xFraud: Explainable Fraud Transaction Detection

TL;DR

xFraud is an explainable fraud detection framework using graph neural networks that accurately predicts transaction legitimacy and provides human-understandable explanations, scalable to large transaction networks.

Contribution

The paper introduces xFraud, a novel framework combining a GNN-based detector with an explainer for transparent fraud detection in large-scale transaction data.

Findings

Outperforms baseline models in accuracy and efficiency

Scalable to networks with over a billion nodes

Provides meaningful explanations for business analysis

Abstract

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.