Statistical fraud detection: a review. (English) Zbl 1013.62115

Summary: Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.


62P99 Applications of statistics
62P25 Applications of statistics to social sciences


C4.5; FAIS
Full Text: DOI


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[118] Provost and Simonoff, 2001).] Also, to succeed at detecting fraud, different sorts of modeling techniques must be composed, for example, temporal patterns may become features for a sy stem for estimating class membership probabilities, and estimators of class membership probability could be used in temporal evidence gathering. Furthermore, sy stems using different solution methods should be on equal footing for comparison. Seeming success on any subproblem does not necessarily imply success on the greater problem. Finally, it would be beneficial to focus researchers from many disciplines, with many complementary techniques, on a common, very important set of problems. The juxtaposition of knowledge and ideas from multiple disciplines will benefit them all and will be facilitated by the precise formulation of a problem of common interest.Of course I am not arguing that research must address all of these criteria simultaneously (immediately), and I am not being strongly critical of prior work on fraud detection: we all must abstract away parts of such a complicated problem to make progress on others. Nevertheless, it is important that researchers take as an ultimate goal the solution to the full problem. We all should consider carefully whether partial solutions will or will not be extensible. Fraud detection is a real, important problem with many real, interesting subproblems. Bolton and Hand’s review of the state of the art shows that there is a lot of room for useful research. However, the research community should make sure that work is progressing toward the solution to the larger problem, whether by the development of techniques that solve larger portions or by facilitating the composition of techniques in a principled manner.
[119] ). The class of problems is novel, even in machine learning. No one tool (neural nets, etc.) is instantly applicable to all of these problems. The algorithms have to be designed to fit the data. This means that an essential part of the venture is immersion in and exploration of the data. My experience is that good predictive algorithms do not appear by a selection, unguided by the data, from what algorithms are available. Furthermore, the process is one of successive informed revision. If an algorithm, for instance, has too high a false alarm rate, then one has to
[120] . Still on a temporal theme, the adaptability of fraud detection tools to the changing behavior of fraudsters must be addressed so as to ensure the continued effectiveness of a fraud detection sy stem: as new detection strategies are introduced, so fraudsters will change their behavior accordingly. Models of behavior can help with this, although the indicators of fraud that are independent of a particular account may require a different strategy. We take Breiman’s point that many of the methods we described were developed outside the narrow statistical community. However, we had not intended the word ”statistical” to refer merely to the stochastic data model-based statistics of his recent article (Breiman,
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