---
title: Connect guide - How to use graph networks effectively in fraud prevention
description: Your customer network heavily impacts the fraud you’re facing, so it’s critical to understand it fully. How many degrees of separation should you allow before blocking a customer with ties to a fraudster?
image: https://pages.ravelin.com/hubfs/Facebook_1200x628_%E2%80%93_33@2xsmalldots-round.png
---

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# Connect guide - How to use graph networks effectively in fraud prevention

 

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Your customer network heavily impacts the fraud you’re facing, so it’s critical to understand it fully. How many degrees of separation should you allow before blocking a customer with ties to a fraudster? 

Ravelin’s database Connect shows you all the links between new accounts and existing ones in a digestible graph network. This makes it easy to see when a new account is linked to fraud, even when the connection is indirect or distant - and allows you to decide where to draw the line on blocking customers with links to fraud. 

Read the guide to understand:

- How understanding network links can help you find the right measures to block fraud
- Why and how graph networks boost machine learning model performance
- Common traits of fraudulent networks in the key types of fraud including online payment fraud, and ATO
- How to use graph networks to detect voucher/promotion abuse, refund policy abuse and false claims in insurance

 

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