CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

• URL: http://arxiv.org/abs/2402.14708v1
• Date: Thu, 22 Feb 2024 17:08:09 GMT
• Title: CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
• Authors: Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang
• Abstract summary: Credit card fraud poses a significant threat to the economy. This paper introduces a novel method for credit card fraud detection, the textbfunderlineCausal textbfunderlineTemporal textbfunderlineGraph textbfunderlineNeural textbfNetwork (CaT-GNN) CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability.
• Score: 14.448467344932078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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