Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production

Authors: Wissam Siblini, Guillaume Coter, Rémy Fabry, Liyun He-Guelton, Frédéric Oblé, Bertrand Lebichot, Yann-Aël Le Borgne, Gianluca Bontempi
Published: 2021-07-20 | Updated: 2021-11-04

Source: https://arxiv.org/abs/2107.09323

PDF: https://arxiv.org/pdf/2107.09323

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機械学習 不正取引 データ管理システム

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Abstract

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.