Fraud detection in telecommunications using kullback-leibler divergence and latent dirichlet allocation

Authors:
Dominik Olszewski
Affiliations:
Faculty of Electrical Engineering, Warsaw University of Technology, Poland
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Year:
2011

Citing 5
Cited 1

Latent dirichlet allocation

The Journal of Machine Learning Research
Employing Latent Dirichlet Allocation for fraud detection in telecommunications

Pattern Recognition Letters
An application of supervised and unsupervised learning approaches to telecommunications fraud detection

Knowledge-Based Systems
Designing an expert system for fraud detection in private telecommunications networks

Expert Systems with Applications: An International Journal
A data mining framework for detecting subscription fraud in telecommunication

Engineering Applications of Artificial Intelligence

Measuring the privacy of user profiles in personalized information systems

Future Generation Computer Systems

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Abstract

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.