quickly moved out of pilot phases and
proved their viability and value around the
world. In fact, there are an estimated five
million active UBI policies in 35 different
countries. From this relatively low base,
EY estimates that UBI policies will reach
15 percent market penetration by 2020 in
Europe, Asia, and the Americas.
The Io T impacts everyone
K The view from underwriting: The
convergence of different data types
leads directly to increased precision
in assessing risk, pricing policies,
and estimating necessary reserves.
There are clear advantages over
current approaches, which rely on
backward-looking claims data and
historical risk studies. Through
constant monitoring, underwriters
can recommend real-time pricing and
policy-term modifications. They can
also model the impact of new health
and well-being services to manage
mortality and morbidity risk over time.
K Claims: The Io T is likely to drive
further evolution in claims as it orients
more toward active loss prevention.
For instance, in-home sensors can
monitor for fire, wind, and water
damage. In-vehicle sensors can also
be useful in providing warnings in
case of dangerous driving patterns.
Increasingly, within commercial
lines, fitness monitors may feature in
officer-and-director insurance. There
are also data-driven opportunities to
enhance incident management and
claims service, such as proactively
offering towing or loaner vehicles in
the event of an accident, rather than
just covering these costs.
K Commercial continues to advance:
In-vehicle sensors and tracking devices
were first installed in trucking fleets
decades ago and industrial control
systems have long been standard
within manufacturing environments.
Commercial insurers have also
matured their modeling capabilities,
especially relative to natural disasters.
These advancements paid off during
the experience of Superstorm Sandy in
2012, where insurers carefully tracked
the impact of the storm and proactively alerted policyholders of imminent
risks. Combining data—layering wearable technology data with GIS streams,
for example, and contrasting real-time
data against historical patterns—
enables deeper understanding of risk,
both in real time and across historical
K Life: Life insurers can now automate
and streamline the traditionally
intrusive and lengthy underwriting
process, because sensor data provides
the means to answer many questions from yesterday’s paper-based
application forms. This opens the
door to a greater focus on millennials
and younger customers, as well as
on policies with lower face amounts.
Wearable technology allows for
ongoing risk profiling, the promotion
of healthier lifestyles and potentially
pay-as-you-go models. There are even
opportunities to automate retirement
planning processes and offer simpler
and more affordable products.
Ways to get started
Engage customers to help formulate
your data strategy: As EY’s 2016 Sensor
Data Survey concluded, the opportunity
is ripe for insurers to enrich and deepen
their customer relationships. Sensor data
must be a lever for overhauling the value
proposition in ways that speak directly
to changing customer needs.
Test and learn with wearables: A
test-and-learn approach to wearables
can help insurers prepare for the new era
of customer relationships. Widespread
adoption of these devices presents opportunities to offer new services and improve
risk modeling. Increased and connected
data streams clarify customer needs and
life changes, which enables more targeted
service offerings. For instance, insurers,
supermarkets and healthcare companies
may offer loyalty bonuses or discounts on
well-being services, healthy food, fitness
programs, and premiums.
“Featurize” and bundle products: In
driving product innovation, insurers should
seek to go beyond personalization to truly
individualized targeting. More granular and
predictive views of customers mean insurers
will no longer be forced to generalize
product offerings with the same standard
features, pricing and access for everyone.
Instead, they can tier, target and “featurize”
offerings based on the specific needs of
much narrower and profitable segments –
even “segments of one.” Product bundling is
also critical for breaking down the barriers
between lines of business and types of coverage. For instance, life and auto policies can
be linked to increase insurers’ share of wallet
with individual customers.
Consider downselling as well as
upselling: Insurers must be prepared to
subtract features and eliminate discounts
for those individual customers who are not
likely to contribute sufficient premiums or
profits. With increased visibility into the cost
and profitability of specific customer segments and product components, insurers
can offer high-value customers enticements
to renew existing policies or buy more
products. Riskier and less profitable customers should not necessarily be encouraged to
renew or make additional purchases. For
an industry obsessed with upselling, such
data-driven downselling may seem illogical.
The business case is compelling however, as
downselling not only boosts profits, but also
optimizes the customer mix for profitability.
Engage underwriting: As new data
sources enhance the value of existing
historical data sets, underwriting teams
can and should play a more active role in
product design, especially in the realm
of modular and customizable product
architectures. By marrying different data
sets (e.g., GIS and wearable technology),
insurers can realize step gains in enhancing modeling capabilities.
Solve for technology: Legacy system
limitations and the variety and volume of
new data requires an overall ecosystem