User Profiling for Fraud Detection in Telecommunication Networks

Abstract

Telecommunications fraud is increasing dramatically each year resulting in loss of a large amount of euros worldwide. A statistical machine learning method is presented that constructs user profiles for the detection of fraudulent activities in telecommunications networks. The approach presented here can be used for the detection of superimposed or hacking fraud, works well for mid-term decisions and cannot be used for on-line account comparison.