CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
CoRR(2024)
摘要
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
Causal Temporal
Graph Neural Network
(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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