Biometrics is the automated recognition of individuals using unique characteristics of one’s identity to do so. The most common spoofing attack is within emails, but there are many others as fraudsters get more savvy to replicate one’s identity. And in a recent study, 80% of hacking-related breaches still involve compromised and weak credentials.
So what can individuals and companies do to protect themselves better when extortion of over 33 million records is expected to occur by 2023, and ransomware or phishing attacks occur every 11 seconds? The answer could be biometric liveness detection.
What is Biometric Liveness Detection?
Biometric liveness detection combines those individual characteristics of one’s identity that can be hacked with the ability to use extra layers to ensure facial and voice detection is more accurate. It involves using all the unique characteristics an individual holds with additional layers of recognition to ensure accuracy, making it more complex for spoofing to occur.
How Biometrics Liveness Detection Helps in Identity Proofing
Liveness detection prevents biometric spoofing by using an authentication process that verifies whether the user is a live person. As Pindrop has found in many of its technologies, like deepfake detection, technology must evolve quickly to ensure that machines are much better at biometric fraud detection than humans.
Here are five steps to understanding how biometrics liveness detection prevents spoofing.
Step 1: Learn About Liveness Detection in Biometrics Basics
Liveness detection is used to detect the spoof attempt by determining whether or not it’s an actual human or a fake in real time. Biometrics is the automated recognition of individuals using unique physical characteristics. Here’s how the two work together to create added security within technology using the example of voice biometrics.
How Liveness Detection Helps in Voice Recognition Biometrics
One in 857 calls analyzed by Pindrop were identified as fraudulent. This represented a 40% increase in fraudulent activity in just 12 months and should alarm any financial or other institution looking to protect its assets. But what is voice biometrics exactly? It’s a technology that verifies the identity of the speaker. Liveness detection determines in real-time whether a call is legitimate through voice authentication.
Liveness Detection and Facial Recognition Together
Voice recognition biometrics is becoming extremely efficient and powerful at detecting and preventing spoofing. Machines proved more effective than humans in tests of all five types of images, scoring 0% error rates across all 175,000 images. Computers were ten times quicker to recognize a photo of a live person versus a spoof.
While conversely, it took humans 4.8 seconds per image to determine liveness, it only took computers .5 seconds per image. This provides strong evidence for organizations to trust automation to prevent fraud while keeping company efficiency high. Employees can then focus on more severe or unique fraud attempts at the business instead.
Step 2: Understand Biometric Liveness Detection Methods
The second step in understanding how biometric liveness works to prevent spoofing is understanding the active versus passive liveness detection categories. The fundamental difference between the two is that active liveness performs a series of ‘challenge-response’ actions. In contrast, passive liveness conducts a series of checks without any awareness from the user.
What is Active Liveness Detection?
Active liveness detection determines whether the face or voice presented is a natural person, requiring the user to input more information or challenging them in a series of areas. They prompt the user to perform actions that cannot easily be spoofed. For instance, multifactor authentication is an example of a series of factors the user needs to do before providing access.
What is Passive Liveness Detection?
Passive liveness detection occurs more naturally in the background without any user input. This could be done using algorithms to determine identity image testing, such as skin and border textures or other means to determine if it is not a spoof. There are also crucial indicators machines can pick up to quickly choose false representation in this way where human input could not.
Step 3: Realize the Benefits of Liveness Detection for Contact Centers
Previous data shows that the rate of phone fraud in corporate call centers can jump up to 45 percent in just a few years. And if one in every 1700 calls was a fraudster — those calls can cost organizations as much as $27M annually.
4 Benefits of Liveness Detection Within Call Centers
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Preventing Spoofing Attacks in Contact Centers
Before 2020, call centers typically saw fraud rates of one out of every 770 calls, but in 2020, the ratio rose to one out of 1,074. This rise is nuanced but begins with how call center activity has changed in the past two years. For instance, some call centers saw calls increase by 800% and last 14% longer than pre-pandemic rates. Some argue that it was impossible to interact in person through various protocols that came with a nationwide pandemic. Today, this requires more layers of security to create efficiency as call centers get flooded with higher calling rates.
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Improving Multifactor Authentication
One way to create this added layer is through multifactor authentication. It means utilizing voice biometric authentication, which includes various data points to ensure the caller is genuine. This could entail voice, device, and behavior as three common data points. Machine learning is also adding extra layers as security gets more personalized.
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Saving Time and Money
Liveness detection in call centers also keeps cost per call low by ensuring the time agents are on the phone and improving customer experience through greater personalization. The more machines can do to detect spoofing before it happens, the higher the likelihood that personnel can focus on other areas with higher importance to the business.
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Productivity Gained Due to Faster Call Handling Times
The more seamless your contact center, the higher the customer experience and satisfaction overall. Voice biometrics can make a big difference in doing so.
Step 4: Implement AI and Machine Learning to Improve Liveness Detection for Your Business
Various options within Pindrop greatly help prevent fraudsters from getting through and spoofing one’s identity. One example is through call verification scores. This eliminates any spoof risk through validation data and a PIN score to provide your team with a green, red, or grey assessment. Another is analyzing data from call history, telcos, proprietary research, and intelligence derived from over 5 billion calls.
Ensure you have a solution that can prevent any fraud before it happens. And in the meantime, educate across the business on the latest solutions in anti-fraud techniques to ensure all of your employees are up to date.
