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Outlier Analysis for Plastic Card Fraud Detection a Hybridized and Multi-Objective Approach

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 6679))

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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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Author information

Authors and Affiliations

  1. Universidad Autónoma de Aguascalientes, Mexico

    Arturo Elías, Alejandro Padilla & Julio Ponce

  2. Universidad Autónoma de Ciudad Juárez, Mexico

    Alberto Ochoa-Zezzatti

Authors
  1. Arturo Elías

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  2. Alberto Ochoa-Zezzatti

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  3. Alejandro Padilla

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  4. Julio Ponce

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Editor information

Editors and Affiliations

  1. GICAP Research Group, University of Burgos, 09006, Burgos, Spain

    Emilio Corchado

  2. Wroclaw University of Technology, 50-370, Wroclaw, Poland

    Marek Kurzyński  & Michał Woźniak  & 

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© 2011 Springer-Verlag Berlin Heidelberg

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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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Chapter
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Price includes VAT (Japan)
  • Available as PDF
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Purchases are for personal use only


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