[Paper Review] Deep Learning Methods for Credit Card Fraud Detection

Thanh Thi Nguyen, Hammad Tahir+2 more|arXiv (Cornell University)|Dec 7, 2020

Imbalanced Data Classification Techniques9 references51 citations

TL;DR

The paper surveys deep learning approaches for credit card fraud detection, comparing them to traditional machine learning methods across three financial datasets and highlighting improved performance and real-world applicability.

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.

Motivation & Objective

• Motivate the need for effective fraud detection in financial services due to rising fraud rates and costs.
• Review and evaluate deep learning models for credit card fraud detection.
• Compare deep learning approaches against traditional machine learning techniques on multiple datasets.
• Discuss challenges such as data imbalance, changing fraud patterns, and false alarms, and assess practical deployment implications.

Proposed method

• Systematically review deep learning techniques applied to credit card fraud detection.
• Benchmark deep learning models against conventional machine learning algorithms on three financial datasets.
• Analyze performance implications for real-world deployment.

Experimental results

Research questions

• RQ1 How do deep learning methods perform for credit card fraud detection compared to traditional machine learning models across multiple datasets?
• RQ2 What challenges (e.g., class imbalance, concept drift, false alarms) affect fraud detection systems, and how do deep learning approaches address them?
• RQ3 Are the proposed deep learning methods feasible for real-world credit card fraud detection deployment?

Key findings

• Experimental results indicate strong performance of deep learning methods relative to traditional models on the evaluated datasets.
• Deep learning approaches show promise for practical implementation in fraud detection systems.
• The study discusses the potential benefits and limitations of applying deep learning to real-world credit card fraud detection.