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The fraud/uncollectible debt problem in the telecommunications industrypresents two technical challenges: the detection and the treatment of theaccount given the detection. In this paper, we focus on the first problem ofdetection using Bayesian network models, and we briefly discuss the applicationof a normative expert system for the treatment at the end. We apply Bayesiannetwork models to the problem of fraud/uncollectible debt detection fortelecommunication services. In addition to being quite successful at predictingrare event outcomes, it is able to handle a mixture of categorical andcontinuous data. We present a performance comparison using linear andnon-linear discriminant analysis, classification and regression trees, andBayesian network models

Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures