article

Employing transaction aggregation strategy to detect credit card fraud

Authors:

Montserrat Guillen,

J. Christopher WestlandAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 16

Pages 12650 - 12657

https://doi.org/10.1016/j.eswa.2012.05.018

Published: 01 November 2012 Publication History

Abstract

Credit card fraud costs consumers and the financial industry billions of dollars annually. However, there is a dearth of published literature on credit card fraud detection. In this study we employed transaction aggregation strategy to detect credit card fraud. We aggregated transactions to capture consumer buying behavior prior to each transaction and used these aggregations for model estimation to identify fraudulent transactions. We use real-life data of credit card transactions from an international credit card operation for transaction aggregation and model estimation.

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Cited By

Paldino GLebichot BLe Borgne YSiblini WOblé FBoracchi GBontempi G(2024)The role of diversity and ensemble learning in credit card fraud detectionAdvances in Data Analysis and Classification10.1007/s11634-022-00515-518:1(193-217)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11634-022-00515-5
Korkoman MAbdullah M(2023)Evolutionary algorithms based on oversampling techniques for enhancing the imbalanced credit card fraud detectionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22234444:6(10311-10323)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222344
Paladini TMonti FPolino MCarminati MZanero S(2023)Fraud Detection under Siege: Practical Poisoning Attacks and Defense StrategiesACM Transactions on Privacy and Security10.1145/361324426:4(1-35)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1145/3613244
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 16

November, 2012

437 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2012.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 November 2012

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Cited By

Paldino GLebichot BLe Borgne YSiblini WOblé FBoracchi GBontempi G(2024)The role of diversity and ensemble learning in credit card fraud detectionAdvances in Data Analysis and Classification10.1007/s11634-022-00515-518:1(193-217)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11634-022-00515-5
Korkoman MAbdullah M(2023)Evolutionary algorithms based on oversampling techniques for enhancing the imbalanced credit card fraud detectionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22234444:6(10311-10323)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222344
Paladini TMonti FPolino MCarminati MZanero S(2023)Fraud Detection under Siege: Practical Poisoning Attacks and Defense StrategiesACM Transactions on Privacy and Security10.1145/361324426:4(1-35)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1145/3613244
Skalski PSutton DBurrell SPerez IWong J(2023)Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction SequencesProceedings of the Fourth ACM International Conference on AI in Finance10.1145/3604237.3626850(141-149)Online publication date: 27-Nov-2023
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Li CDing NZhai YDong H(2021)Comparative study on credit card fraud detection based on different support vector machinesIntelligent Data Analysis10.3233/IDA-19501125:1(105-119)Online publication date: 1-Jan-2021
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Xie YLi AGao LLiu Z(2021)A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud DetectionWireless Communications & Mobile Computing10.1155/2021/25312102021Online publication date: 1-Jan-2021
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