Title: Adaptive Fraud Detection
Speaker: Foster Provost, New York University
Date: Friday, December 6, 2002, 3:30 pm
Location: CoRE Building, room 601, Busch Campus, Rutgers University
Professor Provost will discuss machine learning/data mining for detecting fraud--both generally and with a case study in wireless communication. The case study was reported in our paper "Adaptive Fraud Detection," which appeared in the journal Data Mining and Knowledge Discovery. In particular, one method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators of fraudulent behavior from a large database of 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 fraud based on a database of call records. 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.
Co-sponsored by Rutgers Graduate Student Association (GSA) and DIMACS.