@Article{10798587.2017.1342415,
AUTHOR = {Yiğit Kültür, Mehmet Ufuk Çağlayan},
TITLE = {A Novel Cardholder Behavior Model for Detecting Credit Card Fraud},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {4},
PAGES = {807--817},
URL = {http://www.techscience.com/iasc/v24n4/39807},
ISSN = {2326-005X},
ABSTRACT = {Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses 
incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we 
focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model 
for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus 
points are proposed and evaluated for CBMs. The first focus point is building the behavior model 
using single-card transactions versus multi-card transactions. As the second focus point, we introduce 
holiday seasons as spending periods that are different from the rest of the year. The CBM is fine-tuned 
by using a real credit card transaction data-set from a leading bank in Turkey, and the credit card fraud 
detection accuracy is evaluated with respect to the abovementioned two focus points.},
DOI = {10.1080/10798587.2017.1342415}
}