  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.
