[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 :
Computer science
Author, co-author :
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
External co-authors :
yes
Language :
English
Title :
Transfer learning for credit card fraud detection : A journey from research to production.
Publication date :
2021
Event name :
DSAA 2021
Event date :
April 2021
Journal title :
The Proceedings of the Data Science and Advanced Analytics (DSAA 2021) IEEE conference
Peer reviewed :
Peer reviewed
Funders :
Innoviris - Institut Bruxellois pour la Recherche et l'Innovation