图欺诈检测从业者终于有了对抗伪装连接的新武器——L2IR用LLM拆穿欺诈意图，直接提升检测精度，做反欺诈系统的团队值得一试。
图欺诈检测中，欺诈者常通过与正常用户伪造大量连接来稀释欺诈信号，导致GNN检测失效。现有方法虽引入LLM提供语义线索，但未深入挖掘可疑连接背后的真实意图。L2IR框架通过LLM从用户行为和可疑连接中提取意图感知表示，区分支持性连接与误导性连接，并采用自适应自训练增强鲁棒性。在两个真实数据集上，L2IR将AUPRC提升高达8.27%，可作为GNN检测器的即插即用增强模块。
L2IR: Revealing Latent Intent in Graph Fraud Detection
Graph fraud detection has long depended on Graph Neural Networks (GNNs) to propagate and aggregate information across relational data. A critical obstacle in practice, however, is that fraudsters frequently disguise themselves by forging numerous connections with benign users, causing fraud signals to be progressively diluted during neighborhood aggregation and undermining detection reliability. While recent efforts have used Large Language Models (LLMs) to provide rich semantic cues for fraud detection, the underlying intent behind suspicious connections remains insufficiently explored. Compounding this issue, the scarcity of annotated fraud samples makes it difficult to train detectors that remain robust under heavy camouflage. To address these gaps, we propose L2IR, an LLM-driven Latent Intent Revealing framework for graph fraud detection. By uncovering latent intent from both user behaviors and suspicious connections, L2IR extracts intent-aware representations from raw behavioral traces and reasons about the true purpose behind individual connections, effectively distinguishing supportive links from misleading ones. It further incorporates adaptive self-training to enhance robustness under limited supervision. Evaluations on two real-world datasets characterized by pervasive camouflage demonstrate that L2IR surpasses strong baselines and can function as a plug-in enhancement for a range of GNN-based detectors, improving AUPRC by up to 8.27%.