Connecting The Dots To Combat Collective Fraud
Mingxi Wu, Xi Chen

TL;DR
This paper presents a real-time risk control system leveraging graph databases and query languages to detect and combat coordinated collective fraud in online platforms.
Contribution
It demonstrates how to build an effective, real-time collective fraud detection system using TigerGraph and GSQL, integrating data science and fraud expertise.
Findings
Successful implementation of a graph-based fraud detection system
Real-time detection capabilities demonstrated
Enhanced coordination between data scientists and fraud experts
Abstract
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.
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Taxonomy
TopicsCybercrime and Law Enforcement Studies · Crime, Illicit Activities, and Governance · Blockchain Technology Applications and Security
