Fraud detection in communication networks using neural and probabilistic methods

Fraud detection refers to the attempt to detect illegitimate usage of a communication network. Three methods to detect fraud are presented. Firstly, a feed-forward neural network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, a Gaussian mixture model is used to model the probability density of subscribers´ past behavior so that the probability of current behavior can be calculated to detect any abnormalities from the past behavior. Lastly, Bayesian networks are used to describe the statistics of a particular user and the statistics of different fraud scenarios. The Bayesian networks can be used to infer the probability of fraud given the subscribers´ behavior. The data features are derived from toll tickets. The experiments show that the methods detect over 85% of the fraudsters in our testing set without causing false alarms