Reference : Cost Sensitive Credit Card Fraud Detection using Bayes Minimum Risk
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/10602
Cost Sensitive Credit Card Fraud Detection using Bayes Minimum Risk
English
Correa Bahnsen, Alejandro mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Stojanovic, Aleksandar mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2013
12th International Conference on Machine Learning and Applications
IEEE Computer Society
Yes
No
International
978-0-7695-5144-9/13
12th International Conference on Machine Learning and Applications
from 4-12-2013 to 7-12-2013
Association for Machine Learning and Applications (AMLA)
Miami
United States
[en] Credit card fraud detection ; Bayesian decision theory ; Cost sensitive classification
[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.
http://hdl.handle.net/10993/10602
10.1109/ICMLA.2013.68

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