A cost-sensitive decision tree approach for fraud detection

Name: A cost-sensitive decision tree approach for fraud detection
Author: Sahin, Y., Bulkan, S., Duman, Ekrem

İsim	A cost-sensitive decision tree approach for fraud detection
Yazar	Sahin, Y., Bulkan, S., Duman, Ekrem
Basım Tarihi:	2013-11-01
Basım Yeri	- Elsevier
Konu	Cost-sensitive modeling, Credit card fraud detection, Decision tree induction, Classification, Variable misclassification cost
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0957-4174
Kayıt Numarası	bda23727-7c48-4215-99d1-cabd8c496926
Lokasyon	Industrial Engineering
Tarih	2013-11-01
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

A cost-sensitive decision tree approach for fraud detection

Yazar Sahin, Y., Bulkan, S., Duman, Ekrem

Basım Tarihi 2013-11-01

Basım Yeri - Elsevier

Konu Cost-sensitive modeling, Credit card fraud detection, Decision tree induction, Classification, Variable misclassification cost

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0957-4174

Kayıt Numarası bda23727-7c48-4215-99d1-cabd8c496926

Lokasyon Industrial Engineering

Tarih 2013-11-01

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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