Detecting fraud in online games of chance and lotteries

Fraud detection has been an important topic of research in the data mining community for the past two decades. Supervised, semi-supervised, and unsupervised approaches to fraud detection have been proposed for the telecommunications, credit, insurance and health-care industries. We describe a novel hybrid system for detecting fraud in the highly growing lotteries and online games of chance sector. While the objectives of fraudsters in this sector are not unique, money laundering and insider attack scenarios are much more prevalent in lotteries than in the previously studied sectors. The lack of labeled data for supervised classifier design, user anonymity, and the size of the data-sets are the other key factors differentiating the problem from previous studies, and are the key drivers behind the design and implementation decisions for the system described. The system employs online algorithms that optimally aggregate statistical information from raw data and applies a number of pre-specified checks against known fraud scenarios as well as novel clustering-based algorithms for outlier detection which are then fused together to produce alerts with high detection rates at acceptable false alarm levels.