US tornadoes: Brit turns to robot to better grasp complex damage claims
Specialty re/insurer Brit has turned to machine learning algorithms to accelerate the identification of damage caused by last week’s catastrophic tornadoes in several US states.
The insurer will use a proprietary machine learning algorithm in tandem with ultra-high-resolution imagery to expedite the identification of US Tornado insured property damage, which was previously used in the wake of Hurricane Ida.
The machine learning algorithm, developed by Brit’s Data Science team, assesses ultra-high-resolution aerial images and data, pinpointing, color-coding, and displaying properties by damage classification in the aftermath of catastrophic events. The technology enables Brit’s Claims team to proactively identify, triage and assign response activity even before the claims are reported.
Brit has been working with the Geospatial Insurance Consortium (GIC) since April 2019, a non-profit organisation that captures post-event aerial imagery for first responders and insurance companies. Utilising the GIC images and Brit’s machine learning algorithm, its claims team has 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.
Mike Barry, head of global property claims for Brit, said: “Innovation is a central pillar to Brit’s Claims strategy, and this includes a number of virtual and digital claims solutions for our customers when they need us the most. We continue to evolve and innovate the use of our best in class aerial imagery and the damage classification algorithm developed earlier this year, enabling quicker and more efficient identification and payment of claims.”
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