An investigation of real-valued accuracy-based learning classifier systems for electronic fraud detection

Authors:
Mohammad Behdad;Luigi Barone;Tim French;Mohammed Bennamoun
Affiliations:
The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia
Venue:
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Year:
2010

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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 masquerade 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.