editor information

Editors and Affiliations

  1. The Insight Centre for Data Analytics, University College Cork , Cork, Ireland

    Derek Bridge

  2. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Heiner Stuckenschmidt

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Lima, R.F., Pereira, A.C.M. (2017). Feature Selection Approaches to Fraud Detection in e-Payment Systems. In: Bridge, D., Stuckenschmidt, H. (eds) E-Commerce and Web Technologies. EC-Web 2016. Lecture Notes in Business Information Processing, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-319-53676-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-53676-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53675-0

  • Online ISBN: 978-3-319-53676-7

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