×

zbMATH — the first resource for mathematics

Off-the-peg and bespoke classifiers for fraud detection. (English) Zbl 1452.62078
Summary: 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.
MSC:
62-08 Computational methods for problems pertaining to statistics
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62P30 Applications of statistics in engineering and industry; control charts
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] APACS, 2006. http://www.apacs.org.uk/media_centre/press/07_07_11.html
[2] Barnett, V.; Lewis, T., Outliers in statistical data, (1994), Wiley New York · Zbl 0801.62001
[3] Bolton, R.J.; Hand, D.J., Statistical fraud detection: A review, Statistical science, 17, 3, 235-255, (2002) · Zbl 1013.62115
[4] Brause, R., Langsdorf, T., Hepp, M., 1999. Neural data mining for credit card fraud detection. In: 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 103-106
[5] Duin, R., On the choice of the smoothing parameters for parzen estimators of probability density functions, IEEE transactions on computers, C-25, 11, 1175-1179, (1976) · Zbl 0359.93035
[6] Fawcett, T.; Provost, F., Fraud detection, (), 726-731
[7] Ferdousi, Z.; Maeda, A., Unsupervised outlier detection in time series data, (), 121
[8] Hand, D.J., Classifier technology and the illusion of progress, Statistical science, 21, 1, 1-14, (2006) · Zbl 1426.62188
[9] Hand, D.J.; Whitrow, C.; Adams, N.; Juszczak, P.; Weston, D., Performance criteria for plastic card fraud detection tools, Journal of the operational research society, (2007)
[10] Hodge, V.; Austin, J., A survey of outlier detection methodologies, Artificial intelligence review, 22, 2, 85-126, (2004) · Zbl 1101.68023
[11] Juszczak, P., 2006. Learning to recognise. A study on one-class classification and active learning. Ph.D. Thesis, Delft University of Technology
[12] Juszczak, P., Tax, D.M.J., Pękalska, E., Duin, R., 2007. Minimum spanning tree data description. Neurocomputing (in press)
[13] Kelly, M.; Hand, D.J.; Adams, N., The impact of changing populations on classifier performance, (), 367-371
[14] Kohonen, T., Self-organizing maps, (1995), Springer-Verlag Heidelberg, Germany
[15] Kou, Y.; Lu, C.-T.; Sirwongwattana, S.; Huang, Y.-P., Survey of fraud detection techniques, IEEE international conference on networking, sensing and control, 2, 749-754, (2004)
[16] Krzanowski, W.J., Mixtures of continuous and categorical variables in discriminant analysis, Biometrics, 36, 493-499, (1980) · Zbl 0442.62045
[17] Kuncheva, L., Combining pattern classifiers, (2004), Wiley Chichester · Zbl 1066.68114
[18] Lanckriet, G.R.G.; El Ghaoui, L.; Jordan, M.I., (), 905-912
[19] Maes, S., Tuyls, K., Vanschoenwinkel, B., Manderick, B., 2002. Credit card fraud detection using Bayesian and neural networks. In: Proc. NEURO Fuzzy, Havana
[20] Pękalska, E.; Tax, D.M.J.; Duin, R.P.W., One-class LP classifier for dissimilarity representations, (), 761-768
[21] Phua, C., Lee, V., Smith, K., Gayler, R., 2005. A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (submitted for publication)
[22] Platt, J., Probabilistic outputs for support vector machines and comparison to regularized likelihood methods, (), 61-74
[23] Shawe-Taylor, J.; Cristianini, N., Kernel methods for pattern analysis, (2004), Cambridge University Press
[24] Tax, D.; Duin, R., Support vector domain description, Pattern recognition letters, 20, 11-13, 1191-1199, (1999)
[25] Tax, D.M.J., 2001. One-class classification. Ph.D. Thesis, Delft UT
[26] Tax, D.M.J.; Müller, K.R., A consistency-based model selection for one-class classification, (), 363-366
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.