The device fingerprinting process gathers data about the user’s hardware and software in order to create a unique identifier for them. This allows a website or mobile app to identify users, even when they use tools to mask their identity like Virtual Private Networks or clearing cookies. It is an essential piece of a fraud detection and verification toolkit.
Browser fingerprinting android device fingerprinting has reached ubiquity in recent years, but there are still challenges for identifying mobile devices accurately. To address these challenges mobile device fingerprinting utilizes a broader set of sources of entropy than is available in a browser. It also remains stable across sessions, incognito browsing, reinstalling apps and clearing cookies.
Mobile device fingerprinting is especially useful for app marketers who run attribution campaigns that directly attribute an app install to a marketing click. Typically a mobile device ID is required for this attribution (like the IDFA or Google Advertising ID) but these IDs are not always available. This is often the case when a user has enabled limited ad tracking in their device settings, when they come from the mobile web or when they are using an ad network that does not pass along the device identifier. In these cases, mobile device fingerprinting can provide critical attribution data and allow for the correct calculation of ROI for an ad campaign.
When used for ad fraud prevention, device fingerprinting can help detect fraudulent activity like fake accounts, account takeovers and other types of online abuse. It can also help detect fraudsters who reset their devices, use emulators or otherwise try to trick anti-fraud systems into believing they are authentic.
In addition to these detection capabilities, device fingerprinting can also help prevent fraud by detecting the use of stolen or synthetic identities. Fraudsters can purchase stolen device IDs from dark web marketplaces and then use these IDs to create fraudulent accounts or make purchases. When the fingerprinting mechanism recognizes this type of behavior, it can then raise a red flag and alert a team to investigate further or suspend a transaction.
Despite its incredibly high accuracy rate, device fingerprinting is not foolproof. For example, fraudsters can use emulators to create fake versions of the original user’s device. Additionally, some devices can be modified to disable or hide many of the features that make them easy to identify. For this reason, it is important to use multiple detection techniques together in order to have the strongest possible toolkit for fraud prevention.
