English(EN) RGH-GNN : Risk-Gated Heterogeneous Graph Neural Network for Financial Fraud Detection

新的RGH-GNN模型通过风险门控聚合增强金融欺诈检测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一种名为RGH-GNN的新模型，这是一种异构图神经网络，旨在改进金融欺诈检测。该模型将由LightGBM分类器生成的交易级别风险评分作为边权重，以指导其消息传递机制。通过优先处理高风险交易，RGH-GNN旨在防止标准图神经网络中均匀聚合可能导致的关键信号稀释。在IEEE-CIS欺诈检测数据集上进行测试时，RGH-GNN表现优于现有模型，AUC-ROC得分为0.914。 AI

影响该模型的风险门控方法有望为金融机构带来更准确、更高效的欺诈检测系统。

排序理由该条目是一篇研究论文，详细介绍了一个新模型及其在特定数据集上的评估。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Mythreyee Hari, Subin Sahayam, Vegesna S M Srinivasavarma · 2026-10-21 04:00

RGH-GNN：用于金融欺诈检测的风险门控异构图神经网络

arXiv:2610.27107v1 Announce Type: new Abstract: Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are continuously evolving, with fraudulent transactions often disguised to resemble …

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RGH-GNN：用于金融欺诈检测的风险门控异构图神经网络

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