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Cost sensitive credit card fraud detection using bayes minimum risk
University of Luxembourg, Luxembourg.ORCID iD: 0000-0003-2298-6774
2013 (English)In: Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, IEEE Computer Society, 2013, 333-338 p.Conference paper (Refereed)
Abstract [en]

Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 333-338 p.
Keyword [en]
Bayesian decision theory, Cost sensitive classification, Credit card fraud detection
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-138993DOI: 10.1109/ICMLA.2013.68ISI: 000353637800060ScopusID: 2-s2.0-84899437078ISBN: 978-0-7695-5144-9OAI: diva2:682141
2013 12th International Conference on Machine Learning and Applications, ICMLA 2013; Miami, FL; United States; 4 December 2013 through 7 December 2013

QC 20140618

Available from: 2013-12-23 Created: 2013-12-23 Last updated: 2014-06-18Bibliographically approved

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Ottersten, Björn
Signal Processing

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