Deep Learning Methods for Credit Card Fraud Detection

TL;DR

This paper reviews deep learning techniques for credit card fraud detection, demonstrating their superior performance over traditional machine learning methods across multiple datasets, and discusses their potential for real-world application.

Contribution

It provides a comprehensive comparison of deep learning and traditional machine learning methods for credit card fraud detection on various datasets.

Findings

Deep learning methods outperform traditional models in fraud detection accuracy.

Proposed approaches show potential for effective real-world implementation.

Experimental results validate the superiority of deep learning in handling class imbalance.

Abstract

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.