PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks

2025-10-14

AI Summary

Promotional abuse fraud—comprising coordinated hoarding and cashback abuse—is increasingly prevalent on e-commerce platforms. Method: We propose a Multi-Relation Fusion Graph Neural Network (MRFGNN) that unifies spatial proximity and temporal dynamics of user transaction behaviors within a homogeneous graph, leveraging multiple edge types for fine-grained message passing and aggregation. Contribution/Results: This is the first systematic modeling of promotional abuse fraud in Meituan’s real-world production environment. Experiments demonstrate an accuracy of 93.15%, enabling the production system to identify 2.1–5.0× more fraudsters and reduce financial losses by 1.5–8.8× compared to baseline methods. MRFGNN significantly outperforms state-of-the-art approaches in detecting complex, interwoven fraudulent patterns.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

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.

Problem

• Detecting group-based promotion abuse fraud in e-commerce
• Identifying intertwined fraudulent activities like Stocking Up
• Integrating spatial-temporal transaction data into graph neural networks

Innovation

• Multi-relation fused graph neural network model
• Integrates spatial-temporal transaction data information
• Detects promotion abuse fraud in e-commerce platforms