Employing Latent Dirichlet Allocation for fraud detection in telecommunications
Fraud detection in the telecommunications industry requires the identification of a small fraction of fraudulent calls from the high volume of call traffic. This presents a significant research challenge in the design of efficient and effective algorithms to combat telecommunications fraud. This paper employs Latent Dirichlet Allocation (LDA) to build user profile signatures and assumes that any significant unexplainable deviations from the normal activity of an individual user is strongly correlated with fraudulent activity. The user activity is represented as a probability distribution over call features which surmises the users calling behaviour. This probability distribution is derived from LDA which can accurately describe user profiles by combining different classes of distributions. To score calls we compare the likelihood of the user generating a call versus a fraudster generating the same call. Our experiments demonstrate that using such a probability distribution and employing even a rough profile of a fraudsters activity ameliorates the detection of fraudulent calls. Our method is computationally efficient and can scale up to a realistic real time detection.
latent dirichlet allocation fraud detection telecommunications user modelling data mining