As e-commerce platforms develop, fraudulent activities are increasingly emerging, posing significant threats to the security and stability of these platforms. Promotion abuse is one of the fastest-growing types of fraud in recent years and is characterized by users exploiting promotional activities to gain financial benefits from the platform. To investigate this issue, we conduct the first study on promotion abuse fraud in e-commerce platforms MEITUAN. We find that promotion abuse fraud is a group-based fraudulent activity with two types of fraudulent activities: Stocking Up and Cashback Abuse. Unlike traditional fraudulent activities such as fake reviews, promotion abuse fraud typically involves ordinary customers conducting legitimate transactions and these two types of fraudulent activities are often intertwined. To address this issue, we propose leveraging additional information from the spatial and temporal perspectives to detect promotion abuse fraud. In this paper, we introduce PROMOGUARDIAN, a novel multi-relation fused graph neural network that integrates the spatial and temporal information of transaction data into a homogeneous graph to detect promotion abuse fraud. We conduct extensive experiments on real-world data from MEITUAN, and the results demonstrate that our proposed model outperforms state-of-the-art methods in promotion abuse fraud detection, achieving 93.15% precision, detecting 2.1 to 5.0 times more fraudsters, and preventing 1.5 to 8.8 times more financial losses in production environments.

翻译：随着电子商务平台的发展，欺诈活动日益增多，对平台的安全与稳定构成严重威胁。促销滥用是近年来增长最快的欺诈类型之一，其特点是用户利用促销活动从平台获取经济利益。为研究此问题，我们首次对美团电商平台的促销滥用欺诈展开研究。我们发现促销滥用欺诈是一种基于群体的欺诈活动，包含囤货和返现滥用两种类型。与虚假评论等传统欺诈活动不同，促销滥用欺诈通常涉及普通用户进行合法交易，且这两种欺诈类型常相互交织。为解决该问题，我们提出利用时空维度的附加信息来检测促销滥用欺诈。本文提出PROMOGUARDIAN——一种新颖的多关系融合图神经网络，它将交易数据的时空信息整合到同质图中以检测促销滥用欺诈。我们在美团真实数据上进行了大量实验，结果表明所提模型在促销滥用欺诈检测中优于现有最优方法，在生产环境中实现了93.15%的精确率，检测出的欺诈者数量提升2.1至5.0倍，并成功阻止了1.5至8.8倍的经济损失。