Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
Abstract
Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these anti-fraud operations, but the organizational context in which these ML models operate cannot be ignored. In this paper, we take an organization-centric view on the topic of fraud detection by formulating an operational model of the anti-fraud departments in e-commerce organizations. We derive 6 research topics and 12 practical challenges for fraud detection from this operational model. We summarize the state of the literature for each research topic, discuss potential solutions to the practical challenges, and identify 22 open research challenges.
Cite
@article{arxiv.2107.01979,
title = {Machine Learning for Fraud Detection in E-Commerce: A Research Agenda},
author = {Niek Tax and Kees Jan de Vries and Mathijs de Jong and Nikoleta Dosoula and Bram van den Akker and Jon Smith and Olivier Thuong and Lucas Bernardi},
journal= {arXiv preprint arXiv:2107.01979},
year = {2021}
}
Comments
Accepted and to appear in the proceedings of the KDD 2021 co-located workshop: the 2nd International Workshop on Deployable Machine Learning for Security Defense (MLHat)