Tom Fawcett and Foster Provost
One method for detecting fraud is to check for suspicious changes in user behavior over time. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining and machine learning techniques. It uses a rule-learning program to uncover indicators of fraudulent behavior from a large database customer transactions. Then the indicators are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies. Finally, the outputs of the monitors are used as features in a system that learns to combine evidence to generate high-confidence alarms. The system has been applied to the problem of detecting cellular cloning, but is applicable to a more general class of fraud called superimposition fraud. Experiments indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, this approach can adapt to the changing conditions typical of fraud detection environments.