Download your guide to link analysis
Get your free copy in your inbox nowGet your guide
A simple explanation of link analysis, graph networks and databases
In a nutshell, link analysis is a technique used to assess and evaluate connections between data. This is much easier and faster when the data is shown in a graph network, so sometimes link analysis is called network visualization.
Link analysis is easier and faster with graph networks
A graph network is a way of visualising connections between various types of information.
These networks are stored in graph databases.
Graph networks contain:
Nodes: circles which represent facts or data such as people, businesses, accounts, addresses. The nodes have attributes or properties which store information about the node in key/value pairs.
Edges: lines between nodes which represent the relationships. They can also have properties such as start date, length of time, distances or costs.
In a graph database, the relationships between the data are just as important as the data itself
How is a graph database different to a traditional database?
Traditional databases allow you to see blocks of facts - but if you want to find out how they’re connected, you need to work harder to do some analysis. If you’re dealing with a large amount of data this can take significant time and effort. Let’s look at example using an online bookshop...
In a graph database, all the information about a customer’s account, email, shipping address, order details and payment information is connected and visible at the same time.
You can see every order each customer has ever made on the site, how they’ve paid and where they’ve had them shipped. There are no limits on adding nodes and edges - such as the device used for the transaction, additional payment methods, shipping addresses and more.
Key benefits of using graph networks for link analysis
Intuitive and easy to use
Our brains love visualisation - over 50% of the brain is involved in visual processing, so a graph network is inherently easy to understand.
Insightful and powerful
Reveal hidden connections between fraudulent customers to build a profile of what a fraudster looks like and use this information to feed into machine learning for fraud prevention.
Save time on analysis
Spend less time on manual scanning and analysis to discover and identify trends, and get an always up-to-date picture of your customer behaviour and fraudulent activity.
Are fraudsters really so closely connected?
Yes! Fraudsters are part of a complex underground community, they are constantly talking and trading with each other. There are countless ‘how to’ tutorials for hacking and fraud on the dark web. Although perhaps as is to be expected, it was recently revealed that many payment fraud guides are actually defrauding would-be fraudsters with incomplete information and out-of-date techniques.
Card details can easily be faked or blocked, so fraudsters buy card details in the thousands. This means you might see multiple credit cards being added to an account to make new orders. Or you could notice the same device being used to open lots of new accounts quickly, with slight variants of the same email address.
Fraudsters often alert each other to share lucrative opportunities and cooperate with each other. We often seen fraudsters post on forums inviting people to make requests for an /order, with a prepared secure pick-up location address.