Insurance executives are still grappling with an all-too-slowly recovering economy, declining returns on equity and increasing pressure to turn a profit any way they can—and carriers offering Homeowners’ coverage face added volatility in light of recent catastrophic weather events like Superstorm Sandy.
In order to bring some balance to this equation, there is a growing need to better understand and manage risk in Homeowners’ insurance.
How Predictive Analytics Impacts Homeowners’
The use of predictive analytics in Homeowners’ insurance has steadily increased over the past several years. A majority of Homeowners’ insurers have utilized predictive analytics for many years in rate-making (i.e. loss-cost relativities, by-peril rating), rating-plan assignment (sometimes called “underwriting scorecards” for tier or company placement, declines and nonrenewals) and price-discount qualification. A widely known practice is the use of credit-based insurance scores in many states.
Homeowners’ carriers are also integrating predictive models to help with claims handling, marketing and new methods of underwriting. Claims analytics models are being used by forward-thinking carriers in several ways, including to: