First Notice of Loss
Give your customers a simple and convenient experience when reporting facts of loss
Make the loss reporting process easy for your customers by asking the right questions to help ensure the proper method of inspection is selected.
Mitchell WorkCenter’s loss profiling leverages Mitchell’s Best Practice Questionnaire that accurately predicts if the vehicle is a total loss greater than 92% of the time. Based on the answers to the loss profiling questions, the system will present various outcomes, such as if the vehicle appears to be repairable or if the consumer will need to pay a deductible. Carriers are also given the freedom to customize the questions for their business needs.
Mobile First Notice of Loss
In today’s mobile-first world, we have created Mobile First Notice of Loss (FNOL) to enable consumers to start the claims process on their mobile device, anywhere, anytime.
With Mobile First Notice of Loss consumers can:
- Report facts of loss
- Capture and share photos of the damage
- View outcomes like the reparability of their vehicle
- Determine desired method of inspection
And best of all? Consumers can report the claim at a time that is convenient for them.
Integrated First Notice of Loss
Want to include the Mobile First Notice of Loss functionalities in your company’s native mobile application? With Integrated First Notice of Loss, you can easily use Mitchell’s API within your own app to create a seamless experience for your customers.
Both methods of FNOL are highly customizable and allow you to brand the application to your company standards along with configuring questions the way you want it. Additionally, there are more workflows that can be added within our Consumer Self Service products that integrates into the FNOL process.
Get a seamless experience with any First Notice of Loss method
Whether your customers choose to report facts of loss through Mobile First Notice of Loss or through your native carrier application, or even calling in by phone with an agent, you can expect consistent outcomes with Mitchell’s API.