Voice Analytics that
Accurately Pinpoints
Claims Fraud
Clearspeed voice analytics, implemented in the early
stages of a process, enables straight-through
processing while reducing fraud loss by immediately
flagging claims that are high risk.
Enable straight-through
processing while quickly
identifying fraud
Clearspeed is the most accurate technology today for conversational fraud
analysis. Early identification is critical and when risk is alerted, Clearspeed’s
pinpoint fraud indicators quickly target specific areas of a claim that might require further analysis or investigation by your risk team.
When there is no risk identified by Clearspeed voice analytics, you will be able to more confidently move a claim through your process.
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.
How Clearspeed is deployed
Flexible options to meet your requirements
Automated Yes/No
Voice Questionnaires
Caller Conversations:
Human Agent
Caller Conversations:
Bot Agents
Clearspeed fits anywhere in the process
Clearspeed can be used to triage early in the claims process (illustrated below) or used for investigations. Wherever it’s placed, Clearspeed will save you time and money while reducing fraud exposure.
How it works
Clearspeed delivers unique risk alerts for reducing fraud,
insider threat, & safety risk that organizations often miss.
01
Customer determines who or which calls should be assessed for risk of fraud.
02
Caller speech converted into a proprietary unbiased universal data model to undergo analysis.
03
Data analyzed to determine the presence or absence of vocal characteristics associated with risk.
04
Any identified high risk is reported to the Customer.
