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.
|Title of host publication||Proceedings of business intelligence workshop of 13th Portugese conference on artificial intelligence (EPIA 2007,) 3-7 December 2007, Guimaraes, Portugal|
|Place of Publication||Guimaraes, Portugal|
|Publication status||Published - 2007|
|Event||conference; EPIA 2007 - |
Duration: 1 Jan 2007 → …
|Conference||conference; EPIA 2007|
|Period||1/01/07 → …|