Understand the Value of Voice Biometrics Basics

Voice biometrics technology verifies a person's identity using their unique vocal attributes, aka how they speak. The combination of physical behaviors and voice characteristics that influence this sound is inimitable to every individual. This voice fingerprint can boost contact center security with secure customer authentication and fraud prevention. To learn more, I contacted Chris Adomaitis, director and solutions architect at Omilia, a natural language solutions (NLS) provider, to ask some questions..

 

Here is an edited version of the interview.

 

CA: Voice biometrics verifies a person’s identity using advanced speech recognition. Similar to a fingerprint, each person has a voiceprint, which is created by identifying a consumer’s unique behavioral speech patterns using them to create a personalized and improved customer service experience. Biometrics technology leverages voiceprints through audio samples provided by the caller, allowing a business to recognize them each time they call the customer service support line. The effective application of voice biometrics is what makes each phone call unique to the caller, and therefore faster and more efficient customer experience for businesses.

 

CA: AI is a core component of biometrics used to speed up and accurately identify the caller, as opposed to scanning through large amounts of data to “find a match” for them. Using AI in this manner eliminates the risk of an incorrect caller match and makes the identification simpler and faster.Biometrics uses AI to bypass several variables associated with the caller, adds data to each voiceprint (such as phone number, call history, and other information), and enhances confidence in the caller verification process. AI-powered technology then uses behavioral analysis to seek patterns to find correlations that would likely be overlooked or unrecognizable during a manual process. Incorporating AI into the biometrics solution allows businesses with high-volume customer service interactions to streamline customer service operations. That way, companies can save resources while significantly improving the consumer experience on the phone.

 

CA: One application example is to automate the consumer authentication process efficiently. When done successfully, the system will recognize and perform a function that will satisfy the caller’s request by allowing them to speak naturally, as they would with a live customer service representative. Timely automated recognition and validation of the caller’s identity create a seamless self-service experience that consumers want to use. Companies must strike a balance between “seamless and easy,” and “accurate and safe.”Vertical specialization enables automated caller interaction across business products and business functions. This balance lets callers complete a variety of requests while speaking naturally as opposed to using predetermined phrases tied to specific functions.

Properly implementing natural speech recognition with biometrics and doing so holistically is the foundation for building consumer trust and customer retention.

 

CA: For conversational AI companies using biometrics, the average industry accuracy rate is 90 percent. Omnichannel solutions leveraging voice biometrics have a 96% semantic accuracy and can create a caller voiceprint with only 10-20 seconds of audio, which is advanced for our industry. Once a caller’s voiceprint gets created, they can get authenticated within three seconds. This rapidity is particularly important because consumer fraud via phone channels has increased by 350% over the last four years.

 

CA: Proper implementation of AI is a prerequisite for improving biometrics operations and has value on multiple fronts—most notably in correlating and linking caller identities based on behavioral characteristics. This step is often overlooked during a manual process. The benefit of biometrics in fraud prevention is the ability to incorporate data points into the AI model that cannot be easily faked. That process can proactively and reactively identify fraudulent patterns on an ongoing basis.

 

CA: Today, the majority of agents become trained to identify and validate a caller a part of every call. The largest part of retraining consists of helping agents to follow a formal process, where previously, they pursued an informal or semi-formal method. Agents will need to retrain their scripts to accept variables they may formerly have disregarded, such as:

 

CA: Companies with a high-volume of customer service requests can save significant resources by integrating voice biometrics into their user authentication systems. When consumers can reduce time on the phone, it eliminates frustration and the impression that their call isn’t important. This enhanced customer experience leads to improved customer retention.

 

CA: In the future, companies can anticipate a diversified set of fraudulent activities that mimic live human methods to bypass voice biometric verification solutions. For example, there may be a rise in “deepfake” technology that enables someone to “steal” another person’s voice or face. We can also anticipate increased use of multi-channel fraud methods as people correlate data elements across a wide set of communication channels—many of which weren’t as carefully tested against fraud as voice channels. This issue is why we continue to make innovative strides in the conversational AI space and remain vigilant with our efforts to prevent fraud.

 

Successful implementation of voice biometrics helps prevent fraud and safeguard consumers’ personal information. Another benefit of voice biometrics is that more customers will talk to fewer agents and the number of conversations will increase. The future requires blending the best of AI (such as automated recognition of patterns) with the best of people (such as flagging things that don’t “look right”) using formal processes and technologies.