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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14708 |
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| _version_ | 1866909406264819712 |
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| author | Duan, Yifan Zhang, Guibin Wang, Shilong Peng, Xiaojiang Ziqi, Wang Mao, Junyuan Wu, Hao Jiang, Xinke Wang, Kun |
| author_facet | Duan, Yifan Zhang, Guibin Wang, Shilong Peng, Xiaojiang Ziqi, Wang Mao, Junyuan Wu, Hao Jiang, Xinke Wang, Kun |
| contents | Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{N}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent correlations within transaction data. By decomposing the problem into discovery and intervention phases, CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability. CaT-GNN consists of two key components: Causal-Inspector and Causal-Intervener. The Causal-Inspector utilizes attention weights in the temporal attention mechanism to identify causal and environment nodes without introducing additional parameters. Subsequently, the Causal-Intervener performs a causal mixup enhancement on environment nodes based on the set of nodes. Evaluated on three datasets, including a private financial dataset and two public datasets, CaT-GNN demonstrates superior performance over existing state-of-the-art methods. Our findings highlight the potential of integrating causal reasoning with graph neural networks to improve fraud detection capabilities in financial transactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks Duan, Yifan Zhang, Guibin Wang, Shilong Peng, Xiaojiang Ziqi, Wang Mao, Junyuan Wu, Hao Jiang, Xinke Wang, Kun Machine Learning Artificial Intelligence Statistical Finance Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{N}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent correlations within transaction data. By decomposing the problem into discovery and intervention phases, CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability. CaT-GNN consists of two key components: Causal-Inspector and Causal-Intervener. The Causal-Inspector utilizes attention weights in the temporal attention mechanism to identify causal and environment nodes without introducing additional parameters. Subsequently, the Causal-Intervener performs a causal mixup enhancement on environment nodes based on the set of nodes. Evaluated on three datasets, including a private financial dataset and two public datasets, CaT-GNN demonstrates superior performance over existing state-of-the-art methods. Our findings highlight the potential of integrating causal reasoning with graph neural networks to improve fraud detection capabilities in financial transactions. |
| title | CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks |
| topic | Machine Learning Artificial Intelligence Statistical Finance |
| url | https://arxiv.org/abs/2402.14708 |