CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks ArXiv ID: 2402.14708 “View on arXiv” Authors: Unknown 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. ...