Machine Learning for Fraud Detection in E-Commerce: A Research Agenda

Autor:	Tax, Niek, de Vries, Kees Jan, de Jong, Mathijs, Dosoula, Nikoleta, Akker, Bram van den, Smith, Jon, Thuong, Olivier, Bernardi, Lucas
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	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. Comment: 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)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.01979 Zobrazit plný text záznamu View this record from Arxiv