Brit Launches Machine Learning Algorithm to Accelerate Catastrophe Claims Response
Brit Launches Machine Learning Algorithm to Accelerate Catastrophe Claims Response
Ltd (“Brit”) announces the creation and successful proof of concept launch of a proprietary machine learning algorithm designed 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.

In this latest innovation, Brit’s Data Science team developed and overlaid a machine learning algorithm to access the ultra-high-resolution ariel images and data such that it pinpoints, color-codes, and displays properties by damage classification within days after a catastrophe event. This enables the Brit Claims team to proactively identify, triage and assign response activity even before claims are reported.

Brit has been working successfully with GIC (Geospatial Insurance Consortium) since April 2019, a non-profit organisation that captures best in class post-event ariel 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 can expedite claims payments in locations that cannot be immediately serviced by local field adjusters in the initial days following catastrophe events.

Innovation is a central pillar to Brit’s Claims strategy, and this includes a number of virtual and digital claims solutions for its customers.

Sheel Sawhney, Group Head of Claims and Operations, said: “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 difficultly. 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.”

Source: Brit

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