Many fraud analysis systems have at their heart a rule-based engine for generating alerts about suspicious behaviors. The rules in the system are usually based on expert knowledge. Automatic rule discovery aims at using past examples of fraudulent and legitimate usage to find new patterns and rules to help distinguish between the two. Some aspects of the problem of finding rules suitable for fraud analysis make this problem unique. Among them are the following: the need to find rules combining both the properties of the customer (e.g., credit rating) and properties of the specific "behavior" which indicates fraud (e.g., number of international calls in one day); and the need for a new definition of accuracy: We need to find rules which do not necessarily classify correctly each individual "usage sample" as either fraudulent or not, but ensure the identification, with a minimum of wasted cost and effort, of most of the fraud "cases" (i.e., defrauded customers). These aspects require a special-purpose rule discovery system. We present as an example a two-stage system based on adaptation of the C4.5 rule generator, with an additional rule selection mechanism. Our experimental results indicate that this route is very promising.

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We consider prediction-model evaluation in the context of marketing-campaign planning. In order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. These should portray the model's ability to score accurately and to identify the relevant target population. In this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. We illustrate our results with a case study based on our experience from several projects.