Connecting The Dots To Combat Collective Fraud
This paper addresses the problem of detecting coordinated collective fraud for online platforms, offering a practical system implementation.
The paper describes two real-time risk control systems designed to detect collective fraud, where malicious programs coordinate groups of accounts to commit illegal acts. These systems leverage a graph database (TigerGraph) and its query language (GSQL) to identify fraudulent activities.
Modern fraudsters write malicious programs to coordinate a group of accounts to commit collective fraud for illegal profits in online platforms. These programs have access to a set of finite resources - a set of IPs, devices, and accounts etc. and sometime manipulate fake accounts to collaboratively attack the target system. Inspired by these observations, we share our experience in building two real-time risk control systems to detect collective fraud. We show that with TigerGraph, a powerful graph database, and its innovative query language - GSQL, data scientists and fraud experts can conveniently implement and deploy an end-to-end risk control system as a graph database application.