Mobile Roaming Fraud Detection Based on User Behavior: In Case of Ethio Telecom
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Date
2022-01
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Addis Ababa University
Abstract
Mobile roaming data-internet fraud, committed on visitor networks is a continued
challenge and significant source of revenue losses for telecommunications
societies including customers. The actually introduced prevention and detection
mechanism have limitations in protection of the service.
In this study, we used different data-sets and build roaming mobile data fraud
detection model. Three supervised machine learning algorithms: Artificial Neural
Network (ANN), Support Vector Machine (SVM) and J48 decision tree (J48
DT) where used to build model from each data-set. The model performance
was computed based on different metrics. The model with merged data-set
(roaming in and roaming out) achieved better performance and J48 DT is resulted
greater in accuracy of 99.50, average F1_Score 99.00 and ROC 99.30.
For compiled usage behavior exceeds the detection of such fraud, organization
better to periodically analysis of data rather than waiting for TAP file-user
usage from visited network in addition to revising roaming agreement.
Description
Keywords
User behavior, Mobile data roaming fraud detection, Mobile data usage, Machine learning algorithms, Machine learning tools, Home network, Visited network