Unifying Behavioral Biometrics and Device Intelligence for Enhanced Security

As fraud continues to evolve, organizations face increasing complexity in their prevention efforts. Cybercriminals are using increasingly sophisticated techniques, such as ransomware, phishing, identity theft, account takeover, and botnet attacks, to carry out fraud attacks. Fraudsters continually find new ways to commit fraud and manipulate users. While organizations are adopting new fraud detection techniques, they also struggle to maintain a smooth customer experience. Traditional methods often prove inadequate for detecting emerging fraud patterns, and overly complex rule-based approaches can negatively impact the overall customer journey. Static authentication methods, like one-time passwords (OTPs) and physical biometrics (e.g., facial recognition and fingerprints), authenticate users only once, leaving them vulnerable to cybercrimes such as session hijacking and man-in-the-middle attacks.

These static authentication methods can be manipulated by fraudsters using dark web resources and social engineering scams. To address these challenges, organizations are increasingly turning to continuous authentication solutions like Behavioral Biometrics, Location-Based Authentication, and Contextual Authentication, which provide ongoing verification of user identity throughout their session, enhancing security and reducing the risk of unauthorized access. Behavioral Biometrics solutions analyze human-device interaction by passively examining the unique digital, physical, and cognitive behaviors of users to differentiate between legitimate customers and fraudsters. These solutions leverage advanced machine learning models to assess human interaction patterns, such as mouse activity, keyboard usage, touchscreen behavior, and device handling. Each user exhibits unique patterns when interacting with a website or mobile application. Behavioral Biometrics solutions create individual customer profiles based on these patterns, generating alerts whenever anomalies are detected during a session. These anomalies can indicate non-human activity or behavior that deviates from a user's typical patterns, thus enhancing fraud detection and prevention.

Device Intelligence solutions gather and analyze various device attributes, such as device information, network type, browser, screen resolution, operating system, page navigation, and languages. These solutions manage all device-related potential threats. Initially, only static parameters, like the operating system, IP address, and device type, were collected. However, vendors are now focusing on including a broader range of device data points to build comprehensive device profiles, which include threat intelligence feeds and behavioral analytics. This enhanced approach allows for a more thorough detection and prevention of device-related threats.

End-users of these solutions face several challenges. Device Intelligence solutions often have lower accuracy rates because device data attributes, such as IP addresses, can be easily masked. Additionally, emulators can manipulate device data to alter device fingerprints. Behavioral Biometrics solutions also encounter issues, particularly high false positive rates. To address these challenges, leading Behavioral Biometrics solution providers offer layered approaches that help organizations prevent a wide range of fraud patterns. Vendors are increasingly integrating Behavioral Biometrics with Device Intelligence solutions to enhance fraud detection and prevention capabilities. The unification of these solutions enables more effective passive authentication by incorporating a wide range of data points. Vendors now offer comprehensive authentication solutions that include behavioral biometrics, device intelligence, behavioral analytics, and malware analysis, allowing organizations to address a broad spectrum of fraud detection scenarios. Additionally, by leveraging multi-factor authentication, this combination results in higher accuracy rates and a more seamless user experience.