Fraud detection in telecommunication industry using Gaussian mixed model
Fraud detection in telecommunication industry using Gaussian mixed model
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DOI：
10.1109/icriis.2013.6716681
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
Mohd Izhan Mohd Yusoff-Mohd-Izhan-Mohd-Yusoff-2656650;Ibrahim Mohamed;Mohd Rizam Abu Bakar
中科院分区：
文献类型：
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作者：
Mohd Izhan Mohd Yusoff-Mohd-Izhan-Mohd-Yusoff-2656650;Ibrahim Mohamed;Mohd Rizam Abu Bakar
电信行业中的欺诈活动已经达到了一个临界点，因此非常需要有效的算法来识别此类活动。在这篇文章中，我们提出了一种新的欺诈检测算法，使用高斯混合模型（GMM），概率模型成功地用于语音识别问题。使用期望最大化算法估计模型的参数，使得使用核方法确定算法的初始值。使用从马来西亚领先的电信公司之一获得的数据，我们表明，所提出的算法不仅成功地检测到欺诈电话，怀疑该公司，而且还确定可疑的电话，可以是候选人的欺诈电话。该算法易于实现，具有很大的潜力，可以扩展到检测（计费/呼出）欺诈电话，从而减少电信公司的损失。
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.