Device fingerprinting capabilities should be considered a key capability for any fraud detection service. But as fraudsters continue to develop new and sophisticated methods of evading detection, even the most robust fingerprinting must be combined with additional detection and response methods to distinguish trusted users from attackers and block fraud tactics that can be used to compromise trusted devices or bypass device reputation measures.
In this follow-up blog post to our first post on device fingerprinting, we’ll explain some of the key challenges with creating strong device fingerprints, how fraudsters evade device reputation measures and the detection methods used on the Transmit Security Platform to create robust fingerprints and supplement them with additional detection methods.
Because device fingerprints are based on unique hardware and software properties that can change over time, may contain similarities to other devices with similar settings and can be masked by both legitimate and malicious users, fingerprints must be constantly updated to ensure a sufficient level of accuracy.
Some of the challenges associated with device fingerprints include:
Especially with regard to fraud evasion tactics, a constantly changing landscape — including generative AI capabilities that are making it easier than ever to mount sophisticated attacks easier — overcoming these challenges becomes not only difficult, but impossible to do without additional detection capabilities.
While leveraging robust device fingerprints gives security and fraud teams a powerful tool against fraudsters, an increase in organized crime and an ever-evolving range of fraud tactics can enable fraudsters to bypass device reputation checks and evade even strong fingerprints.
Some of the advanced techniques used to evade robust device fingerprints include:
Especially as generative AI and other tools enable even novice fraudsters to develop sophisticated attacks, security and fraud teams should prepare themselves to defend against these advanced attacks with not only robust device fingerprints, but multiple methods of detection.
To provide a higher level of assurance for trusted devices and known malicious ones, Transmit Security’s Detection and Response Services deploy automatic updates to our device fingerprinting capabilities and balance the tradeoff between consistency and uniqueness in device fingerprints by leveraging large datasets and a wide range of device telemetry, such as device model and distribution, as shown below, as well as unique device properties.
Using these techniques, Transmit Security is able to deliver a 97% true acceptance rate and a 99.7% true rejection rate for device fingerprints — a significantly higher level of assurance than competing solutions.
While device fingerprinting is an effective way to detect and prevent fraud, it’s not a silver bullet. The best strategy is to combine device fingerprint capabilities with other data points such as behavioral biometrics, network reputation, and app activity using advanced AI to get full coverage from advanced types of fraud, which is precisely what Transmit Security Detection and Response is built for.
To learn first hand how to leverage Detection and Response to improve fraud detection, check out our Attack Simulator, which lets you test how automated attacks with evasive properties can be detected on our platform.
Or, to learn about the results businesses can achieve with Detection and Response, read our case study from a leading bank that leveraged our services to detect 10x as many fraud cases as three legacy vendors combined and gain a 1300% return on investment through fraud reduction and vendor consolidation.