  Credit card frauds are at an ever-increasing rate and have become a major
problem in the financial sector. Because of these frauds, card users are
hesitant in making purchases and both the merchants and financial institutions
bear heavy losses. Some major challenges in credit card frauds involve the
availability of public data, high class imbalance in data, changing nature of
frauds and the high number of false alarms. Machine learning techniques have
been used to detect credit card frauds but no fraud detection systems have been
able to offer great efficiency to date. Recent development of deep learning has
been applied to solve complex problems in various areas. This paper presents a
thorough study of deep learning methods for the credit card fraud detection
problem and compare their performance with various machine learning algorithms
on three different financial datasets. Experimental results show great
performance of the proposed deep learning methods against traditional machine
learning models and imply that the proposed approaches can be implemented
effectively for real-world credit card fraud detection systems.
