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Framework for adaptive sequential pattern recognition applied onc credit card fraud detection in the online games industry

  • Online credit card fraud presents a significant challenge in the field of eCommerce. In 2012 alone, the total loss due to credit card fraud in the US amounted to $ 54 billion. Especially online games merchants have difficulties applying standard fraud detection algorithms to achieve timely and accurate detection. This paper describes the Special constrains of this domain and highlights the reasons why conventional algorithms are not quite effective to deal with this problem. Our suggested solution for the problem originates from the fields of feature construction joined with the field of temporal sequence data mining. We present Feature construction techniques, which are able to create discriminative features based on a sequence of transaction and are able to incorporate the time into the classification process. In addition to that, a framework is presented that allows for an automated and adaptive change of features in case the underlying pattern is changing.

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Metadaten
Name:Laux, Friedrich
URN:urn:nbn:de:bsz:rt2-opus4-868
URL:http://www.iariajournals.org/software
eISSN:1942-2628
Erschienen in:International Journal on Advances in Software
Document Type:Article
Language:English
Year of Publication:2014
Creating Corporation:International Academy, Research, and Industry Association (IARIA)
Tag:binary classification; credit card fraud; feature construction; temporal data mining
Volume:7
Issue:3 & 4
Pagenumber:13
First Page:422
Last Page:434
Dewey Decimal Classification:330 Wirtschaft
Open Access:Ja
Licence (German):License Logo  Creative Commons - Namensnennung, nicht kommerziell, Weitergabe unter gleichen Bedingungen