Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures
This addresses fraud detection for telecom companies, but it is incremental as it applies an existing Bayesian method to a specific domain problem.
The paper tackled fraud and uncollectible debt detection in telecommunications by applying Bayesian network models, which successfully predicted rare events and handled mixed data types, outperforming methods like discriminant analysis and classification trees.
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