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Behavioral Biometrics: How Actions Signal Intention

What are behavioral biometrics?

Behavioral biometrics verify users based on their unique behavior patterns. They can detect and prevent fraud by spotting activity that differs from a user’s normal baseline activity. This includes how users interact with devices, applications, or online platforms.

When financial institutions (FIs) spot these deviations, it can reveal fraud and connect fraud rings. Behavioral biometrics also adds an extra layer of authentication beyond passwords or PINs. This behavioral biometric authentication makes it more difficult for fraudsters to get through.

How do behavioral biometrics stop online fraud?

Continuous authentication

Behavioral biometrics track user behavior throughout a session in real time. Then, they authenticate those actions against the user’s baseline. If the system detects anomalies or suspicious behavior, it triggers alerts for further authentication.

User profile creation

Behavioral biometrics create unique user behavior profiles by capturing and analyzing user interactions. This profile serves as a reference point for authentication and is continually updated as the user engages with the system.

Real-time analysis

Authentication is not a one-time event, but an ongoing process. Behavioral biometric systems’ real-time behavior analysis allow for immediate detection of suspicious activities.

Adaptive security

Behavioral biometric authentication systems adapt security measures based on changes in user behavior. Any deviations from a user’s profile prompts extra authentication to ensure the user’s identity.

Multi-factor authentication

Behavioral biometrics work with traditional two-factor authentication methods like passwords or device authentication. This multi-factor authentication approach provides enhanced security.

What are the most used types of behavioral biometrics?

Behavioral biometrics capture and analyze many unique behavioral signatures, including:

  • Typing patterns like keystroke speed, rhythm, and pressure.
  • Dwell time, or how long a user spends on each key, including the duration of key presses and releases.
  • Mouse movements like speed, acceleration, and trajectory as well as mouse clicks.
  • The way users click buttons or links, including the timing and sequence of clicks.
  • The patterns and pressure of touch gestures on touchscreens or mobile devices.
  • How users navigate through websites or apps and the time they spend on specific tasks.
  • How users scroll through content, including scrolling speed and patterns.
  • Unique characteristics of an individual’s voice, such as pitch, tone, and rhythm.
  • The speed, pressure, and style of an individual’s signature.

All these combine to create a user profile based on aggregated behavioral data. That acts as a reference for detecting deviations or anomalies.

Behavioral biometrics can work together or on their own based on the specific needs of the system. Using mutliple behavioral biometrics enhances the accuracy and reliability of user authentication and fraud detection.

What’s the difference between behavioral biometrics and behavioral analytics?

Though similar, there are a few key differences between behavioral biometrics and behavioral analytics. Let’s look at the main ones.

1. Focus

Behavioral biometrics focus on the unique behavioral patterns of individuals. The goal is to use these patterns as a form of biometric authentication to verify and identify users.

Behavioral analytics, on the other hand, have a broader scope and analyze user behavior in groups. The goal is to gain insights into user preferences, engagement, and potential security threats.

2. Authentication vs. analysis

Behavioral biometrics are mainly used for user authentication and identity verification. It’s a security measure to ensure that the current user’s behavior matches their established profile.

Behavioral analytics focus on understanding user behavior to improve user experience, optimize business processes, and detect security threats. They’re not necessarily tied to user authentication.

3. Data types

Behavioral biometrics rely on biometric data like physical actions or interactions.

Behavioral analytics look at a broader set of data, including user interactions with websites, applications, purchase history, and other digital activities.

4. Timing of analysis

Behavioral biometrics involve real-time analysis to verify a user’s identity during the authentication process.

Behavioral analytics can involve real-time analysis, but often focus on historical data analysis to identify patterns and trends over time.

How are behavioral biometrics used in banking fraud prevention?

FIs use behavioral biometrics for enhanced security, fraud prevention, and user authentication. Continuous biometric user authentication helps detect fraud at the time it happens. Deviations trigger fraud alerts in real time for fraud investigators to look into. That means they are one of the key forms of real-time fraud prevention for FIs.

They’re also used to verify the authenticity of financial transactions. FIs can step in to reverse or block a transaction if they notice suspicious behavioral patterns. Because biometric profiles are so accurate, relying on them to catch fraud also helps reduce false positives.

Security is top priority, but behavioral biometrics can also make user experience better. They reduce the need for things like passwords, making logins more secure and straightforward. Banks can control access based on behavior, too. If they notice restricting certain fraudulent actions, they can restrict access.

How AI enhances behavioral biometrics

AI not only boosts, but fuels behavioral biometrics authentication. There’s a handful of pieces in the behavioral biometric process AI assists in:

  • Machine learning algorithms analyze massive sets of user behavior data. Then they identify patterns and create accurate models of normal behavior for individual users.
  • AI systems create dynamic user profiles based on continuous analysis of behavioral biometrics. These profiles adapt to changes in user behavior and ensure accuracy.
  • ML models are trained to recognize anomalies or deviations from established behavioral patterns. Then the system can quickly detect unusual activities that reveal fraud.
  • AI-powered real-time monitoring of user behavioral biometrics provides a proactive defense against fraud.
  • AI’s automated processing of large datasets makes it scalable for financial institutions with a large user base. This efficiency is crucial for handling huge volumes of behavioral data.
  • AI calculates risk scores based so FIs can prioritize threat response.
  • AI helps reduce false positives by making intelligent decisions about which user behavior is normal. This ensures that legitimate users are not unnecessarily flagged or inconvenienced.

Adding AI-powered behavioral biometrics to your fraud platform provides a proactive defense that increases efficiency, reduces false positives, and improves user experience. Ready to add these powerful verification tools to your fraud solution? Set up a time to chat with our fraud experts to learn how to get started.