Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
Published in: (2021) Machine Learning for Fraud Detection in E-Commerce: A Research Agenda. In: Wang G., Ciptadi A., Ahmadzadeh A. (eds) Deployable Machine Learning for Security Defense. MLHat 2021. Communications in Computer and Information Science, vol 1482. Springer, Cham.
By: Niek Tax, Kees Jan de Vries, Mathijs de Jong, Nikoleta Dosoula, Bram van den Akker, Jon Smith, Olivier Thuong, and Lucas Bernardi
Links: published paper, pdf
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 (see image below). 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.
