4299 Altus Insurance Whitepaper FINAL SPREAD - Flipbook - Page 10
Chapter 1: Underwriting doesn’t
need to transform, it needs to be
optimised (cont.)
Unlike a transformation project, modernisation
doesn’t require a business to optimise every element
at once. Instead, it encourages a mindset of continual,
incremental improvement, constantly moving the
business forward, providing a flexibility that enables
natural adaptability to changing conditions. Crucially
though, before the modernisation process can begin,
an element of transformation must be undertaken –
the integration of modern so昀琀ware systems.
To truly optimise, every part of the business must
be brought into scope. Whilst we encourage starting
with underwriting, we must not ignore the other key
capabilities – product development, distribution,
claims management, policy administration, risk and
compliance, 昀椀nance and IT.
Transformation programmes have provided a glimpse
of what this future might look like in practice with
AI, data analytics, automation and cloud computing
all signi昀椀cantly enhancing the speed, accuracy,
and e昀케ciency of underwriting. The journey towards
algorithmic underwriting by default has begun but
crucially, in the Altus vision of the future, humans are
still 昀椀rmly embedded into the underwriting process.
While technology – and AI in particular – has assumed
an increasingly important role in underwriting,
modernisation won’t see humans relinquishing the
underwriting pen to machines. In the modern insurer,
technology and humans work hand in hand, picking
up and laying o昀昀 tasks back and forth, optimising
processes as they go.
Modernisation transforms the underwriting
process
Making the most of the underwriting
workbench
The three pillars of underwriting – risk assessment,
pricing and exposure (policy terms) – remain
unchanged at the modern insurer. However, the
overall assessment of that risk is much broader,
supported by hundreds of data points for any one risk,
requiring a greater level of systems support than we
currently see.
Today, the way in which those pillars support the
process is radically di昀昀erent. Traditional underwriting
waits for the risk data to be submitted before being
assessed and priced by the underwriter, requiring
a signi昀椀cant amount of human input, and bringing
signi昀椀cant potential for human error. In the modern
insurer, the underwriter is pre- armed with relevant
risk data rendering the submission itself more of a
truth checker than fact gatherer.
In the modern insurer, the underwriter
is pre- armed with relevant risk data
rendering the submission itself more of
a truth checker than fact gatherer.
10
11
The bene昀椀ts of these tools are undeniable.
Embedding machine learning and automated
underwriting into business processes has allowed
some organisations to improve their process
e昀케ciency by 30%10. This is certainly true for Vitality
where 70% of applications are now automated and
underwriting in many other insurers is heading in a
similar direction11.
One of the more recent promises to transform insurer
processes comes in the form of the underwriting
workbench, and it perfectly illustrates the challenges
of a transformational approach. Designed to
streamline and enhance the underwriting process,
the workbench seeks to bring underwriting into the
modern age; but even here, outdated processes and
systems are limiting its impact.
By stripping operations back to the bare bones of
process, organisations can start to modernise in a more
deliberate way and, whilst doing so, maximise the
investment made in existing and future technology.
https://www.mckinsey.com/capabilities/operations/our-insights/operationalizing-machine-learning-in-processes#/
https://healthcareandprotection.com/ai-is-transforming-underwriting-but-advisers-still-see-long-customer-journeys-analysis/
10
