What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline

Abstract

Fraud behavior poses a severe threat to e-commerce platforms and anti-fraud systems have become indispensable infrastructure of these platforms. Recently, there have been a large number of fraud detection models proposed to monitor online purchasing transactions and extract hidden fraud patterns. Thanks to these fraud detection models, we have observed a significant reduction of committed frauds in the last several years. However, there have been an increasing number of malicious sellers on e-commerce platforms, according to our recent statistics, who purposely circumvent these online fraud detection systems by transferring their fake purchasing behaviors from online to offline. This way, the effectiveness of our existing fraud detection system built based upon online transactions is compromised. To solve this problem, we study in this paper a new problem, called offline fraud community detection, which can greatly strengthen our existing fraud detection systems. We propose a new FRaud COmmunity Detection from Online to Offline (FRODO) framework which combines the strength of both online and offline data views, especially the offline spatial-temporal data, for fraud community discovery. Moreover, a new Multi-view Heterogeneous Graph Neural Network model is proposed within our new FRODO framework which can find anomalous graph patterns such as biclique communities through only a small number of black seeds, i.e., a small number of labeled fraud users. The seeds are processed by a streamlined pipeline of three components comprised of label propagation for a high coverage, multi-view heterogeneous graph neural networks for high-risky fraud user recognition, and spatial-temporal network reconstruction and mining for offline fraud community detection. The extensive experimental results on a large real-life Taobao network, with 20 millions of users, 5 millions of product items and 30 millions of transactions, demonstrate the good effectiveness of the proposed methods.