  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.
