CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

TL;DR

CaT-GNN introduces a causal invariant learning approach with a novel graph neural network architecture to improve credit card fraud detection by identifying causal nodes and enhancing robustness.

Contribution

This paper presents CaT-GNN, a new GNN-based method that incorporates causal invariant learning and a causal mixup strategy for more accurate and interpretable fraud detection.

Findings

Outperforms existing fraud detection methods on multiple datasets.

Effectively identifies causal nodes without extra parameters.

Enhances model robustness and interpretability through causal mixup.

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.