Abstract
Credit card fraud detection is an obviously difficult problem. There are two reasons for that. The first one is the overwhelming majority of good operations over fraudulent ones. The second one is the similarity of many bad operations to legal ones. In other words, to catch a fraudulent operation is akin to find needles in a haystack, only that some needles are in fact hay! In this type of problems (that we term below as Extreme Sample problems) well established methods for classifier construction, such as Multilayer Perceptrons (MLPs), may fail. Non Linear Discriminant Analysis, an alternative method, is described here and some issues pertaining to its practical use, such as fast convergence and architecture selection, are also discussed. Its performance is also compared with that of MLPs over Extreme Sample problems, and it is shown that it gives better results both over synthetic data and on credit card fraud.
With partial support from Spain’s CICyT, grant TIC 98–247.
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Dorronsoro, J.R., González, A.M., Santa Cruz, C. (2002). Extreme Sample Classification and Credit Card Fraud Detection. In: Segovia, J., Szczepaniak, P.S., Niedzwiedzinski, M. (eds) E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing, vol 105. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1779-9_9
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