Recently, biometric authentication technologies have been rapidly developing, which provide users with real-time security without compromising their user experience.
In the financial sector, where most transactions are of high value, biometric technologies such as fingerprints and facial recognition have become indispensable for fraud prevention. According to the latest research, biometric technologies will be used for more than 18 billion transactions by 2021.
With the expansion of digital banking due to global restrictions due to the COVID-19 pandemic, biometric authentication will become the main means of protecting users and confirming their identity.
However, some misconceptions about the security level of these technologies and how they can be integrated into existing processes can become obstacles to the application of biometric technologies by banks and users. We offer you a look at common myths and get real information about the advantages and disadvantages of biometrics.
Myth: Face and fingerprint recognition can be easily tricked with a static fingerprint or photo
Reality: Modern complex biometric authentication systems have the ability to determine the animacy of an object. These tools allow the system to determine whether the provided biometric identifier belongs to a living person or is part of a digital or artificial object, such as a photo or 3D model.
Myth: Biometric authentication is less secure than using credentials
Reality: Biometric authentication with animate tools and anti-fraud technology is more secure because it uses a fingerprint, face, or other biometric data associated with a real person.
Unlike credentials such as PIN or personal data, biometric identifiers cannot be stolen or acquired, which provides greater security.
Myth: Biometric authentication is a violation of privacy.
Reality: Face recognition and comparison tools used for remote authentication in security systems at commercial facilities require user consent and voluntary participation in the recognition process. This is fundamentally different from the use of facial recognition technologies in public spaces, where such tools are used without the prior consent of people..
More importantly, commercial solutions with biometric authentication tools do not store original photographs for identification purposes, but instead translate facial data into a digital code, which is then used as an identifier. Moreover, this information is usually encrypted and absolutely useless for those who want to get it illegally.
Myth: Biometric authentication is not practical in the long term as face or fingerprint recognition technology will stop working as a person ages and changes in their features.
Reality: Biometric markers such as the retina of the eye remain largely unchanged throughout a person's life, while a person's face or voice may change slightly over time. Moreover, the long period required for significant changes to occur makes this problem insignificant. Some biometric access control solutions are dynamic and updated regularly.
Myth: Biometric data is only available when the face to be recognized is already known.
Reality: While biometric authentication uses a person's unique characteristics to verify their identity, there are also other uses of biometrics that can help businesses improve security and combat fraud, such as behavioral biometrics. Behavioral biometrics analyzes the user's actions by measuring how the user interacts with their device, allowing them to constantly verify their identity. Such tools may include measuring user input, tracking actions on a site or application, and their interactions with the device, such as screen pressure, keystrokes, and dynamics..
Behavioral biometrics analyzes the interactions of a consumer with a device in comparison to a previously developed user profile or “behavioral fingerprint”. If the user is unknown, such as when a person is applying to open a new bank account, behavioral biometrics tools can compare the user's behavior with that of the wider population. Thus, behavioral biometrics can be used to assess the likelihood that the actions of a new client correspond to the usual actions of a law-abiding user.
The more the similarity, the less the organization needs to worry about the identity or intentions of the user. Less similarity may indicate the need for additional tools and fraud detection tools.
