Case Study: Passive vs Active Liveness
Case Study: The Economics of User Friction in Digital Onboarding.
How a change in liveness detection approach leads to a significant drop in abandonment rate and creates long-term value for financial institutions.
This review of a bank’s transition of its customer onboarding technology illustrates how removing user friction is about more than convenience. Their upgrade from active to?passive?liveness detection demonstrates how removing friction can significantly decrease abandonment, and how that can hit the bottom line in a big way.?
Measuring algorithm performance: biometric matching and liveness detection
Biometrics are playing an increasingly vital role in digital banking, enhancing remote identity verification and authentication security with biometric checks that complement other signals of fraud. Using facial recognition demands liveness detection to prevent bad actors from spoofing biometric comparisons with screens, photos, or masks.
The measurement of liveness algorithm performance is analogous to that of biometric matching. Both exhibit false-positive and false-negative errors, with an inherent tradeoff between security and convenience; the algorithms can be tuned to optimize for either. In matching, a false-positive rate indicates the frequency of incorrect matches between genuine and impostor samples, and represents a higher security threat. The false-negative rate points to rejections of genuine customers that negatively impact user experience.
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In liveness detection, APCER1?is the rate of error in detecting a presentation attack, and liveness technology vendors tout a low or even near-zero APCER as a measure of the level of security it affords. But given the inherent trade-off between false-negative and false-positive errors, a low APCER can come at the cost of a high BPCER2,?the error rate in classifying?bona fide?customers as legitimate.
Friction contributes to a higher BPCER that is also less predictable
?An “active” liveness detection approach relies upon interactions with the user to help assess liveness, while a “passive” approach is transparent to the user, and typically uses only the same images used for biometric comparison. A BPCER can be made worse by the friction introduced by an active liveness technique. Frustration, distraction, and errors in interpreting or executing upon instructions can all increase the frequency of interruptions and failures, and can be particularly impactful in a digital onboarding process, where users are new and performing tasks for the first time. Furthermore, user friction introduces variables of human behaviour that are difficult to anticipate and measure, so the BPCER observed in a real-world deployment of a high-friction solution can be higher than planned for, and the difference can be significant.?
In another case, a well known IDV provider was using an active 3D liveness solution that involved moving the device, had reported a 29% failure rate with onboarding new customers and to add insult to injury, they were being charged for every API call. Therefore, they were paying for one in four liveness failures. That cost can have a dramatic impact on the bottom line. They were very happy to switch from active to passive liveness.
Find out more about passive facial liveness detection at ID R&D and feel free to request a demo at [email protected].