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In recent years, topics such as fraud detection and fraud prevention have received a lot of attention on the research front, in particular from payment card issuers. The reason for this increase in research activity can be attributed to the huge annual financial losses incurred by card issuers due to fraudulent use of their card products. A successful strategy for dealing with fraud can quite literally mean millions of dollars in savings per year on operational costs. Artificial neural networks have come to the front as an at least partially successful method for fraud detection. The success of neural networks in this field is, however, limited by their underlying design - a feedforward neural network is simply a static mapping of input vectors to output vectors, and as such is incapable of adapting to changing shopping profiles of legitimate card holders. Thus, fraud detection systems in use today are plagued by …