FNOL with Crash Detection

 

What is FNOL?

 

FNOL, or First Notification of Loss, is the process used to immediately capture, report and escalate information following a road traffic incident involving one of your drivers. It plays a vital role in ensuring that claims are managed quickly and efficiently. 

Our managed FNOL solution enables immediate action, reducing your claims lifecycle and your indemnity spend. It combines Industry-leading response times with a compassionate and empathetic experience for your drivers 

 

Why is it Important?

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With this enhanced service, all alerts generated from your connected vehicle cameras are filtered through a unique Crash Detection algorithm, after which, a 24/7/365 UK based Contact Centre reviews all remaining alerts. For each detected crash, your driver will be proactively called, providing added driver support and completing your FNOL process in minutes. 

A claims handler quickly reviews liability shortly after of the incident occurring using the captured video footage and supporting vehicle data, this enables an early decision whether to settle or defend a claim. Within 15 minutes of the completion of the FNOL process for claims that need to be settled, TPI can be carried out. 

 

What is TPI?

 

TPI, or Third Party Intervention is the process used to protect your company brand image and mitigate any Third Party claims cost by way of capturing and repairing any 'Non-Fault' Third Party vehicles following a road traffic incident involving one of your drivers. This process also prevents any unexpected and inflated Credit Repair or Credit Hire claims from being presented. 

Our combined solution offers an industry leading success rate for Third Party Intervention, averaging at 85%. Repair and hire arrangements are organised immediately where required, delivering the same compassionate and empathetic experience throughout at a controlled cost. 

FNOL with Crash Detection is the only solution of it's kind currently available for vehicle operators that provides a joined-up approach, using the highest quality video evidence and supporting data. 

 

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