Scott Waddell, Chief Technology Officer, explains iovation's unique network effect, an associations graph that makes it possible for subscribers to uncover hidden fraud rings.
When an iovation subscriber reports fraud or abuse against a device or end-user account, that report affects all associated devices and accounts, even across multiple subscribers. This means that if one device in a fraud ring is flagged, any device associated with that fraud ring is flagged as well. Our subscribers can uncover criminal networks thanks to this network effect.
See https://www.iovation.com/fraudforce-fraud-detection-prevention to learn all about the iovation consortium and schedule a demo today.
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Hi, I'm Scott Waddell, CTO here at iovation, where one of our core principles is the network effect and how it helps our customers fight fraud and streamline authentication.
You're already familiar with the network effect if you use social media. If you're on Facebook or Twitter, you have a fundamental understanding of how you're linked to your friends or your followers. We're using the same exact principles to understand how the devices that users bring to visit our customers attach to the accounts that those users are accessing and logging into across that set of customers. For a specific customer, they already know about their accounts. We're helping them get visibility into the devices that access those accounts, but, in aggregate, we're also helping them understand how those devices are related to one another through common account access across all of these subscribers.
That graph effect is hugely powerful. It's a big differentiator over our competitors, who tend to look at things more narrowly and only provide insight into the behaviors that are happening at a single business at a time. So now you get an early learning benefit because anyone else in the consortium who's observed bad behavior from that device can now reflect on your scores when you're looking at the transaction history.
The other aspect of it is it helps uncover fraud rings that would otherwise go unnoticed. If you think about the way fraudsters work, particularly in things like identity theft, they tend to take stolen credentials, stolen identity elements, and kind of shotgun them, onesie, twosies. So you may only see a transaction or two that would fly under the radar of your velocity rules or anything that would be sort of volume based in how you're trying to detect fraud, but in aggregate, across the hundreds of customers that iovation serves, we might see dozens or even hundreds of transactions happening in a very short period of time because of the network effect that comes from that graph. So now a fraud ring that, to you, maybe looked like a single individual, sticks out like a sore thumb in aggregate because we're able to understand its behavior across that collection of properties and help you mitigate it very quickly.
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