Every good detective understands the significance of spotting patterns. Like a technically sophisticated modern day Sherlock Holmes, today’s fraud analyst understands that it’s usually the subtle clues that yield the big ‘Aha’ moment during an investigation. These patterns of fraud can be especially fruitful when it comes to analyzing the devices that connect to and are associated with other accounts. This is what we refer to as ‘device intelligence’ and is what enables our customers to predict and prevent fraud with a high degree of precision.

This theme is the subject of our November webinar: “Patterns of Fraud – Your Blueprint to Prevention” where I and iovation’s customer success manager, Matt Giberson, explore how device intelligence can illuminate the subtle indicators that can help reveal the patterns of fraud. We received a number of questions from attendees that we did not have a chance to address during the webinar, so we’ve answered them below.

If you missed the webinar, you can watch a replay here or download the presentation.

Q: Can you talk a little about what you mean by 'history of fraud'?

In this webinar, we talked about how iovation can be used to detect and prevent fraud based on device patterns. Even though this stops a large number of fraudulent transactions, an even more powerful capability is our ability to track the ‘reputation’ of devices, which some people might refer to as a device’s ‘history of fraud’.

When one of our customers confirms that a device has committed fraud or violated another policy or abuse, their fraud analyst will file a fraud report on our network. There are over 4,000 analysts that do that and they have filed over 43M fraud reports. When that device is seen again by one of our customers, they are informed that that device has a ‘history of fraud’ along with the specific nature of the fraud: was it involved in a loan default? Stolen or synthetic identity? First party fraud? Cheating? Credit application fraud?

Q: What types of device details are returned by iovation and are those returned in real-time?

Besides offering a decision engine (which will indicate if a transaction should be allowed, denied, or passed on for further review) that our customers configure to specify the device risk factors that their business is most concerned about, every call to our service returns back hundreds of device attributes that we have collected in real-time along with other analytics information, including our own machine-learning based score of the transaction based on risk factors. Our customers find this unique data quite valuable and will introduce this data into their own business intelligence and data analytics tools. This data is returned in real-time which is usually within approximately 100ms.

Q: How can iovation help us prevent unauthorized account access?

We recommend integrating iovation at log-in to monitor which devices are accessing the accounts and to look for suspicious activity, but we also offer new solutions beyond our flagship FraudForce fraud prevention platform. Our newest cloud-based products, ClearKey, and LaunchKey provide both device-based authentication and multifactor authentication capabilities, which are incredibly effective in preventing unauthorized account access while delivering a superior user experience to your most trusted customers.

Q: What do you return to a customer - and how much is 'scored' (preprocessed) vs left to the customer to decide on?

We return data across three different domains: 1) device information, 2) the IP-based geolocation data and 3) the business rules we will help you set up. These business rules have individual scores associated with them as well as an overall transaction score, but a majority of the information is raw data that the customer can leverage using their own internal resources and tools.

Q: Do you curate any PII associated with devices?

If a device can connect to the internet, we can recognize it and do so without using directly identifying personal information.