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.
Chapter PDF
References
Amari S (1985) Differential Geometric Methods in Statistics. Lecture Notes in Statistics 28, Springer-Verlag.
Amari S (1998) Natural Gradient Works Efficiently in Learning. Neural Computation 10: 251–276.
Bernard E, Botha EC (1993) Backpropagation uses prior information efficiently. IEEE Trans. in Neural Networks 4: 794–802.
Bernard E, Casasent D (1989) A comparison between criterion functions with an application to neural nets. IEEE Trans. in Systems, Man and Cybernetics 19: 1030–1041.
Bourlard HA, Morgan N (1994) Connectionist Speech Recognition. Kluwer.
Dorronsoro J, Ginel F, Sanchez C, Santa Cruz C (1997) Neural Fraud Detection in Credit Card Operations. IEEE Trans. in Neural Networks 8: 827–834.
Dorronsoro J, Gonzlez A, Santa Cruz C (2001) Natural gradient learning in NLDA networks. In: Proceedings of the 2001 IWANN Conference, Lecture Notes in Computer Science 2084. Springer Verlag, pp 427–434.
Dorronsoro J, Gonzlez A, Santa Cruz C (2001) Arquitecture selection in NLDA networks. In: Proceedings of the 2001 Internationa Conference on Artifical Neural Networks, Lecture Notes in Computer Science 2130. Springer Verlag, pp 27–32.
Duda R, Hart P (1973) Pattern classification and scene analysis. Wiley.
Fukunaga K (1972) Introduction to Statistical Pattern Recognition. Academic Press.
Geman S, Bienenstock E, Doursat R ((1992) Neural networks and the bias/variance dilemma. Neural Computation 4: 1–58.
Golden R (1996). Mathematical Models for Neural Network Analysis and Design. MIT Press.
Lawrence S, Burns I, Back A, Tsoi A, Giles C (1998). Neural network classification and prior class probabilities. In: Lecture Notes in Computer Science State—of—the—Art Surveys. Springer, pp 299–314.
Manoukian E (1986) Modern Concepts and Theorems of Mathematical Statistics. Springer.
Mardia K, Kent J, Bibby J (1979) Multivariate Analysis. Academic Press.
Murray M, Rice J (1993) Differential Geometry and Statistics. Chapman and Hall.
Park H, Amari S, Fukumizu K (2000) Adaptive Natural Gradient Learning Algorithms for Various Stochastic Models. Neural Networks 13: 755–764.
Press W, Flannery B, Teukolski S, Vetterling W (1992) Numerical Recipes in C. Cambridge U. Press.
Richard M, Lippmann R (1991), Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation 3: 461–483.
Rao C (1973) Linear Statistical Inference and its Applications. Wiley.
Ripley B (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
Ruck D, Rogers S, Kabrisky K, Oxley M, Suter B (1990) The multilayer perceptron as an approximation to an optimal Bayes estimator. IEEE Trans. in Neural Networks 1: 296–298.
Santa Cruz C, Dorronsoro J (1998) A non-linear discriminant algorithm for data projection and feature extraction. IEEE Trans. in Neural Networks 9: 1370–1376.
Webb A, Lowe D (1990) The optimised internal representation of multilayer classifier networks performs non-linear discriminant analysis. Neural Networks 3: 367–375.
White H (1989) Learning in artificial neural networks: a statistical perspective, Neural Computation 1: 425–464.
Yaeger L, Lyon R, Webb B (1997) Effective training of a neural network character classifier for word recognition. In: Advances in Neural Information Processing Systems 9. MIT Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1779-9_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2514-5
Online ISBN: 978-3-7908-1779-9
eBook Packages: Springer Book Archive