xFraud: Explainable Fraud Transaction Detection
This work addresses the problem of fraud detection and explainability for online retail platforms, aiming to improve customer experience and minimize financial loss.
This paper introduces xFraud, a framework for explainable fraud transaction detection. It effectively predicts transaction legitimacy using a heterogeneous graph neural network and generates human-understandable explanations from graphs, outperforming various baseline models on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges.
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.