Fraud Detection in Telecommunications Using Kullback-Leibler Divergence and Latent Dirichlet Allocation
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- 作者：Dominik Olszewski
- 关键词：Fraud detection – ; User profiling – ; Kullback ; Leibler divergence – ; Mixture models – ; Latent Dirichlet Allocation
- 刊名：Lecture Notes in Computer Science
- 出版时间：2011
- 出版年：2011
- 期刊代码：99_0302-9743
- 类别：cp
- 卷：6594
- 期：1
- 页码：71-80
- 数据来源：sp
摘要
In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling by employing the Latent Dirichlet Allocation (LDA). The detection of fraudulent behavior is achieved with a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The accounts are classified with use of the Kullback-Leibler divergence (KL-divergence). Therefore, we also introduce four methods for approximating the KL-divergence between two LDAs. Finally, the results of experimental study on KL-divergence approximation and fraud detection in telecommunications are reported.