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Abstract:
Fraud causes substantial losses to telecommunication
carriers. Detection systems which automatically detect illegal use
of the network can be used to alleviate the problem. Previous
approaches worked on features derived from the call patterns of
individual users. In this paper we present a call-based detection
system based on a hierarchical regime-switching model. The
detection problem is formulated as an inference problem on the
regime probabilities. Inference is implemented by applying the
junction tree algorithm to the underlying graphical model. The
dynamics are learned from data using iterative maximum likelihood
EM-learning. The methods are assessed using fraud data from a real
mobile communications network.
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