Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection

TL;DR

This paper introduces GCD-GNN, a graph neural network that incorporates a global confidence degree to better detect financial fraud by capturing both typical and atypical node behaviors, outperforming existing methods.

Contribution

The paper proposes a novel GNN model utilizing a global confidence degree to enhance global information capture and fraud detection accuracy in financial graphs.

Findings

GCD-GNN outperforms state-of-the-art baselines in fraud detection accuracy.

GCD-GNN$_{light}$ improves convergence and inference speed while maintaining high performance.

The global confidence degree effectively captures both typical and atypical node behaviors.

Abstract

Graph Neural Networks (GNNs) are widely used in financial fraud detection due to their excellent ability on handling graph-structured financial data and modeling multilayer connections by aggregating information of neighbors. However, these GNN-based methods focus on extracting neighbor-level information but neglect a global perspective. This paper presents the concept and calculation formula of Global Confidence Degree (GCD) and thus designs GCD-based GNN (GCD-GNN) that can address the challenges of camouflage in fraudulent activities and thus can capture more global information. To obtain a precise GCD for each node, we use a multilayer perceptron to transform features and then the new features and the corresponding prototype are used to eliminate unnecessary information. The GCD of a node evaluates the typicality of the node and thus we can leverage GCD to generate attention values for message aggregation. This process is carried out through both the original GCD and its inverse, allowing us to capture both the typical neighbors with high GCD and the atypical ones with low GCD. Extensive experiments on two public datasets demonstrate that GCD-GNN outperforms state-of-the-art baselines, highlighting the effectiveness of GCD. We also design a lightweight GCD-GNN (GCD-GNN$_{light}$) that also outperforms the baselines but is slightly weaker than GCD-GNN on fraud detection performance. However, GCD-GNN$_{light}$ obviously outperforms GCD-GNN on convergence and inference speed.