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

2021/07/20 by Wissam Siblini, Guillaume Coter, Siblini, Wissam +13
Business, Management and Accounting · Computer Science · #Cryptography and Security (cs.CR) #Data Stream Mining Techniques #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Algorithms

paper · pdf · doi:10.48550/arxiv.2107.09323

openalex publication_date 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

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