Using biometric authentication has become a new standard on smartphones and even payment industry. Passwords and PINs are for some time now overshadowed for security reasons. Biometrics are less vulnerable for hacking and fraud, but also for convenience reasons, because users prefer seamlessness in life and payments.
This is driven both by the industry standardization initiatives and vendors continuously developing smartphone biometric authentication forms. The need for security measures is growing each day and is expected to continue growing with the introduction of new payment methods such as voice.
What is biometrics? Measurement and analysis of unique physical or behavioral characteristics especially as a means to verify personal identity. Biometric modalities in use are DNA, Iris scan, facial recognition, fingerprints, palm prints, voice recognition.
According to the new study by Juniper Research, mobile biometrics will authenticate $2 trillion worth of in-store and remote mobile payment transactions annually by 2023. The fastest growth is forecasted to come from biometrically-verified remote m-commerce transactions, reaching over 48 billion in volume by 2023. This will be around 57% of all biometric transactions, up from an estimated 28% in 2018.
Given the fact that mobile payments are expected to drive retail e-commerce sales worldwide and that the average shopper is continuously becoming digitally mature, the need for security measures and biometrics will most certainly rise even more.
Payments and behavior
The current trend in authentication – behavioral biometrics – is nothing new, but combined with machine learning, it can truly provide a better and more comprehensive defense against fraud on an individual level. The combination of physical biometrics and user behavior will enable secure cloud-based identity checks that are cross-platform and authenticate in the background. Behavioral biometrics authentication implies measurement of unique patterns to differentiate certain user’s “normal” from “deviant” behavior. All of this will result in leveraging a combination of personal and device characteristics to distinguish between legitimate customers and fraudsters.