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

TL;DR

This paper presents an organization-centric research agenda for applying machine learning to fraud detection in e-commerce, outlining key topics, challenges, and open questions to guide future research.

Contribution

It introduces an operational model of e-commerce anti-fraud departments, deriving research topics and practical challenges, and summarizes existing literature and open issues.

Findings

Identified 6 key research topics in ML-based fraud detection.

Outlined 12 practical challenges for implementing ML in anti-fraud operations.

Highlighted 22 open research challenges for future work.

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.