  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.

Cryptography and Security;Databases