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CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Feb. 23, 2024, 5:42 a.m. | Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv card credit credit card credit card fraud cs.ai cs.lg detection detection methods economy fraud fraud detection gnn graph graph neural network graph neural networks network networks neural network neural networks node novel paper predictions q-fin.st temporal threat type via
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