Report
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
| العنوان: | Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation |
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| المؤلفون: | Xiang, Sheng, Zhu, Mingzhi, Cheng, Dawei, Li, Enxia, Zhao, Ruihui, Ouyang, Yi, Chen, Ling, Zheng, Yefeng |
| المصدر: | Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 12. 2023 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks |
| الوصف: | Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data. Comment: 9 pages, 5 figures, AAAI 2023, code: https://github.com/AI4Risk/antifraud |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1609/aaai.v37i12.26702 |
| URL الوصول: | http://arxiv.org/abs/2412.18287 |
| رقم الانضمام: | edsarx.2412.18287 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1609/aaai.v37i12.26702 |
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