Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
It addresses fraud detection for e-commerce businesses, but it is incremental as it focuses on organizational context rather than new methods.
The paper tackles the problem of fraud detection in e-commerce by developing an organization-centric operational model, resulting in the identification of 6 research topics, 12 practical challenges, and 22 open research challenges.
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.