Abstract
Fraud detection in the retail banking sector poses some novel and challenging statistical problems. For example, the data sets are large, and yet each transaction must be examined and decisions must be made in real time, the transactions are often heterogeneous, differing substantially even within an individual account, and the data sets are typically very unbalanced, with only a tiny proportion of transactions belonging to the fraud class. We review the problem, its magnitude, and the various kinds of statistical tools have been developed for this application. The area is particularly unusual because the patterns to be detected change in response to the detection strategies which are developed: the very success of the statistical models leads to the need for new ones to be developed.
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Hand, D.J. (2007). Mining Personal Banking Data to Detect Fraud. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_35
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DOI: https://doi.org/10.1007/978-3-540-73560-1_35
Publisher Name: Springer, Berlin, Heidelberg
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