CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
<|reference_start|>CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks: 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.<|reference_end|>
@article{duan2024cat-gnn:,
    title={CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph
  Neural Networks},
    author={Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi,
  Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang},
    journal={arXiv preprint arXiv:2402.14708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14708},
    primaryClass={cs.LG cs.AI q-fin.ST}
}