Fraud detection in communication networks using neural and probabilistic methods

Abstract

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.