Brit launches machine learning algorithm for rapid claims response to catastrophes

Global speciality re/insurer  Brit has launched a machine learning algorithm to enable faster claims response to catastrophe events.

The creation of a proprietary machine learning algorithm aims to to accelerate the identification of post catastrophe property damage, based on the use of ultra-high-resolution imagery.

This proof of concept is being used by the Brit Claims team and its delegated claims adjusters in the wake of Hurricane Ida, to further improve claims service and expedite payments for customers.

Developed by Brit’s Data Science team, the algorithm enables claims teams to proactively identify, triage and assign response activity even before claims are reported.

The re/insurer has been working with GIC (Geospatial Insurance Consortium) since April 2019, an organisation that captures post-event aerial imagery for first responders and insurance companies. With the GIC images and the machine learning algorithm, the Brit Claims team have a virtual claims adjusting platform that it says can expedite claims payments in locations that cannot be immediately serviced by local field adjusters in the initial days following catastrophe events.

Sheel Sawhney (pictured), group head of claims and operations at Brit, explained: “A claim is the single most important interaction that an end client will have with their insurer and this will often be at a time of significant difficulty. We are therefore continually focused on improving the service we offer and how quickly we can provide resolution for our customers. Innovation and technology are critical to the equation.

“This use of machine learning techniques and the best available imagery is further evidence of how our award-winning claims team is finding new ways to increase the speed and accuracy of claims payments.”

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