Testing the fraud detection ability of different user profiles by means of FF-NN classifiers

Constantinos S. Hilas, John N. Sahalos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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. Feedforward neural networks were used as classifiers. It is found that summary characteristics of user's behavior perform better than detailed ones towards this task.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Pages872-883
Number of pages12
Volume4132 LNCS - II
ISBN (Print)3540388710, 9783540388715
Publication statusPublished - 2006
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: 10 Sept 200614 Sept 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4132 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Artificial Neural Networks, ICANN 2006
Country/TerritoryGreece
CityAthens
Period10/09/0614/09/06

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