Behavioral biometrics is a technology that uses data gathered from a user’s behavior to authenticate them. It contrasts with physical biometrics, which involves assessing human characteristics such as fingerprint and face recognition - physically identifiable information.
The types of behavioral biometrics include keystroke dynamics, gait analysis, cognitive biometrics, and signature analysis. Each of these can be used to authenticate users. For example, in keystroke dynamics, computers are programmed to recognize unique keyboard strokes. A user’s unique combination of keystrokes could be used to authenticate them. However, this method has its limitations, as the unique patterns of keystrokes may not be unique for all users, or the keyboard could get hacked.
If we examine gait analysis, based on the user's movements, sensors are attached to a user’s device and are used to detect their steps and other activities. Based on the trajectory of their movements, it can be determined whether the user is real or a fraud. Similarly, in cognitive biometrics, sensors are used to track a user’s behavior and examine how they use their devices and how they respond during authentication processes. Through this data analysis, patterns can be identified that indicate if a user is legitimate or if there is foul play.
Behavioral biometrics are changing the way users authenticate themselves by adding a seamless security layer. This security layer is extremely valuable in the context of the customer experience due to its passive nature. As mentioned, with behavioral authentication, the user does not actively participate in the authentication process. Instead, the user's behavioral patterns stored in the database are matched in the background. To get more sense of how behavioral biometrics work, we'll go through the most common factors used for determining whether the user is legitimate or a fake.
These are just some of the factors taken into consideration when implementing behavioral authentication. However, it is important to mention that this type of authentication must use a layered approach and consider multiple authentication factors in order to determine the identity of the user.
The most common use case for behavioral biometrics is e-commerce authentication. This method allows them to authenticate customers quickly and accurately using digital credentials. The same logic can be applied in other industries as well. For instance, behavioral biometrics can be used by financial services organizations to prevent malicious access attempts with stolen user credentials.
However, before implementing behavioral biometrics, it is crucial to understand the strengths and limitations of the method. For instance, behavioral biometrics cannot always replace traditional authentication methods like multi-factor authentication. Also, vendors must have adequate user data about their behavior for the method to work effectively. If a user interacts with secured systems briefly and only once or twice per year, alternate authentication methods must be used until sufficient data about the user’s behavior can be gathered.
With that in mind, let's mention some of the most common use cases for behavioral biometrics examples present today.
Gait recognition, analyzing user's movements and walking patterns, is a great way to authenticate on-the-go. What does that even mean? Simply put, the goal of access control is to determine who gets in and who stays out. If we are talking about a physical entry access control system, gait recognition feature could determine, with high accuracy, who is allowed to enter a specific part of a building. This would happen without any user interaction - improving the user experience while still staying secure.
In a world where banking moved to mobile, applying behavioral biometrics makes sense. This use case is not limited to only the login step of the customer's journey. Behavioral authentication keeps track and analyzes how the user interacts with the mobile bank app during their entire session. If, by any means, the bad actor manages to bypass the initial login requirement, further fraudulent actions will be prevented since their, and the legitimate user's application usage patterns won't be a match.
Ecommerce is always at the forefront of new technology adopters - behavioral biometrics is a great addition to their arsenal. Improving the customer experience, especially the authentication and checkout process, is constantly being optimized in order to minimize cart abandonment and lost profits. You can see how behavioral authentication could bring benefits to the e-commerce industry in the form of a smooth user experience by eliminating the need to authenticate multiple steps, such as the login and checkout phases.
The key benefits behavioral biometric authentication brings are the following:
The passive nature of behavioral biometrics makes the authentication process seamless.
Each industry use case requires a different set of behavioral identificators that can be easily tailored to their specific needs.
Behavioral authentication, apart from granting secure access to a service, also runs in real time. This means that the monitoring of user behavior during a session can also detect unusual patterns and prevent the potential for fraud even if the bad actor gains access.
Simultaneous analysis of multiple behavior factors is near impossible to imitate.
Since behavioral biometrics relies on unique behavioral markers of users, fraud prevention can thrive by leveraging data from behavioral biometric authentication in a variety of ways.
Some use cases for such data include understanding user behavior across channels (such as desktop, mobile, and social media), detecting abnormal patterns during authentication attempts or account login activity, and correlating customer data with their behavioral biometric login credentials. Behavioral biometrics generate a score to asses how data matches a customer’s historical behavior with their current behavior or with a representative peer group. Behavioral analytics considers an even broader context.
Let's take banking channels as an example of complex behavioral analysis. Among mentioned key factors for behavioral authentication, the analysis also takes into consideration the typical time at which the user usually logs into their mBanking application. Moreover, the IP address and geolocation are verified as a typical match. Adding a new payee could be a sign of fraud. The analysis also takes into account how and when these types of actions usually occur.
The analysis does not limit itself to the mBanking application alone. Cross-channel analysis of interacting with other applications available on the devices is also taken into consideration. All of the mentioned data, and more, is part of a carefully conducted analysis producing a complex behavioral profile of a user that is impossible to imitate. This opens door not only to detecting fraud based on previous patterns but discovering new fraud patterns that are stored for future fraud prevention instances.
As we move forward in the age of digitalization, frauds are attempting to use every method at their disposal to steal our data and personal information. However, behavioral biometrics combines authentication with unique user patterns for a more secure experience. It provides a higher degree of security than traditional password authentication, as well as customer experience benefits such as faster login times and reduced password-reset frauds. We believe that behavioral biometrics is here to stay and will only get better as time passes by. If you’re looking for a way to boost customer experience while reducing fraud and identity theft, behavioral biometrics is a solution to consider.
Behavioral biometric authentication is a technology that uses behavioral traits of users to authenticate their identity. It does this by measuring their physiological and/or behavioral traits in real time and then evaluating these patterns against a user's information stored on the device.
Behavioral biometric authentication is different from traditional authentication methods. It authenticates continuously and evaluates a user's interaction with their device in real time. This makes it more secure as it distinguishes between legitimate users and cybercriminals by identifying people based on their online behavior and interactions.
Behavioral biometrics authentication is a technology that authenticates users based on patterns in their behavior. It works by measuring a user's physiological and/or behavioral traits and comparing this with data that's already stored on file. This allows for a true frictionless authentication that is passive and secure. Behavioral biometrics can be used to recognize people by their faces, voice, or fingerprint; as well as by how they interact with a device such as a tablet, smartphone, or computer.
There are many potential benefits to behavioral biometrics authentication, including the following:
Behavioral biometrics authentication uses unique data points to continuously authenticate a user, irrespective of what they’re doing on their computer. This eliminates any opportunity for identity theft or fraud since there is no way to steal or replicate biometric information.
Behavioral biometrics authentication does not reveal user identity like traditional authentication methods do. This preserves user privacy and allows them to keep their personal information private.
Behavioral biometrics authentication evaluates a user’s ongoing interaction with their device in real time, making it harder for hackers to get around security measures.
Unlike standard security measures, which are susceptible to theft or replication, behavioral biometrics authentication is almost impossible to hack or replicate. This makes it more secure than traditional security measures.
Behavioral biometrics authentication is easier and more convenient for users than traditional security measures such as multi-factor authentication processes.
In case you're curious, feel free to contact us. Our ASEE team will be happy to hear you out.