Off-the-peg and bespoke classifiers for fraud detection

Piotr Juszczak, Niall M. Adams, David J. Hand, Christopher Whitrow and David J. Weston

Computational Statistics & Data Analysis, 2008, vol. 52, issue 9, 4521-4532

Abstract: Detecting fraudulent plastic card transactions is an important and challenging problem. The challenges arise from a number of factors including the sheer volume of transactions financial institutions have to process, the asynchronous and heterogeneous nature of transactions, and the adaptive behaviour of fraudsters. In this fraud detection problem the performance of a supervised two-class classification approach is compared with performance of an unsupervised one-class classification approach. Attention is focussed primarily on one-class classification approaches. Useful representations of transaction records, and ways of combining different one-class classifiers are described. Assessment of performance for such problems is complicated by the need for timely decision making. Performance assessment measures are discussed, and the performance of a number of one- and two-class classification methods is assessed using two large, real world personal banking data sets.

Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00171-0
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:9:p:4521-4532

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().