4299 Altus Insurance Whitepaper FINAL SPREAD - Flipbook - Page 17
Question everything and make no
assumptions
An insurer might want to understand why it takes
three days on average to underwrite a risk. Are the
systems too outdated to perform to the expected
standard? Do underwriters lack the necessary data to
make faster decisions? Or are they spending too much
time on manual tasks? These are obvious questions,
all of which have an obvious solution – technology.
However, applying a technology solution may indeed
make a di昀昀erence but it is unlikely to tackle the root
cause of slow underwriting. In fact, it may simply push
the problem down to another part of the process and
another team. It doesn’t tackle the fundamental issue
which could be an unnecessary step in governance, a
culture of fear in the underwriting team, or it could be
...identify the pinch points, question
everything and make no assumptions.
any number of causes hiding within the vast estate of
an insurance company. Technology can address the
symptoms, but rarely can it tackle the root cause.
Which is why the best route through is to assess the
maturity of the underlying capabilities for the endto-end process, identify the pinch points, question
everything and make no assumptions. To draw out the
business steps, ask why things happen the way they
do and if they have to happen that way, and then start
to understand what capabilities currently exist and
which ones need to be developed further.
Capability Maturity Assessment – Sample Maturity Criteria
Maturity Criteria
Basic
Basic
Managed
Managed
Optimised
Optimised
Underwriting principles and
methodology only understood by
underwriters
Underwriting principles and
methodology understood by
core functions, but limited
understanding across the business
Underwriting principles and
methodology fully understood
across the business
Underwriting processes automated
for low complexity cover decisions,
otherwise manual intervention
required, with limited integration
Automated underwriting decisions
for simple to mid-complexity risks,
high complexity cases referred to
underwriters
Highly automated cover decision
process for all risk types, with
con昀椀gurable rules / machine
learning applied
Underwriting rules and question
sets hard coded and duplicated
across legacy systems
Underwriting rules and question
sets for some product lines hard
coded and duplicated across legacy
systems, with a move towards a
single, centralised repository
Single, central location for
underwriting rules and question
sets
Limited technical underwriting
capability, limits ability to
underwrite new products / target
new segments
Technical underwriting capability
to assess risk for targeting new
customer segments
Technical underwriting capability
to underwrite non-traditional
products, e.g. usage-based
insurance, smart home o昀昀erings,
electric vehicles, and wearables
Rating factor selection based
largely on historical data
Dedicated data analytics resource
to support accuracy of rating factor
selection, applying historical and
third party data enrichment sources
Advanced analytics integrated to
improve accuracy of rating factor
selection through big data and
machine learning
No / limited understanding of risk
aggregation
Awareness of risk aggregation,
supported by data enrichment
sources, e.g. flood risk
Detailed understanding of current /
potential risk aggregation, with KPI
monitoring, linked to clear strategy
for managing overall risk exposure
Figure 2.5: Sample underwriting capability maturity criteria
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