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.

本文介绍了一种名为 CaT-GNN 的新型信用卡欺诈检测方法，该方法利用因果不变性学习揭示了交易数据中的内在相关性，并通过发现和干预阶段将问题分解，通过识别事务图中的因果节点并应用因果混合策略来提高模型的鲁棒性和可解释性。实验结果表明，CaT-GNN 在多个数据集上表现出超过现有最先进方法的优越性能，突显了将因果推理与图神经网络相结合以提高金融交易中的欺诈检测能力的潜力。