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
