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On XCSR for electronic fraud detection

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Abstract

Fraud is a serious problem that costs the worldwide economy billions of dollars annually. However, fraud detection is difficult as perpetrators actively attempt to mask their actions, among typically overwhelming large volumes of, legitimate activity. In this paper, we investigate the fraud detection problem and examine how learning classifier systems can be applied to it. We describe the common properties of fraud, introducing an abstract problem which can be tuned to exhibit those characteristics. We report experiments on this abstract problem with a popular real-time learning classifier system algorithm; results from our experiments demonstrating that this approach can overcome the difficulties inherent to the fraud detection problem. Finally we apply the algorithm to a real-world problem and show that it can achieve good performance in this domain.

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Acknowledgments

The first author would like to acknowledge the financial support provided by the Robert and Maude Gledden Scholarship.

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia

    Mohammad Behdad, Luigi Barone, Tim French & Mohammed Bennamoun

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  1. Mohammad Behdad
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  2. Luigi Barone
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  3. Tim French
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  4. Mohammed Bennamoun
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Corresponding author

Correspondence to Mohammad Behdad.

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Behdad, M., Barone, L., French, T. et al. On XCSR for electronic fraud detection. Evol. Intel. 5, 139–150 (2012). https://doi.org/10.1007/s12065-012-0076-5

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