期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
页码：1-8
出版社：Science and Information Society (SAI)
摘要：The usage of credit card has increased dramatically
due to a rapid development of credit cards. Consequently, credit
card fraud and the loss to the credit card owners and credit cards
companies have been increased dramatically. Credit card
Supervised learning has been widely used to detect anomaly in
credit card transaction records based on the assumption that the
pattern of a fraud would depend on the past transaction.
However, unsupervised learning does not ignore the fact that the
fraudsters could change their approaches based on customers’
behaviors and patterns. In this study, three unsupervised methods
were presented including autoencoder, one-class support vector
machine, and robust Mahalanobis outlier detection. The dataset
used in this study is based on real-life data of credit card
transaction. Due to the availability of the response, fraud labels,
after training the models the performance of each model was
evaluated. The performance of these three methods is discussed
extensively in the manuscript. For one-class SVM and auto
encoder, the normal transaction labels were used for training.
However, the advantages of robust Mahalanobis method over
these methods is that it does not need any label for its training.
