[en] The dark face of digital commerce generalization is the increase of fraud attempts. To prevent any type of attacks, state-of-the-art fraud detection systems are now embedding Machine Learning (ML) modules. The conception of such modules is only communicated at the level of research and papers mostly focus on results for isolated benchmark datasets and metrics. But research is only a part of the journey, preceded by the right formulation of the business problem and collection of data, and followed by a practical integration. In this paper, we give a wider vision of the process, on a case study of transfer learning for fraud detection, from business to research, and back to business.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Siblini, Wissam
Coter, Guillaume; Worldline
Fabry, Remy
He-Guelton, Liyun; Worldline
Oble, Frederic; Worldline
LEBICHOT, Bertrand ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Le Borgne, Yann-Aël
Bontempi, Gianluca
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Transfer learning for credit card fraud detection : A journey from research to production.
Date de publication/diffusion :
2021
Nom de la manifestation :
DSAA 2021
Date de la manifestation :
April 2021
Titre du périodique :
The Proceedings of the Data Science and Advanced Analytics (DSAA 2021) IEEE conference
Peer reviewed :
Peer reviewed
Organisme subsidiant :
Innoviris - Institut Bruxellois pour la Recherche et l'Innovation
