An application of supervised and unsupervised learning approaches to telecommunications fraud detection

Abstract

This paper investigates the usefulness of applying different learning approaches to a problem of telecommunications fraud detection. Five different user models are compared by means of both supervised and unsupervised learning techniques, namely the multilayer perceptron and the hierarchical agglomerative clustering. One aim of the study is to identify the user model that best identifies fraud cases. The second task is to explore different views of the same problem and see what can be learned form the application of each different technique. All data come from real defrauded user accounts in a telecommunications network. The models are compared in terms of their performances. Each technique’s outcome is evaluated with appropriate measures.