For over a decade, a crucial part of fraud detection in the virtual world has been assigning an identity to every laptop, tablet and mobile device that accesses a website or app. Such a fingerprint, often referred as a device fingerprint or device ID, is a representation of hundreds of different device-specific values taken from an end user's device. Like in the real world, a device fingerprint aids in identification and tracking of bad actors.
As a first step to any fraud attempt, fraudsters try to hide their identity or pretend to be many different people when they are, in fact, just one person. For example, a person who owns a local barber shop may want to publish a lot of fake positive reviews about his business and negative reviews about the competitors' businesses on a review app. Obviously, the person will use a fake name because his customers and competitors know about his business. Also, the business review site probably requires a different email address for each account, so he might enter fake email addresses when setting up multiple fake accounts. Perhaps the review app even requires each account to be from a different IP address and to register a new phone number. These hurdles can make it a little more difficult for a fraudster to set up fake accounts, but they can all be easily circumvented.
Fraudsters' devices often share patterns in their set of signals. With the help of machine learning, device signal datasets render a fraud score. This score tells a story about the device and the user behind it. For example, fraudsters are 5X more likely to have flushed their browser referrer history or have null values in browser settings [source: simility.com/device-recon-results]. As fraudsters change their tactics, the most advanced device fingerprinting technologies will recognize the pattern shift, detect any fraudulent activity and automatically adjust the fraud model.
Download this whitepaper to understand the current state of device fingerprinting and where it is headed with more effective and advanced techniques such as fuzzy matching, clustering and predictive modeling.