Fraud/Uncollectible Debt Detection Using a Bayesian Network Based
  Learning System: A Rare Binary Outcome with Mixed Data Structures
  The fraud/uncollectible debt problem in the telecommunications industry
presents two technical challenges: the detection and the treatment of the
account given the detection. In this paper, we focus on the first problem of
detection using Bayesian network models, and we briefly discuss the application
of a normative expert system for the treatment at the end. We apply Bayesian
network models to the problem of fraud/uncollectible debt detection for
telecommunication services. In addition to being quite successful at predicting
rare event outcomes, it is able to handle a mixture of categorical and
continuous data. We present a performance comparison using linear and
non-linear discriminant analysis, classification and regression trees, and
Bayesian network models
