12th International Conference on Machine Learning and Applications
IEEE Computer Society
1
333-338
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] State-of-the-art algorithms ; Crime ; Transactional data ; State of the art ; Credit card frauds ; Credit card fraud detections ; Cost sensitive classifications ; Comparison measures ; Risk assessment ; Learning systems ; Costs ; Algorithms ; Credit card fraud detection ; Cost sensitive classification ; Bayesian decision theory
[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.
Association for Machine Learning and Applications (AML and A);IEEE Computer Society
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
