Voice Analytics that
Accurately Pinpoints
Resume Fraud

Clearspeed voice analytics, implemented in the early
stages of a hiring process, greatly reduces
bad hires and associated costs and impact
of resume fraud.

Who are you really hiring?

With greater than 50% of resumes reported to be fraudulent, it’s really difficult to know if you have a good match and not knowing leads to bad hiring decisions. Background checks aren’t effective in detecting most resume fraud.

By using Clearspeed, you will kickstart an amazing domino effect that will result in better hiring decisions and save you time and money.

An essential, pure, and complementary
data point in your tech ecosystem

Clearspeed provides completely objective fraud alerts, without bias or reliance on prior individual data. It can detect risk that no other technology can and it both adds to and fully complements all the AI-prediction data that you have.

It is not weighted based on someone’s history and instead provides a pure risk assessment at a specific point in time.

The domino effect of resume fraud

The presence or absence of resume fraud creates either a significant negative or positive domino effect in your hiring process. Your ability to detect it early greatly affects the risk you have in your candidate pool. You need the ability to hire transparent candidates that are less likely to result in bad hires.

How Clearspeed is deployed

Flexible options to meet your requirements

Automated Yes/No
Voice Questionnaires

Caller Conversations:
Human Agent

Caller Conversations:
Bot Agents

Better hiring powered by
Clearspeed Voice Analytics

Clearspeed Voice Analytics provides remote screening through automated voice questionnaires. A questionnaire typically contains 2–4 yes/no questions that can be answered in a five-minute phone call. Questionnaires contain your custom questions and are delivered in any language, at any scale, without bias.

How it works

Clearspeed delivers unique risk alerts for reducing fraud,
insider threat, & safety risk that organizations often miss.


Customer determines who or which calls should be assessed for risk of fraud.


Caller speech converted into a proprietary unbiased universal data model to undergo analysis.


Data analyzed to determine the presence or absence of vocal characteristics associated with risk.


Any identified high risk is reported to the Customer.