Industry: adaptive fraud detection

This article describes the development of a prototype data mining system for detecting cellular phone (cloning) fraud. In cellular cloning fraud, the identity of a legitimate cellular phone is programmed into another; from the second phone, calls can be made illicitly that are charged to the customer's account. The system for detecting such fraud is based on a framework that uses a sequence of data mining techniques. First, a rule learning program discovers general indicators of fraudulent behavior from a large database of defrauded accounts. Next, the indicators are used to create a set of monitors, which profile customer behavior and measure anomalies. Finally, the outputs of the monitors are assigned weights by a linear threshold unit. Experiments with the system indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, the system can be retrained as necessary to accommodate changing conditions of fraud detection environments.