Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

TL;DR

This paper introduces a semi-supervised graph neural network that constructs a temporal transaction graph and uses message passing to improve credit card fraud detection, especially when labeled data is scarce.

Contribution

It proposes a novel Gated Temporal Attention Network (GTAN) that models transaction interactions and fraud patterns using a graph-based approach for enhanced detection.

Findings

GTAN outperforms state-of-the-art methods on three datasets.

The model achieves high accuracy with minimal labeled data.

Extensive experiments validate the effectiveness of the approach.

Abstract

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.