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
Résumé :
[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.
Disciplines :
Sciences informatiques
Identifiants :
eid=2-s2.0-84899437078
Auteur, co-auteur :
CORREA BAHNSEN, Alejandro ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
STOJANOVIC, Aleksandar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Cost Sensitive Credit Card Fraud Detection using Bayes Minimum Risk
Date de publication/diffusion :
2013
Nom de la manifestation :
12th International Conference on Machine Learning and Applications
Organisateur de la manifestation :
Association for Machine Learning and Applications (AMLA)
Lieu de la manifestation :
Miami, Etats-Unis
Date de la manifestation :
from 4-12-2013 to 7-12-2013
Manifestation à portée :
International
Titre de l'ouvrage principal :
12th International Conference on Machine Learning and Applications
Maison d'édition :
IEEE Computer Society
ISBN/EAN :
978-0-7695-5144-9/13
Pagination :
333-338
Peer reviewed :
Peer reviewed
Organisme subsidiant :
Association for Machine Learning and Applications (AML and A);IEEE Computer Society
