4299 Altus Insurance Whitepaper FINAL SPREAD - Flipbook - Page 24
Chapter 4: Exploring the Technology
Landscape: Technology Themes and
Implementation
Insurance can’t make serious claims to being a truly modernised sector but it’s
not so backward that it’s stuck in the technological dark ages.
In fact, the UK boasts a thriving Insurtech sector with
hundreds of startups operating across the country.
Once tipped to completely replace the traditional
insurance market, Insurtechs have recently settled
into a more supportive role, enabling incumbents to
modernise their operations in very speci昀椀c ways.
Insurance has also been developing without the
assistance of Insurtechs in its wholesale adoption of
big data and advanced analytics. Underwriters have
never had so much clean, actionable data at their
disposal, and they’re using it to assess risk more
accurately and to price more e昀昀ectively than ever
before. AI, robotics, cloud computing, big data, APIs
and wearables – there is hardly a piece of cuttingedge technology that insurance isn’t using.
However, the operational impact that technology is
having is debateable, as arguably insurance continues
to lag behind other 昀椀nancial services when it comes
to delivering on modernisation, but it does have
one advantage over its peers – the sheer volume of
data the sector collects on a day-to-day basis. That
data, when identi昀椀ed, categorised, and understood
correctly, is the insurance industry’s ticket to catching
up with, and perhaps even surpassing, the levels of
modernisation seen in other 昀椀nancial services today.
Fighting data with data
The volume and availability of data in the insurance
industry today is driving the need for modernisation;
a modernisation that will be built upon even more
data. It’s a self-perpetuating cycle, one that makes
it increasingly di昀케cult for any underwriter or
organisation to compete without going all-in on data.
Evolution of where underwriting data has been typically held
Brain
Mainframe /
Green Screen
Paper
email / Online
Underwriting
Workbenches
Today, modern underwriters use data to monitor activity in real time, connect to external sources
to track changes to a risk pro昀椀le while keeping an eye on any emerging risks. Fuelled by good data
they are able to be much more selective, using data and insight to seek out the risks they want
in the areas they’re comfortable with. This approach has led rise to the modern day underwriter
workbench, a tool that is maturing, with many insurers starting to explore as a distinct system.
Basic1
LEVEL
Managed
LEVEL 2
Managed
LEVEL 3
Managed
LEVEL 4
Optimised
LEVEL 5
Initial
Document
ingestion using
PL:P to extract
data
Developing
Submissions
昀椀ltered using
context or
sentiment analysis
Connected
Submissions
connected into a
workflow
Measured
Submissions are
able to be scored
and prioritised
Optimised
Underwriting
orchestration
including pricing,
risk appetite and
propensity to
close
Figure 4.1: Evolution of underwriting data stores
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