A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry

Abstract

This paper presents a real-time fully autonomous prescriptive solution for explainable cyber-fraud detection within the iGaming industry. We demonstrate how our solution facilitates the time-consuming task of player risk and fraud assessment through prescriptive analytics. Our tool leverages machine learning algorithms and advancements in the field of eXplainable AI to derive smarter predictions empowered by local interpretable explanations in real-time. Our best-performing pipeline was able to predict fraudulent behaviour with an average precision of 84.2% and an area under the receiver operating characteristics of 0.82 on our dataset. We also addressed the phenomenon of concept-drift and discussed our empirical and data-driven strategy for detecting and dealing with this problem. Finally, we cover how local interpretable explanations can help adopt a pro-active stance in fighting fraud.