Account Risk Detection in Large-Scale Financial Graphs With Auxiliary Asset Prediction

Account risk detection, which aims to identify accounts at forced liquidation risk within financial account-asset bipartite graphs, is crucial for ensuring financial market stability and economic resilience. Although traditional node-classification-based anomaly detection techniques can be applied to this task, these approaches often exhibit two key limitations: (1) insufficient consideration of asset fluctuations, resulting in unsatisfactory accuracy; and (2) scalability challenges, making them unsuitable for large-scale financial graphs. To address these issues, we propose RiskGuard, a novel framework for account risk detection that integrates auxiliary asset prediction and gradient-based sampling. First, we introduce an auxiliary asset prediction paradigm to capture the critical influence of asset fluctuations on account risk. Rather than solely predicting account risk, our unified model employs aTemporal-AttentionNet(TANet) to jointly predict asset fluctuations and account risk. This auxiliary task enables the model to learn fluctuation-aware asset representations, significantly enhancing prediction accuracy. To overcome scalability challenges, we design GLUE, an online graph sampler leveraging gradient entropy. GLUE dynamically adjusts sampling weights based on model gradients and graph structure, prioritizing high-entropy nodes in the neighborhood for improved efficiency. Extensive experiments on five financial datasets demonstrate that RiskGuard outperforms existing techniques in accuracy while achieving high efficiency in processing large financial graphs.