Detecting credit card fraud by modified Fisher discriminant analysis

Name: Detecting credit card fraud by modified Fisher discriminant analysis
Author: Mahmoudi, Nader, Duman, Ekrem

İsim	Detecting credit card fraud by modified Fisher discriminant analysis
Yazar	Mahmoudi, Nader, Duman, Ekrem
Basım Tarihi:	20115-04-01
Basım Yeri	- Elsevier
Konu	Credit card fraud, Linear discriminant, Fisher linear discriminant function, Modified Fisher discriminant, Profitability
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1873-6793
Kayıt Numarası	66562783-4101-4ace-a1f4-b7e32baa0d9b
Lokasyon	Industrial Engineering
Tarih	20115-04-01
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.
DOI	10.1016/j.eswa.2014.10.037
Cilt	42

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Detecting credit card fraud by modified Fisher discriminant analysis

Yazar Mahmoudi, Nader, Duman, Ekrem

Basım Tarihi 20115-04-01

Basım Yeri - Elsevier

Konu Credit card fraud, Linear discriminant, Fisher linear discriminant function, Modified Fisher discriminant, Profitability

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1873-6793

Kayıt Numarası 66562783-4101-4ace-a1f4-b7e32baa0d9b

Lokasyon Industrial Engineering

Tarih 20115-04-01

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

Örnek Metin In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.

DOI 10.1016/j.eswa.2014.10.037

Cilt 42