PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914092180045824 |
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| author | Li, Shaofei Han, Xiao Zhang, Ziqi Hua, Minyao Gao, Shuli Liang, Zhenkai Guo, Yao Chen, Xiangqun Li, Ding |
| author_facet | Li, Shaofei Han, Xiao Zhang, Ziqi Hua, Minyao Gao, Shuli Liang, Zhenkai Guo, Yao Chen, Xiangqun Li, Ding |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks Li, Shaofei Han, Xiao Zhang, Ziqi Hua, Minyao Gao, Shuli Liang, Zhenkai Guo, Yao Chen, Xiangqun Li, Ding Cryptography and Security 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. |
| title | PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.12652 |