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

 


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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