Fraud detection

Authors:
Tom E. Fawcett;Foster Provost
Affiliations:
Research Scientist, Hewlett-Packard Labs, Palo Alto, California;Associate Professor of Information Systems, Leonard N. Stern School of Business, New York University, New York
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

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Abstract

Fraud is the deliberate use of deception to conduct illicit activities. Automatic fraud detection involves scanning large volumes of data to uncover patterns of fradulent usage, and as such it is well suited to data mining techniques. We present three general types of fraud that have been addressed in data mining research, and we summarize the approaches taken. We also discuss general characteristics of fraud detection problems that make them difficult, as well as system integration issues for automatic fraud detection systems.