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

TL;DR

This paper explores the comprehensive process of implementing transfer learning for credit card fraud detection, emphasizing the journey from business problem formulation through research to practical deployment.

Contribution

It provides a holistic view of applying transfer learning in fraud detection, bridging the gap between research results and real-world deployment.

Findings

Transfer learning improves fraud detection accuracy.

Practical integration challenges are addressed.

End-to-end process from business to research to deployment is outlined.

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.