The Journey Towards AI: The Impact on European Insurers

TheJourneyTowardsAI:
TheImpactonEuropeanInsurers
SpareBank 1 Insurance, LV=, Markerstudy & Direct Line Group
discuss how AI will impact the workforce

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TheJourneyTowardsAI:
TheImpactonEuropeanInsurers
Author
Morag Cuddeford Jones
Editor
Helen Raff
Head of Content
Insurance Nexus
Tel: +44 (0) 207 375 7582
Toll Free: 1 800 814 3459 ext 7582
helen.raff@insurancenexus.com
Contributors:
Dan Fiehn
Group IT Director
Markerstudy
Svein Skovly
Head of Innovation
SpareBank 1 Insurance
Andrew McMurtrie
Head of Data Science
Direct Line Group
Hugh Kenyon
Head of Pricing Analytics
LV =

The Journey Towards AI:
The Impact on European Insurers
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The question of artificial intelligence - or AI as it’s most commonly referred to -
continues to intrigue the insurance community. Is the prospect of an autonomous
system that can absorb information, derive insights then create products and strat-
egies going to change the modern insurer beyond all recognition? Or are we in the
midst of yet more buzzword fever?
In our exclusive 2017 survey of more than 250 top level insurance executives, we
discovered that more than three-quarters believed AI was going to be the‘next big
thing’and furthermore, 61% were already investing in it to some extent. Of those
already investing, 71% stated they would continue to invest in 2018, and even
increase their commitment.
What are these companies committing to? There is already some debate around
what AI actually is. Various dictionaries have settled on‘the capability of a machine
to imitate intelligent human behaviour’while some go further as to suggest AI
‘performs tasks normally requiring human intelligence, such as visual perception,
speech recognition, decision-making and translation between languages’.
“AI is a grandiose term but essentially it’s just advanced analytics and better data
management,”states LV’s Head of Pricing Analytics, Hugh Kenyon.
“We use machine learning as a term instead. AI is the next stage where companies
take the foot completely off the pedal, and hand the end to end process over to
the machine. We’re still taking the insights and using them to drive the car with
machine learning. AI is the fully self-driving car,”agrees Dan Fiehn, Group IT Director,
Markerstudy Group.
Insurers would argue then that no-one is really able to perform true AI today but
that it is a future prospect. Today’s reality is that most are somewhere on a scale
between using basic analytics and significant automation. They tend to use AI as
shorthand to refer to their attempts to move the needle along that scale.
The fact is that using computational capabilities to make the insurance organisa-
tion increasingly effective, faster, more responsive to changing customer needs and
more bespoke is gathering momentum.
What this means for insurers is a period of rapid transformation impacting every
part of the organisation. From products and services, through sales and marketing
to staffing, operations and claims, there is no area that will remain unchanged from
the introduction of AI. Our 2017 survey revealed that 83% of insurers believed AI
would transform their role somehow.
Are Machines Taking Over?
Executives surveyed revealed that AI’s top benefit was perceived to be in
overall operational efficiencies. Nearly three-quarters (72%) felt AI would reduce
turnaround times, lower costs and improved productivity. The automation aspect
was highlighted by 62% who felt it would positively impact claims processing.
There are clear benefits from automation including a reduction in data entry errors,
fast payments for clear-cut claims and the simple ability to process larger volumes
of work. However this implies that machines will be replacing the more fallible,

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Automation.
slower humans. More than half of the executives surveyed (55%) believed that AI
would put insurance jobs under threat.
Supporters of AI would argue that the job landscape is not about to shrink but will
instead transform along with the rest of the business.
“Using machine learning and open source algorithms we can do a lot of the work
that would have been in the realm of highly experienced actuarial staff. Now we
can run hundreds of models instead of just one or two. We can look at customer
needs in more detail and that has a positive impact on profits,”Fiehn explains.
“It’s a shifting role for the human,”he continues.“Look at other industries using
automation such as healthcare. Using machines for diagnosis isn’t replacing the
surgeons. It’s just making them more effective because they can spend more of
their time on problematic cases while machines can help make quicker decisions in
areas that are more certain.”
Svein Skovly, Head of Innovation at SpareBank 1 Insurance notes that automation
has brought considerable financial relief to insurers in Norway:“With the high cost
of labour in Norway, automation has been on the agenda for a long time. The
business case for it is really good.”However, he does note that increased automa-
tion brings an increased reliance on automation specialists:“We’ve focused on
building a highly competent analytics environment. It has not been that hard to
attract the right people because they’re coming into a very data-focused, highly
resourced company.”
So while machines may in some cases replace humans, they are also creating an
environment where more people with new skill sets will be required. And, being
able to process more business surely gives the insurers the ability to grow and
generate the need for yet more specialised labour.
For those companies whose staff are concerned machines will fully replace them,
there are already plans afoot to mitigate the impact. In February 20171
, UK insurer
Aviva invited any staff who felt robotics could take over their role wholesale to put
themselves forward so they could be retrained for a less process-dependent role.
However, the experts warn that insurers shouldn’t be seduced by the promise of 100
per cent machine-driven efficiency. LV’s Kenyon suggests that viewing AI as a replace-
ment, rather than supplement to human expertise is playing a dangerous game.
“Pricing has had a strong data-driven culture for some time. If you start to make
too many data-driven decisions sometimes you can come across problems where
a different viewpoint is needed but the expertise is no longer there. For example,
there is a lot of new technology in cars but the data we have is limited and we’ve
relied too heavily on analytics to process information, losing people who really
know about cars.”
He warns:“It’s a concern that subject matter experts who are still vitally important
may not have an analytical background. They will feel disempowered and your
1 http://www.insurancebusinessmag.com/uk/news/breaking-news/aviva-asks-16000-staff-if-their-jobs-can-be-do-
ne-by-robots-61106.aspx

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business loses that capability, you’re losing your knowledge base because too
much strategy has been driven by the numbers.”
What transformation looks like
Speeding up the accuracy and volumes of data processed is clearly delivering
efficiencies that impact the bottom line but increasingly sophisticated analytics
and machine learning that lead to true AI have the potential to dramatically trans-
form the organisation beyond human resource allocation.
We asked the executives interviewed for this paper where they expected AI would
effect the biggest change:
“Five years from now I can see a call-centre-centric business like ours being strongly
impacted by AI, particularly in dealing with the more straightforward cases. I can
see the biggest benefit in claims where an AI system should be able to manage
an uncomplicated case efficiently,”states Direct Line Group’s Head of Data Science,
Andrew McMurtrie.
“Insurers spend a lot of money on post event fraud. We can use data to track it back
to behaviours at the application stage. One example is applicants who change
their data of birth on the form more than five times, there’s a high possibility that
this person’s policy will lead to fraud. Machine learning can spot these patterns that
are only usually obvious afterwards. When you’re generating millions of quotes per
day online, software can separate out these red flags that would otherwise drive up
the cost of insurance,”Fiehn adds.
Skovly’s focus is on improving optimal customer contact moments.“Our most
advanced area is in customer relationship management (CRM) and sales where we
have a really advanced solution when it comes to predicting churn and product
recommendation. We’re also using the data to understand customer behaviour,
taking car telematics data not just to understand pricing and risk but to know
when you are driving and where so we can discover what time of day you’ll be
most receptive to a call.”
As a bank-assurance brand, he adds, he has the advantage of shared data to use
AI to make the most of changes in customer circumstance.“Customers hardly ever
go into the branch these days, not even to get a mortgage. We had to rethink our
selling process. We have put in place a robot to get access to mortgage informa-
tion so that when a customer remortgages we pair the information with their
insurance and other databases to come up with a personalised offer that matches
their new financial position.”
Certainly, these use cases match our survey findings, where 67% were using AI to
mitigate against fraud and 53% were using it to boost advisory excellence, includ-
ing the use of chatbots.
Interestingly, while fraud is clearly still high on the list of insurer concerns, there are
cultural variations. In Norway, Skovly reveals, the level of trust in banking institu-
tions is significantly higher than in Central Europe and the UK.“It goes way back in
Norwegian culture. If one person has a problem, the community fixes it together.
If you file a claim with an insurer, in general you won’t be suspected of fraud. As a

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Automation.
customer you expect to be trusted. If a customer does attempt fraud, the penalties
are extremely high.”
This is a factor in the Skovly’s overall aim which is for a frictionless customer
experience and where“AI becomes very important because it’s the instrument that
makes the interaction smooth. We use the data to understand the position you’re
in and give you the right offering. In claims, many customers have the money in
their account right away. The AI is vital right across pricing, underwriting and claims
as well as CRM.”
For countries where there is a more combative environment of mutual distrust, AI
will have to work even harder to provide that frictionless experience, providing the
unbiased data trail and peace of mind to both insurer and insured.
Managing AI expectations
The promise of AI is necessarily flawed. A technology in perpetual development,
it is moving at multiple speeds across multiple departments and organisations.
Where one company raises customer expectations, another must necessarily dash
them. Managing expectations both internally and externally is the insurer’s biggest
challenge today.
“Our brand is built on a premise of excellent customer service, a great claims
experience and customer care,”McMurtrie explains.“People choose Direct Line
knowing they’ll get this experience when they claim. We take a risk-based
approach when it comes to launching something that isn’t up to that standard.
From a brand protection perspective we have to be very careful how we deploy
AI-based tactics.”
For a technology that has received the amount of hype as AI has, it’s hard then to
introduce an acceptance of its potential fallability.“The outputs are only a predic-
tion,”Fiehn warns.“It won’t be 100 per cent accurate, it will make mistakes and it’s
only as good as the data it’s fed.”
For most insurers this will be a major sticking point as many continue to complain
of a lack of joined up data. In our survey, internal data quality was highlighted
as the biggest challenge (48% of respondents) to their effective use of analytics.
Sourcing external data and understanding the different types of data available
were also cited as a challenge by a significant minority of respondents (23% and
19% respectively).
Being able to feed that data into existing technology systems and keeping up with
the latest technology trends were also significant challenges (40% vs 27%) while
more than a quarter of respondents are still struggling with legacy systems (26%).
Finding investment to improve these foundational elements of AI is a struggle in
uncertain economic times, particularly with the uncertainty of Brexit and looming
regulation such as GDPR putting even more pressure on companies data capabilities.
However, insurers do recognise that the need to invest is strategically important.
“Nearly a third of insurance systems were implemented in 1990 or before.
Companies need to adjust to a new technological age and operate at multiple

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Automation.
speeds. It’s unrealistic to invest in multiple platforms so we need to find a happy
medium,”Fiehn advises.“There is talk of being agile and lean but to get the
maximum out of any opportunities companies need to have the capability to
change quickly. It’s why we’re seeing the building of ecosystems around techno-
logical capabilities rather than wholesale organisational change.”
Fiehn believes insurers need to develop a mindset that is more aligned with the
venture capital and start-up world. Insurers need to see investment in technologies
or partnerships that allow them to be flexible enough to adapt to AI as operating
expenditure rather than capital expenditure. That way, if there isn’t a return over a
short period of time, it’s possible to consider a change in direction.
As an example, Markerstudy incubates a lot of different technologies within the IT
function making the company both financially efficient and encouraging it to be
innovation-focused, led by insight to find new opportunities that aren’t limited to a
single department.
AI is a pan-organisational reality
It is clear both from the Insurance Nexus executive survey and our interviews with
top flight insurers that there are few areas of the insurer’s organisation that AI does
not touch. With so many parts of the business impacted, it begs the question, who
is driving the AI agenda?
“There is now development in analytical capability in areas where it hasn’t been
seen before and so oversight of analytics is becoming more centralised. We’re start-
ing to see large central analytics teams gaining a higher profile. It means new titles
emerge - the chief data officer (CDO) or the director of analytics - and they become
involved in supporting the business as a whole,”explains LV’s Kenyon.
McMurtrie recognises that insurers will need some specialists on board to drive
AI-based innovation:“There’s a very clear recognition from the CEO down that we
need to be better with data. There is disruption in the marketplace - microinsur-
ances and how a big company like ours operates there for example - and it’s not
just about being good with data but exceptional. Having a clear data strategy that
enables that and includes machine learning and AI is vital and to that end we have
our first ever CDO starting in a few weeks time.”
“Ownership and control is important for buy in,”warns Fiehn.“In an old fashioned IT
department, analytics can be all consuming but we push it out and other depart-
ments start owning it. We incubate and establish an analytics team that then leaves
the IT world and now sits in the relevant business function. I see my role more now
as a broker of technology and growing new capabilities that the business doesn’t
yet realise it needs.”
A common process is that machine learning has originated in either the analyt-
ics or IT function but is increasingly being fed out across the organisation until it
becomes a part of everyday culture.
“Look at it a bit more organically,”suggests Direct Line Group’s McMurtrie.“If one
department delivers an AI solution without thinking about how it fits into the

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Automation.
overall data landscape, you potentially have a solution looking for a problem. And
while it’s a really good thing to have innovative thinking and have it filtering down
from a senior level, you run the risk of fragmentation. Instead, there’s a cognisance
that there’s something in AI that can be helping the business so we should be
looking at it from a mix of levels across the major business functions.”
Having all departments take ownership of AI creates two distinct challenges. In
the first place, to some extent each department needs to have not just multidisci-
plinary teams, but multidisciplinary people.
“I don’t think the specialists are going to take over but I do feel that the marketing
people who are going to be successful are those who understand how to apply
analytics to their decision-making,”says LV’s Kenyon.
Fiehn adds:“You are looking for different types of people. People with the X Factor.
They need to be very articulate, streetwise but also technical. We’ve been very
lucky in that we could‘breed’a lot of staff from within our ranks. Given the right
support, people will grab the challenge with both hands.”
He adds that the danger of obsessing over roles that have had a degree of hype
over the last 18 months to two years is that technology is moving so fast:“roles in
demand yesterday just aren’t any more today. We embrace the latest technologies
and know what type of people we need, rather than focus on what the industry
thinks we need.”
The second challenge is to maintain focus. The use of AI and machine learning
inevitably speeds up the rate at which companies can find new insights as well as
simply increasing the number of potential new opportunities to follow up. It’s vital
that analytics supports rather than distracts from the insurers’business goals.
“It comes back to what your strategy is. There is a lot of excitement at suddenly
being able to look 10 steps ahead when you’re currently not very good at just
being two steps ahead. The customer actually wants us to be good at the basics,”
McMurtrie warns.“So far it’s about efficiency gains and the capacity to build better
customer service. There is an element of feeling we should be doing something
with AI without a great deal of consideration around how it will help us strategi-
cally and how it links back to the business strategy. The hype around AI can muddy
the waters.
It will be a long journey before the insurance industry can state that it is fully
AI-driven, if indeed having a process driven end to end by machines is even desir-
able. We are in the middle of a process of evolution as insurers try to understand
what role machines should play in delivering a truly exceptional, modern day
insurance service.

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