Detection of Fraudulent Transactions Through a Generalized Mixed Linear Models
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Keywords
Generalized linear model, transactional history, detected frauds, outliers detection.
Abstract
The detection of bank frauds is a topic which many financial sector companies have invested time and resources into. However, finding patterns in the methodologies used to commit fraud in banks is a job that primarily involves intimate knowledge of customer behavior, with the idea of isolating those transactions which do not correspond to what the client usually does. Thus, the solutions proposed in literature tend to focus on identifying outliersor groups, but fail to analyse each client or forecast fraud. This paper evaluates the implementation of a generalized linear model to detect fraud. With this model, unlike conventional methods, we consider the heterogeneity of customers. We not only generate a global model, but also a model for each customer which describes the behavior of each one according to their transactional history and previously detected fraudulent transactions. In particular, a mixed logistic model is used to estimate the probability that a transactionis fraudulent, using information that has been taken by the banking systems in different moments of time.
MSC: 62p05
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References
[2] P. Chan, W. Fan, Andreas, A. Prodromidis, y S. J. Stolfo, “Distributed DataMining in Credit Card Fraud Detection”, IEEE Intelligent Systems, vol. 14, n.o6, pp. 67-74, 1999. Referenced in 221, 226
[3] J. Dorronsoro, F. Ginel, C. Sanchez, C. Cruz, “Neural fraud detection in creditcard operations”, IEEE Transactions on Neural Networks, vol. 8, n.o 4, pp. 827-834, jul. 1997. Referenced in 221, 226
[4] I-C. Yeh, C. Lien, “The comparisons of data mining techniques for the predictiveaccuracy of probability of default of credit card clients”, Expert Syst. Appl., vol.36, n.o 2, pp. 2473-2480, mar. 2009. Referenced in 221, 226
[5] T-S. C. Rong-Chang Chen, “A new binary support vector system for increasingdetection rate of credit card fraud.”, International Journal of Pattern Recognitionand Arti_cial Intelligence (IJPRAI), vol. 20, n.o 2, pp. 227-239, 2006.Referenced in 221, 226
[6] Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun K. Majumdar. Creditcard fraud detection using hidden Markov model. IEEE Transactions on Dependableand Secure Computing, vol. 5, no1, pp. 37-48, 2008.Referenced in 221, 226
[7] J. Quah, M. Sriganesh, “Real-time credit card fraud detection using computationalintelligence”, Expert Systems with Applications, vol. 35, n.o 4, pp. 1721-1732, nov. 2008. Referenced in 221, 226
[8] Vladimir Zaslavsky y Anna Strizhak, “Credit Card Fraud Detection Using Self-Organizing Maps”, Information & Securit, vol. 18, n.o 48, pp. 48-63, 2006.Referenced in 221, 226
[9] R. Bolton, D. Hand, “Statistical Fraud Detection: A Review”, Statist. Sci., vol.17, n.o 3, pp. 235-255, ago. 2002. Referenced in 221, 226
[10] Linda Delamaire, Hussein Abdou, John Pointon, “Credit card fraud and detectiontechniques: a review”, Banks and Bank Systems, vol. 4, n.o 2, pp. 57-68,2002. Referenced in 221, 226
[11] E. Aleskerov, B. Freisleben, y B. Rao, “CARDWATCH: a neural network baseddatabase mining system for credit card fraud detection”, in Computational Intelligencefor Financial Engineering (CIFEr), 1997., Proceedings of the IEEE/IAFE1997, 1997, pp. 220 -226. Referenced in 221
[12] E. Kirkos, C. Spathis, y Y. Manolopoulos, “Data Mining techniques for thedetection of fraudulent financial statements”, Expert Systems with Applications,vol. 32, n.o 4, pp. 995-1003, may 2007. Referenced in 221, 232
[13] C. Whitrow, DJ. Hand, P. Juszczak, D. Weston, “Transaction aggregation as astrategy for credit card fraud detection”, Data Mining Knowledge Disc, vol. 18,n.o 1, pp. 30-55, 2009. Referenced in 221
[14] D. Sánchez, M. A. Vila, L. Cerda, y J. M. Serrano, “Association rules appliedto credit card fraud detection”, Expert Systems with Applications, vol. 36, n.o 2,Part 2, pp. 3630-3640, mar. 2009. Referenced in 221
[15] Helen Brown, Robin Prescott. Applied Mixed Models in Medicine, Statistics inPractice, 2001. Referenced in 223
[16] “Mixed Models: Theory and Applications”.[Online]. Available:http://www.dartmouth.edu/ eugened/index.php?section=summary_points. [Accessed: sept-2011]. Referenced in 224
[17] M. Quintana, A. Gallego, M. Pascual, “Aplicación del análisis discriminante y regresión logística en el estudio de la morosidad en las entidades financieras. Comparación de resultados”, Pecunia: revista de la Facultad de Ciencias Económicasy Empresariales, vol. 1, pp. 175-199, 2005. Referenced in 225
[18] A. Alderete, “Fundamentos del Análisis de Regresión Logística en la Investigación Psicológica”, Revista Evaluar, vol. 6, pp. 52-67, 2006. Referenced in 225
[19] Brady West, Kathlen Welch, Andrzej Galecki. Linear Mixed Models: A practicalguide to using statistical software, Chapman & Hall,2007. Referenced in 229