[论文解读] Deep Learning Methods for Credit Card Fraud Detection

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

Imbalanced Data Classification Techniques参考文献 9被引用 51

一句话总结

本文综述用于信用卡欺诈检测的深度学习方法，并将它们与传统机器学习方法在三个金融数据集上进行比较，强调性能提升和在真实世界中的适用性。

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.

研究动机与目标

• 由于欺诈率和成本上升，金融服务中对有效欺诈检测的需求日益增加。
• 综述并评估用于信用卡欺诈检测的深度学习模型。
• 在多个数据集上将深度学习方法与传统机器学习技术进行比较。
• 讨论数据不平衡、欺诈模式变化、误报等挑战，并评估实际部署的影响。

提出的方法

• 系统性评估用于信用卡欺诈检测的深度学习技术。
• 在三个金融数据集上将深度学习模型与传统机器学习算法进行基准比较。
• 分析对实际部署的性能含义。

实验结果

研究问题

• RQ1 在多个数据集上，深度学习方法相对于传统机器学习模型在信用卡欺诈检测中的性能如何？
• RQ2 哪些挑战（如类别不平衡、概念漂移、误报）会影响欺诈检测系统，深度学习方法如何应对它们？
• RQ3 所提出的深度学习方法在现实世界的信用卡欺诈检测部署中是否可行？

主要发现

• 实验结果表明相较于传统模型，深度学习方法在所评估的数据集上具有较强的性能。
• 深度学习方法在欺诈检测系统的实际实现方面展现出前景。
• 本研究讨论了将深度学习应用于现实世界的信用卡欺诈检测的潜在好处和局限性。