Fraud detection problems have some uniquely challenging properties which make them difficult. In this paper, we investigate the fraud detection problem by describing the common properties of electronic fraud and examining how learning classifier systems (LCSs) can be applied to it. Also, we introduce "random Boolean function" (RBF); an abstract problem with high level of controllability which can be tuned to exhibit those characteristics individually, and report the results of using XCSR (a continuous variant of LCS) on RBF problem and also on a real-world problem. Results from our experiments demonstrate that XCSR can overcome most of the difficulties inherent to the fraud detection problem and can achieve good performance in case of the real-world problem.
['Mohammad Behdad', 'Tim French', 'Luigi Barone', 'Mohammed Bennamoun']
On the problems of using learning classifier systems for fraud detection