Employing Kullback-Leibler divergence and Latent Dirichlet Allocation for fraud detection in telecommunications

Article type: Research Article

Authors: Olszewski, Dominik

Affiliations: Warsaw University of Technology, ul. Koszykowa 75, Warsaw, Poland. Tel.: +48 22 234 7618; Fax: +48 22 625 6278; E-mail: olszewsd@ee.pw.edu.pl

Abstract: In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling using the Latent Dirichlet Allocation (LDA). Fraudulent behavior is detected with use of a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The paper provides also a method for automatic threshold computation. The accounts are classified with use of the Kullback-Leibler divergence (KL-divergence). Therefore, we also introduce three 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.

Keywords: Kullback-Leibler divergence, mixture models, Latent Dirichlet Allocation, fraud detection, user profiling, telecommunications

DOI: 10.3233/IDA-2012-0534

Journal: Intelligent Data Analysis, vol. 16, no. 3, pp. 467-485, 2012