As more data breaches occur and more data is flooded onto the internet, customers are in real danger of having their information exposed and used fraudulently. At the same time, bots and non-human traffic are getting more sophisticated and less costly. Creating new accounts or stealing information to make purchases from other accounts is becoming easier every day. This leaves companies vulnerable to losing more money to fraud than ever.
Behavioral Analytics Spotlight Fraud
While many eCommerce fraud prevention companies focus on payment data and known user attributes to detect fraud, NS8 expands on this technique. We use behavioral analytics algorithms to supplement that information. By observing movements and choices made by users, we can see fraud indicators that other software does not.
So how does it work? We distinguish non-human activity and better identify fraudsters by looking beyond information the user provides and focusing on how a user behaves. As with any other data, these behaviors are weighted and considered together and contribute to the overall EQ8 Score. Behaviors we watch for at NS8 include site navigation, hidden sessions, VPN or proxy use, repeat visits or patterns based on user ID, velocity checks, pre-session data, and others.
How Behaviors Show Fraud
While a user may have all the right information to mimic a basic online identity, it is much harder to recreate all of the behaviors of real humans. Anything from entering a site through the checkout page to making a purchase in less than 3 seconds could indicate a non-human user. Patterns are also a strong indication of fraud. While humans create some patterns, we do not generally do the same thing, in the same way, over and over again without variation. Behavioral analytics can pick up on navigation patterns, click patterns, orders, and other movements that are consistently the same every time, suggesting fraud.
Combining this information with the static data points generally used by fraud prevention software ensures that NS8 has a more complete view of every user. Companies can then make informed decisions about orders based on all of the information available, which leads to more fraud being captured and less false declines.
Beyond Identifying Fraud
The use of this additional information also means that we can do more than simply identify fraud. Because we are watching every movement a user makes, those movements can be used to trigger different actions. Our data can be used to optimize a site based on devices that are used, such as featuring Apple products for an iPhone user. It can be used to trigger promotions when an item is viewed multiple times to push a customer into finally making a purchase. It can also be used to identify bots and isolate them or move them off your site.
With less reliance on static data points, companies gain better insights into their customers and learn more ways to optimize their experience. Our technology is made to be flexible and adaptable to take advantage of these needs as part of our fraud prevention hub.