The Evolution of Device Recognition to Attack Fraud at-Scale


Fraud prevention today is like a game of whack-a-mole. When one fraudster or attack method is stamped out, another arises to take its place. Similarly, when a fraud prevention solution makes life difficult for bad actors, they work to crack it.

In the years since device fingerprinting was established as a fraud-fighting best practice, several things have deprecated its effectiveness. To evolve, it’s important to understand what has changed, why it matters, and what companies should look for to ensure they are deploying future-proof fraud prevention solutions.

Traditional device fingerprinting doesn’t work well anymore

First, it is worth noting that not all device fingerprinting technologies were built for fraud prevention. A solution developed to manage users or personalize marketing for them will not withstand even the simplest tampering methods. Many companies misunderstand this and end up with a solution that gives them a false sense of security.

But even device fingerprinting solutions that are purpose-built for fraud prevention have become less effective over the last few years. The big operating systems have limited the way third-party solution providers can use device data, making it even more difficult for traditional device fingerprinting solutions to identify new and returning devices.

Fraudsters have also become increasingly sophisticated. They leverage advanced tools and tactics, like emulators, a device that enables them to tamper with apps in a synthetic environment, app cloners, and reset schemes to manipulate or disguise device attributes. For example, fraudsters will perform factory resets or reinstall apps, effectively creating a “new” device in the eyes of basic fingerprinting solutions. These “new” devices create “new” accounts that bad actors use to carry out scams at scale without detection.

At this point, fraud teams are effectively flying blind. They might see 30 new accounts when, in fact, they have all been created by the same user from the same device. Without reliable device identification capabilities, apps are significantly disadvantaged in their fight against fraud.

The advantages of a modern approach

Modern device fingerprinting solutions are purpose-built for fraud and risk management. Many of these approaches are intentionally cookieless, making them much more stable and immune to manipulation techniques like cookie theft. By focusing on high-quality data signals, not personal information, these solutions provide a much higher device recognition rate and remain privacy-centric.

Next-generation device fingerprinting solutions, like the one developed by Incognia, take a new approach to device identification. By adding new layers, like advanced tamper detection and location intelligence, modern device recognition solutions are more reliable and provide greater accuracy.

Here’s what you should look out for when evaluating device fingerprinting solutions:

  1. Tamper detection: Advanced tamper detection mechanisms identify and thwart attempts to mask device identity, ensuring that even sophisticated fraud attempts are detected.
  2. High device recognition rate: Modern device fingerprinting technologies are designed to persist against common fraud tactics such as factory resets, app reinstalls, and emulators or app cloners. Location intelligence adds an additional layer of security. By analyzing location patterns, unexpected relationships between devices and accounts often emerge, revealing risky behavior.
  3. Built-in risk analysis: Recognizing a device as new or returning is only half the battle. How do you know if it is risky or not? Look for solutions that provide you with risk analysis so that you can take action. A solution that combines hard-coded rules and machine learning models that adapt to new fraud gives you the most comprehensive and actionable solution.

As fraud continues to evolve, so should your fraud prevention vendors. If you are using a legacy device fingerprinting vendor, there is a chance that some risky behavior is slipping through the cracks. Modern device recognition signals will help protect your bottom line and users from fraud.

About the Author

André Ferraz is the Co-Founder and CEO of Incognia, the innovator of next-generation identity solutions that enable secure and seamless digital experiences, with teams in the U.S. and Brazil. André is an expert on location technology and a strong advocate for user privacy. Originally from Brazil. André founded his first company while a university student in computer science and has Endeavor Entrepreneur and Forbes Under 30 Brazil badges. Today the location technology developed by André and his co-founders has been deployed on over 200M smartphones in over 25 countries. André can be reached online at André Ferraz on LinkedIn and at our company website https://www.incognia.com/



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