UPM Institutional Repository

Fraud detection in telecommunication industry using Gaussian mixed model


Mohd Yusoff, Mohd Izhan and Mohamed, Ibrahim and Abu Bakar, Mohd Rizam (2013) Fraud detection in telecommunication industry using Gaussian mixed model. In: 3rd International Conference on Research and Innovation in Information Systems – 2013 (ICRIIS'13), 27-28 Nov. 2013, Kuala Lumpur, Malaysia. (pp. 27-32).


The prevalence of fraud activities in telecommunication industry has reached a critical point so that efficient algorithms to identify such activities are greatly needed. In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. The expectation maximization algorithm is used to estimate the parameter of the model such that the initial values of the algorithm is determined using the kernel method. Using data obtained from one of the leading telecommunication companies in Malaysia, we show that the proposed algorithm has successfully not only detected fraud calls as suspected by the company, but also to identify suspicious calls which can be candidates of fraud call. The proposed algorithm is easy to implement with a great potential to be extended to detect (billed/outgoing) fraud calls and hence reduces the lost incurred by the telecommunication companies.

Download File

[img] Text (Abstract)
Fraud detection in telecommunication industry using Gaussian mixed model.pdf

Download (34kB)

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Science
DOI Number: https://doi.org/10.1109/ICRIIS.2013.6716681
Publisher: IEEE
Keywords: Fraud; Telecommunication; Gaussian mixed models; EM algorithm; Kernel method
Depositing User: Nabilah Mustapa
Date Deposited: 04 Aug 2020 02:39
Last Modified: 04 Aug 2020 02:39
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICRIIS.2013.6716681
URI: http://psasir.upm.edu.my/id/eprint/44932
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item