Reference : Cost sensitive credit card fraud detection using bayes minimum risk
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/26382
Cost sensitive credit card fraud detection using bayes minimum risk
English
Bahnsen, A. C. [Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg]
Stojanovic, Aleksandar mailto [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 mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
2013
Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
IEEE Computer Society
1
333-338
Yes
International
2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
4 December 2013 through 7 December 2013
Miami, FL
[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
[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.
Association for Machine Learning and Applications (AML and A);IEEE Computer Society
http://hdl.handle.net/10993/26382
10.1109/ICMLA.2013.68
104787

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