Over the years, technology advancements have resulted in biometric modalities being used in commercial applications such as finance and retail. Obviously, Apple & Samsung’s introduction of a fingerprint sensor in their phones has resulted in enhancing customer confidence about biometric use in mobile phones. Behavioural biometrics is the new kid in the block with respect to biometric modality usage in commercial applications. Behavioural biometric is defined as the form of identification or authentication that uses the uniqueness of a person’s interaction with the device or his or her behavioural trait. Gait recognition, keystroke, signature, and cognitive biometrics are examples of behavioural biometrics.
Exponential growth of smartphones has been instrumental in the rise of online applications, thereby paving way for newer business models. As customers look for frictionless experience, the need for enhanced security becomes paramount. The challenge for vendors today is to offer a hardware agnostic solution that offers the necessary security without compromising on customer convenience.
The financial technology (fintech) revolution is reshaping the traditional financial services ecosystem, and, more specifically, the payment industry. Financial institutions are under increasing pressure to protect customer details =and accounts from a variety of access portals. This trend is fuelling the demand for security technologies that will remain high as the nature of threats to the banking industry continues to evolve.
For example, a leading US banking group with over 500,000 customers and 300 offices in multiple states wanted to profile its customers to determine fraudulent activities. BioCatch used cognitive choice analysis, navigational analysis, and invisible cognitive challenges to determine that more than 80% of identity matched an actual customer. BioCatch platform was able to learn and create a biometric behavioural profile for users based on their daily transactions over a period of 3 months. This enabled the leading US banking group to verify its customers using biometrics while enhancing security to detect anomalies.
In another example, DARPA deployed active authentication program which addressed issues of authentication for long session. It used software based biometrics to validate unique aspects of the user intrinsically. The project used techniques such as keystroke, pressure, touch using various sensors embedded in the device to create a user profile with which the user was validated constantly.
Digital transformation is increasingly becoming synonymous in many industries and data have truly transformed into becoming the next oil. Emphasis on data security has gained much importance in the last 5 years and industry experts believe that behavioural biometrics could be the future of cybersecurity. Innovations are massively driven by the rise of technologies such as cloud platforms, artificial intelligence, machine learning, deep learning and blockchain. Device usage by individuals does not follow a fixed pattern and users cannot be identified based on a fixed template of their characteristics. AI, machine and deep learning address this challenge and help in continuously updating user characteristics, thereby offering a dynamic authentication system.
Physical biometrics has been growing in use across commercial applications with finger, face, and iris biometrics being dominantly used. Currently, iris recognition is predominantly used (and will continue to be used) in access control applications. As iris potentially gets integrated into mobile phones, it opens the possibility of a number of applications. For example, iris in mobile phones can be used as a single-sign-on (SSO) tool to gain access in various use cases, thereby enabling greater security. 3D facial recognition is a recent development in face biometrics and is expected to address certain limitations of 2D recognition, considering that the former has proven to be more accurate. The advantage of face recognition is that it is not intrusive and is low priced. For example, Apple is currently working on areas such as high-end 3D recognition and personalization of devices using biometrics. The usage of behavioural biometrics in addition to this physical biometrics is expected to offer enhanced security.
The rising trend of technology convergence and Internet of Things (IoT), along with the above-mentioned macroeconomic trends, is expected to enhance the growth prospects of behavioural biometrics. The risks associated with behavioural biometrics are expected to be low due to careful assessment by technology providers and system integrators in assessing spoofing vulnerabilities and implementing necessary design measures to ensure the robustness of the system. Behavioural biometrics is definitely creating a buzz in the industry and is expected to expand its use case beyond the financial horizon.