Detecting telecommunications fraud based on signature clustering analysis

In the telecommunications services, fraud situations have a significant business impact. Due to the massive amounts of data handled, fraud detection stands as a very difficult and challenging task. In this paper, we propose the application of dynamic clustering over signatures to support this task. Traditional static clustering is applied to determine clusters characteristics, and dynamic clustering analysis is provided to identify changes on cluster membership over time. This approach eliminates the bias caused by special situations like market campaigns or holidays. In order to overcome scalability issues with respect to the huge volume of data involved, a partition-clustering approach is also proposed. Experimental evaluation demonstrates the scalability of the method and its ability to detect previous fraud cases as well as new potential fraud situations