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.
@article{arxiv.2107.09323,
title = {Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production},
author = {Wissam Siblini and Guillaume Coter and Rémy Fabry and Liyun He-Guelton and Frédéric Oblé and Bertrand Lebichot and Yann-Aël Le Borgne and Gianluca Bontempi},
journal= {arXiv preprint arXiv:2107.09323},
year = {2021}
}