Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users' behavior characterization. In the present paper, we deal with the issue of user characterization. Several real cases of defrauded user accounts for different user profiles were studied. Each profile's ability to characterize user behavior in order to discriminate normal activity from fraudulent one was tested. Feed-forward neural networks were used as classifiers. It is found that summary characteristics of user's behavior perform better than detailed ones towards this task.
['Constantinos S. Hilas', 'John N. Sahalos']
Testing the fraud detection ability of different user profiles by means of FF-NN classifiers