# Connecting The Dots To Combat Collective Fraud

arXiv: https://arxiv.org/abs/2101.01898 · HTML: https://mlsanity.com/paper/2101.01898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Relational Graph Neural Networks for Fraud Detection in a Super-App environment](https://mlsanity.com/paper/2107.13673/) (2021)

## Abstract (arXiv)

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.