ArticlePeerJ. Computer science2023
CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- CTGAN-Based Data Augmentation and XGBoost-LSTM Strength Prediction of CSG.Materials (Basel, Switzerland) · 2026Article
- Multidimensional cfRNA response modeling identifies a 5-gene pair signature for high-robust pulmonary tuberculosis diagnosis.iScience · 2026Article
- Gated attention based generative adversarial networks for imbalanced credit card fraud detection.PeerJ. Computer science · 2025Article
- Enhancing fraud detection in auto insurance and credit card transactions: a novel approach integrating CNNs and machine learning algorithms.PeerJ. Computer science · 2024Article
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2 authors.
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Abstract
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.
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