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Evolutionary Intelligence
Published in: Evolutionary Intelligence 2/2012

01-06-2012 | Special Issue

On XCSR for electronic fraud detection

Authors: Mohammad Behdad, Luigi Barone, Tim French, Mohammed Bennamoun

Published in: Evolutionary Intelligence | Issue 2/2012

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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.
previous article On principal component analysis for high-dimensional XCSR

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Metadata
Title
On XCSR for electronic fraud detection
Authors
Mohammad Behdad
Luigi Barone
Tim French
Mohammed Bennamoun
Publication date
01-06-2012
Publisher
Springer-Verlag
Published in
Evolutionary Intelligence / Issue 2/2012
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-012-0076-5

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