Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation
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.
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Datasets
Yelp-Fraud
Amazon-Fraud
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Node Classification | Amazon-Fraud | GTAN | AUC-ROC | 97.50 | # 1 | |
| Fraud Detection | Amazon-Fraud | GTAN | AUC-ROC | 97.50 | # 1 | |
| Averaged Precision | 89.26 | # 1 | ||||
| Node Classification | Yelp-Fraud | GTAN | AUC-ROC | 94.98 | # 1 | |
| Fraud Detection | Yelp-Fraud | GTAN | AUC-ROC | 94.98 | # 1 | |
| Averaged Precision | 82.41 | # 1 |
