Brit has announced the creation and proof-of-concept launch of a proprietary machine-learning algorithm designed to accelerate the identification of post-catastrophe property damage.
The proof-of-concept is being used by the Brit Claims team and its delegated claims adjusters to improve claims service and expedite payments in the wake of Hurricane Ida, Brit said.
The algorithm accesses ultra-high-resolution aerial images and data, which it uses to pinpoint, colour-code and display property by damage classification within days of a catastrophe, Brit said. This enables the claims team to proactively identify, triage and assign response activity – even before claims are reported.
The algorithm is part of a collaboration between Brit and Geospatial Insurance Consortium (GIC), a non-profit organisation that captures post-event aerial imagery for first responders and insurance companies. With GIC’s images and the machine-learning algorithm, the Brit Claims team can have a virtual claims adjusting platform that can expedite payments in locations that can’t be immediately reached by local field adjusters in the days following a catastrophe, the company 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 difficulty,” said Sheel Sawhney, group head of claims and operations for Brit. “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.”
