Reference : Cost sensitive credit card fraud detection using bayes minimum risk

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/26382

Title :	Cost sensitive credit card fraud detection using bayes minimum risk
Language :	English
Author, co-author :	Bahnsen, A. C. [Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg]
	Stojanovic, Aleksandar [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Aouada, D. [Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Publication date :	2013
Journal title :	Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
Publisher :	IEEE Computer Society
Volume :	1
Pages :	333-338
Peer reviewed :	Yes
Audience :	International
Event name :	2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
Event date :	4 December 2013 through 7 December 2013
Event place (city) :	Miami, FL
Keywords :	[en] Bayesian decision theory ; Cost sensitive classification ; Credit card fraud detection ; Algorithms ; Costs ; Learning systems ; Risk assessment ; Comparison measures ; Cost sensitive classifications ; Credit card fraud detections ; Credit card frauds ; State of the art ; State-of-the-art algorithms ; Transactional data ; Crime
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. © 2013 IEEE.
Funders :	Association for Machine Learning and Applications (AML and A);IEEE Computer Society
Permalink :	http://hdl.handle.net/10993/26382
DOI :	10.1109/ICMLA.2013.68
Commentary :	104787

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