Brit develops ML algorithm for catastrophe claims

Brit has launched a proprietary machine learning algorithm designed to accelerate the identification of post-catastrophe property damage using ultra-high-resolution imagery.

The proof of concept is being used by the insurer’s claims team and its delegated claims adjusters in the wake of Hurricane Ida.

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.”

To develop the new tool, Brit’s Data Science team created 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 team to proactively identify, triage and assign response activity before claims are reported.

The insurer has been working with the 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.

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