CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph
  Neural Networks
Yifan Duan Guibin Zhang Shilong Wang Xiaojiang Peng Wang Ziqi Junyuan Mao Hao Wu Xinke Jiang Kun Wang
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.