Data Mining and Knowledge Discovery

, Volume 1, Issue 3, pp 291–316

Adaptive Fraud Detection

Authors

  • Tom Fawcett
    • Nynex Science and Technology
    • Nynex Science and Technology
  • Foster Provost
    • Nynex Science and Technology
    • Nynex Science and Technology
Article

DOI: 10.1023/A:1009700419189

Cite this article as:
Fawcett, T. & Provost, F. Data Mining and Knowledge Discovery (1997) 1: 291. doi:10.1023/A:1009700419189

Abstract

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.

fraud detectionrule learningprofilingconstructive inductionintrusion detectionapplications

Copyright information

© Kluwer Academic Publishers 1997