Smart Industry 1/2019

Digital disruption is about to hit commercial insurers
full force as the industry prepares to undergo major
transformation. IoT will be at the heart of many
of the new initiatives as insurance firms find new
ways of using IoT data to assess customers,
reduce premiums, and provide real-time warnings.
n By Stian Overdahl
Collecting data is critical
for the insurance industry
because it enables underwriters
and actuaries to
estimate the likelihood and cost of
future misfortunes. Consequently,
data harvested from connected devices
embedded in the Internet of
Things could have a major impact
on the industry, giving insurers new
insights into customer behavior and
real-time risks which, in turn, could
reduce premiums for fastidious customers
while raising them for the
risk-takers.
Already there are some trailblazers.
Corvus Insurance, based in Boston,
Massachusetts, uses IoT data collected
from sensors that are transported
along with climate-controlled goods
in the food and life sciences sectors to
assess each company’s performance,
using algorithms to customize premiums
(Corvus doesn’t sell policies
to end users, but relies on brokers for
distribution). Businesses with a better
track record can also receive broader
spoiler coverage, while access to onboard
sensors allows Corvus to provide
real-time warnings to customers
of potential threats to their cargoes,
says Phil Edmundson, the company’s
CEO and founder.
In bringing its IoT insurance product
to market, Corvus investigated where
IoT could be used to predict loss, including
unusual threats like measuring
pressure from the weight of snow
on a roof, or detecting water leaks.
Despite this, a stumbling block was
the fragmented nature of the market
– while historical data to build predictive
algorithms could be obtained
from sensor manufacturers, Corvus
couldn’t easily identify who its future
customers might be.
The company subsequently narrowed
its focus to temperature-controlled
goods, partnering with supply chain
visibility specialist Sensitech,
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