ID R&D touts passive liveness detection for lower abandonment rates

A switch from active to passive liveness detection for face biometrics may significantly decrease abandonment in customer onboarding rates, a new case study by ID R&D suggests.

“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,” reads the paper.

“Furthermore, user friction introduces variables of human behavior that are difficult to anticipate and measure, so the BPCER [bona fide classification error rate] observed in a real-world deployment of a high-friction solution can be higher than planned for, and the difference can be significant.”

ID R&D substantiated these claims by mentioning its study of an unnamed partner with “a large customer in the financial services sector” that used active liveness detection in its biometrics tools.

The company reportedly had a low advertised attack presentation classification error rate [APCER] rate, but in the field, they were noticing a high rate of application interruptions (an observed BPCER of around 40 percent).

“With only 60 percent of customers able to apply for an account without interruption, the impact on customer acquisition was substantial,” ID R&D wrote.

The unnamed firm then switched to ID R&D’s IDLive Face solution, a face biometrics software suite with passive liveness detection capabilities.

“Unlike an active approach, IDLive Face uses only the same single selfie image used for biometric matching, adding zero effort to the user experience,” ID R&D explains. “Zero added effort means zero potential for user error contributed by liveness detection.”

In terms of figures, the company said new customer applications went from a 60 percent completion rate to over a 95 percent rate.

“This means that over a third of all applicants went from being interrupted in their applications to completing them without interruption. The change was implemented without degrading spoof detection performance,” ID R&D adds.

The biometric firm also suggests that, while not explicitly measured in this case, an increase in the rate of completions of over 50 percent likely had a similar impact on customer acquisition and revenue.

For additional key figures and the estimated financial impact of the case study, a link to its original text is available here.

Its publication comes weeks after Uruguay-based firm Simpletech deployed voice biometrics from ID R&D to WhatsApp chatbots.

Article Topics

biometric liveness detection  |  biometrics  |  face biometrics  |  fraud prevention  |  ID R&D  |  onboarding  |  passive facial liveness  |  selfie biometrics  |  spoof detection  |  user experience