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Abstract

A technique for automatically designing a fraud detection system using a series of machine learning methods. Data mining and constructive induction are combined with more standard machine learning techniques to design methods for detecting fraudulent usage based on profiling customer behavior. Specifically, a rule-learning is used to uncover indicators of fraudulent behavior from a large user database. These indicators are used to create profilers, which then serve as features to the fraud detection system that combines evidence from multiple profilers to generate high-confidence intervention activities when the system is deployed on-line with user data.

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