Deep Learning Methods for Credit Card Fraud Detection
This research addresses the significant problem of credit card fraud detection for financial institutions and card users, aiming to improve the efficiency of current detection systems.
This paper investigates deep learning methods for credit card fraud detection, comparing their performance against traditional machine learning algorithms across three financial datasets. The deep learning approaches demonstrated superior performance, suggesting their effectiveness for real-world fraud detection systems.
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.