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Keywords:
Call data fraud detection neural networks probabilistic models user profiling
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The copyright of a submitted article is only transferred to the publishers if and when the article is accepted for publication. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, electrostatic, mechanical, photocopying, recording or otherwise without the prior written permission of the publishers.
Main Article Content
Fraud Detection In Mobile Communications Networks Using User Profiling And Classification Techniques
Abstract
Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. The intentions of mobile phone users cannot be well observed except through the call data. The call data is used in describing behavioural patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data by detecting changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behaviour by empirically testing the methods with data from real mobile communications networks.
Keywords: Call data, fraud detection, neural networks, probabilistic models, user profiling
