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Abstract
Nowadays, plastic card fraud detection is of great importance to financial institutions. This paper presents a proposal for an automated credit card fraud detection system based on the outlier analysis technology. Previous research has established that the use of outlier analysis is one of the best techniques for the detection of fraud in general. However, to establish patterns to identify anomalies, these patterns are learned by the fraudsters and then they change the way to make de fraud. The approach applies a multi-objective model hybridized with particle swarm optimization of typical cardholder’s behavior and to analyze the deviation of transactions, thus finding suspicious transactions in a non supervised scheme.
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Authors and Affiliations
Universidad Autónoma de Aguascalientes, Mexico
Arturo Elías, Alejandro Padilla & Julio Ponce
Universidad Autónoma de Ciudad Juárez, Mexico
Alberto Ochoa-Zezzatti
- Arturo Elías
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- Alberto Ochoa-Zezzatti
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- Alejandro Padilla
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- Julio Ponce
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Editors and Affiliations
GICAP Research Group, University of Burgos, 09006, Burgos, Spain
Emilio Corchado
Wroclaw University of Technology, 50-370, Wroclaw, Poland
Marek Kurzyński & Michał Woźniak &
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Elías, A., Ochoa-Zezzatti, A., Padilla, A., Ponce, J. (2011). Outlier Analysis for Plastic Card Fraud Detection a Hybridized and Multi-Objective Approach. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_1
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