Off-the-peg and bespoke classifiers for fraud detection

Detecting fraudulent plastic card transactions is an important and challenging problem. The challenges arise from a number of factors including the sheer volume of transactions financial institutions have to process, the asynchronous and heterogeneous nature of transactions, and the adaptive behaviour of fraudsters. In this fraud detection problem the performance of a supervised two-class classification approach is compared with performance of an unsupervised one-class classification approach. Attention is focussed primarily on one-class classification approaches. Useful representations of transaction records, and ways of combining different one-class classifiers are described. Assessment of performance for such problems is complicated by the need for timely decision making. Performance assessment measures are discussed, and the performance of a number of one- and two-class classification methods is assessed using two large, real world personal banking data sets.