Author
Sahin, Y., Bulkan, S., Duman, Ekrem
Publication Date
2013-11-01
Publication Place
-
Elsevier
Subject
Cost-sensitive modeling, Credit card fraud detection, Decision tree induction, Classification, Variable misclassification cost
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0957-4174
Record ID
bda23727-7c48-4215-99d1-cabd8c496926
Library Location
Industrial Engineering
Date
2013-11-01
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.
DOI
10.1016/j.eswa.2013.05.021
Cilt
40
