Zhao Xiaoyan, Guan Shaopeng
School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China.
PeerJ Comput Sci. 2023 Oct 10;9:e1634. doi: 10.7717/peerj-cs.1634. eCollection 2023.
Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this article, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and temporal convolutional network (TCN) for credit card fraud detection. Our approach includes an oversampling algorithm that uses CTGAN to balance the dataset, and Neighborhood Cleaning Rule (NCL) to filter out majority class samples that overlap with the minority class. We generate synthetic minority class samples that conform to the original data distribution, resulting in a balanced dataset. We then employ TCN to analyze transaction sequences and capture long-term dependencies between data, revealing potential relationships between transaction sequences, thus achieving accurate credit card fraud detection. Experiments on three public datasets demonstrate that our proposed method outperforms current machine learning and deep learning methods, as measured by recall, F1-Score, and AUC-ROC.
信用卡欺诈会给个人和金融机构都带来重大的经济损失。在本文中，我们提出了一种名为CTCN的新颖方法，该方法使用条件表格生成对抗网络（CTGAN）和时间卷积网络（TCN）进行信用卡欺诈检测。我们的方法包括一种过采样算法，该算法使用CTGAN来平衡数据集，并使用邻域清理规则（NCL）来过滤掉与少数类重叠的多数类样本。我们生成符合原始数据分布的合成少数类样本，从而得到一个平衡的数据集。然后，我们使用TCN来分析交易序列并捕捉数据之间的长期依赖关系，揭示交易序列之间的潜在关系，从而实现准确的信用卡欺诈检测。在三个公共数据集上进行的实验表明，以召回率、F1分数和AUC-ROC衡量，我们提出的方法优于当前的机器学习和深度学习方法。
