#214: Reinventing the Digital Workplace at Swiss Re


Welcome to Episode #214 of CxOTalk.
I’m Michael Krigsman.
CxOTalk brings together really the most interesting,
innovative people in the world for in-depth
and genuinely substantive and meaningful conversation.
I want to thank Harmon.ie for sponsoring today’s
show.
Harmon.ie has a set of products that bring
information together from multiple sources
and make it easy to consume on the desktop.
And, I also want to thank Livestream, as always,
for being a great supporter of CxOTalk.
We have a tweet chat going on right now using
the hashtag #cxotalk, and I hope you’ll join
in, and you can ask questions of our guest
during this live show.
And we have the pleasure and the honor today
of speaking with Rainer Baumann, who is the
Chief Information Officer for Information
at a hundred-and-fifty-year-old major, large
Swiss insurance company called Swiss Re.
Rainer, how are you?
Good!
And you?
I am doing great!
Thanks so much for joining us today!
Please, tell us about Swiss Re.
So, Swiss Re is one of the world’s largest
reinsurance companies.
Reinsurance might not be familiar to many
of you.
However, typically you work with your insurance
companies, which give you coverages for your
home, for your cars.
However, if they are working in some countries,
or focusing on specific areas, they typically
need to balance the respective risks.
They reach out to companies like us, where
we help them for extreme events to cover for
that.
And, what are your role and your mandate at
Swiss Re?
My team and I are taking care of all information-related
aspects of Swiss Re, such as collecting, storing,
and managing all information.
And we are focusing on exploiting and leveraging
the latest possibilities around digital analytics
to get most out of it for our business.
You talk about the company as being a knowledge
company, and knowledge is central to the type
of insurance business that you’re in.
So, can you elaborate on that, and tell us
more about what reinsurance is, and how it
connects to data?
Reinsurance itself is not [so] much about
interacting with policyholders ─ that’s
what we call individuals like you, Michael,
who are typically purchasing insurance, or
with SMEs who are also buying insurance.
We are focusing more on supporting the large
and smaller insurance companies on how to
deal with the customers.
So, we give them an understanding about the
different risks: how they calculate it and
how they work with it.
To give you a very simple example: If you
want to purchase life insurance, and if you’re
a very healthy person, that’s a very straightforward
thing.
And most companies will just give you a nice
offer.
However, if you have a few medical constraints,
it might suddenly become very complex.
How do you price this risk?
And “the risk” means the price you actually
need to pay for this insurance.
We help the primary insurers identify that.
But, there is also much more than life insurance.
If you go, for example, to industrial insurances.
When you have very complex facilities, we
do something we call “risk engineering.”
You go out to these facilities, [and] try
to find out: what can go wrong; what the impact
might be.
And this needs tons of experts.
So, if you look into Swiss Re, you’ll only
find a few guys focusing on bringing insurance,
or reinsurance to our customers, but tons
of experts from many, many different areas.
Tons of engineers, physicists, [and] mathematicians
who build the brightest models about earthquakes,
weather, [and] people aging.
All these graphs [word?] make us into a melting
pot of experts, who give us a nice and interesting
cultural environment.
So, your business is not focused on end-consumers.
You are supplying a backstop primarily for
other insurance companies, and much of what
you do relates to gathering data about different
aspects of the world, integrating that data,
and finding ways to manage that data so that
your in-house experts can make the most informed
choices.
I would add formally that we try to understand
the world.
Sometimes, there are great models that you
can apply to understand the world.
Sometimes, you need tons of data.
And sometimes, it is a lot of long-term expertise,
learning, and the latest research around how,
for example, medical costs are creating one
of the biggest unknown problems: Will they
increase by 5 or 10% annually?
Or, will they go down?
And if you knew the extrapolation of the treatment
costs over the next decade, it would be a
very relevant factor for understanding how
to price these actual health costs.
So, you are the CIO for Information, which
I’m assuming means that this information
challenge is an important part of your mandate.
And so, how do you think about bringing this
information in, and [about] the nature of
the workplace and how your workplace is evolving
to make it easier for this group of data experts
to carry out their job?
First of all, luckily, my folks don’t need
to be the biggest experts in these different
insurance disciplines and the underlying knowledge
areas.
When we talk about bringing in data, we are
not only the experts in how to work with that
data and on methodologies but also leveraging
the latest technologies.
When it gets to mathematical models, for example,
or how you calculate weather conditions, then
we have our engineer-physicists who know this
much better than we do.
However, when you have …
Let me give you a little example.
When you get a claim after something happens,
then classically, you will have somebody typing
this from an email into a system.
From that system, someone would review it,
and put some more information into another
system.
However, everything you get, you could extract
more or less automatically, and digitize significant
part of the processes.
But, you need to leverage quite a lot the
capabilities that are slowly emerging in the
field.
And it is the same as [for] understanding
different risks.
There is so much information there and we
only use a minuscule portion.
But, the majority of that information is particularly
unstructured ─ is unrelated, meaning it
comes from very different sources.
You need to put it into context; understand
it.
And no, we do not employ hundreds and thousands
of people.
You need to do this in an intelligent way.
And, and that’s where our focus is.
… Which, of course, then raises the question
of how do you do this?
[Laughter]
[Laughter] And that’s the magic question.
[Laughter] And, two days later, we probably
wouldn’t have found that silver bullet yet.
I would say we apply all the best practices
around.
When you, for example, acquire external information,
you first need to make sure that you understand
where it’s coming from and the context, and
enrich [?] history.
With respect to master data, we put it into
a context where we have structured information,
try to have the right models, and that where
you have unstructured information, get them
in a way you can work with them.
But, all of what I just described is very
much technical.
The challenge ─ yes, it is technical.
But, the bigger challenge is how do you create
actual business value out of it, and how do
you enable our how we call it business ─ the
experts in the risk functions, underwriting,
claims functions ─ to take true business
advantages out of this information.
So, you get in this mass of unstructured data,
and maybe you can tell us a little bit more
about what that kind of data is.
But even more importantly, then, how do you
make use of it in order to add the business
value, and make it useful and in a practical
way?
I mean, this seems to me like a … For your
company, it seems like it must be a strategic
competence to be able to do that very well.
I would even go a step further.
It is the strategic competence to understand
information and leverage it.
Or, to do a little historical exploration:
How did insurance, over the last hundreds
of years, define the risks and estimate the
right prices?
You looked at what is called “actuarial models,”
which is nothing else than the development
of the respective claims, a bit simplified.
So, you looked, and you had a particular risk:
How many losses did you have with that risk.
And how did this develop?
And with this, you naturally assume that the
risk develops somehow similarly.
Or you add a few parameters and say, “Oh,
there are now more interactions so it will
have a slightly higher claims rate or a higher
cost per claim.”
But, these developments are naturally extrapolations;
they do [not] respect the individual risk.
So, we say, “You move from, let’s say a statistical
risk, to a more individual risk.
And you move from a historical perspective
on the risk to [a] more forward looking – what
most people would say predictive, or a predictive
model.”
There are also sometimes limitations to how
predictive you can be, depending on the information
you have at hand.
You talk about smart analytics, and what is
that?
I mean, I’m so interested in this issue
of how you manage the flow of data, and how
you organize yourself around that as well.
Smart analytics, advanced analytics, digital
analytics ─ whatever magic word you want
to use, [they] are just interchangeable words
to me – is leveraging intelligent algorithms
paired with massive computational power.
By the way, most of these algorithms have
been here for ages.
So, if you studied computer science in the
nineties, you have heard most of it.
But, most of what the people said is not usable
in reality.
So, with sheer computational power, we can
now do many interesting things; and this starts
by understanding information.
A very simple example is when you have a sentence
from a person.
Is that person angry or not, which is a sentiment
analysis.
You now could try to analyze the grammar,
and if one word comes after another.
Or you add a hundred thousand or millions
of examples, and compare them, and say, “Typically,
this is angry.
Typically, this is happy.”
And you can apply different kinds of these
algorithms; and you find out, “Oh, this is
a happy customer or an angry customer.”
And, this sort of sentiment analysis is just
a tiny glimpse.
Now, think about when you have a text, and
in your text, you have written something about
… Let’s go into a property ─ a facility
of a big manufacturer.
When they write in the assessment, “We had
a local fire brigade for over ten years, but
didn’t have a good experience.
This is why we fully trust in the fire brigade
from our town.”
So, we immediately know that they don’t
have a fire brigade, and we hope the town
is very close.
Otherwise, the facility will burn down when
they have a large fire.
For a computer to understand such a sentence,
and really get, “Oh!
So they do not have one.”
But they said, ‘We had one over ten years
[ago].’
There is not even a negation in the whole
sentence.
All this is tough to understand.
And besides this natural language processing
part, there are also tons of other things.
For example, when you want to understand forms,
you scan a form.
It is tough to comprehend.
Where have people done across; where have
people done, or how have they filled certain
parts of it.
Now, funnily enough, we can apply algorithms
from genetic engineering that were developed
from DNA analysis to detect patterns in forms.
These are graphical algorithms.
So, you do not apply them to text; you apply
them to graphics.
Based on these patterns, you can pre-process
the information in a way so that afterward,
you can go much further.
But, it also goes towards classical quantitative
methods enriched with newer capabilities.
Artificial intelligence can also be applied
to well-structured data, to find hidden insights
which you wouldn’t have found yourself.
[…] There’s also a human dimension to
this, which is: You come from a very well-established
industry, the insurance industry, in a company
that’s 150 or more years old, and so, how
do you ensure adoption?
How do you get your workforce to change the
way it works, in order to absorb these new
ways of thinking about data, analyzing data,
incorporating data, or making decisions with
data?
And this is a huge challenge you’re bringing
up here because it’s not just about analyzing
data.
If you think [about it], We typically have experts
whom we have had with us for years.
So, at Swiss Re, you can find a lot of very
experienced intel people.
They are confronted with an industry that
is suddenly changing because it’s getting
more products in there ─ for example, new
cyber defense products. Then, they get a
work environment that changes.
Ten years ago, they never used any sort of
iPads to go to customers, write in notes,
chat and interact in high frequency with their
customer [when] developing a contract.
Then, you suddenly have new models of transparency,
and understanding of how risks are developing
and where it’s going.
And then you have all these new possibilities
with data.
So, all of these things suddenly come together.
And, in this environment, to be fully transparent,
we don’t have a gold nugget.
And we heavily struggled with [on-boarding]
all of our workforces onto the journey by
adapting different capabilities offered.
And, the solution and path we are taking at
the moment, is to provide different possibilities
and opportunities to them and let them choose,
to some extent.
[We] tried sometimes to impose a certain way
of working.
We failed pretty miserably.
If you think that you understand how the folks
in the company should work, you are either
working in a factory, or you might have to
rethink your position.
And that’s what we now do constantly: Try
to provide opportunities; listen to them;
bring the whole “consumerization” into the
game, meaning, like, enabling them with their
own phone tablets at work.
We also have a big mantra, which is, “Own
the way you work.”
So, people can work at home, while traveling,
and with that, have a much more attractive
work environment for them.
At least, we have had some pretty good feedback
on how we are exposing these capabilities
to our employees now.
So in this case, when you talk about the digital
workplace or the future of work, it mainly
comes down to convincing these experts to
adopt new ways of working that enable them
to take advantage of the data – the unstructured
data – especially that which you’re able to
supply through various types of data feeds
and analytic tools.
Yes, that’s one part of it…
You can also see this classical adoption problem
with the latest technologies when you… and
let me perhaps switch to another industry
─ if you go to lawyers.
Lawyers, especially in their early years,
spend tons of time finding the right information
somewhere, preparing it, and distilling insights
out of court case decisions.
There is a small portion that is truly high
in value-adding: sitting with the customers,
listening to them, understanding where they
are, defining a strategy on how to tackle
the legal challenge.
And, that part of thinking, this value-adding,
probably remains.
But, the other part that I described initially.
You can substitute more and more [with] technology,
leaving you on one side with a problem that
the other people don’t have the possibility
to learn and get into it.
And for the well-established lawyers, let’s
think like this: You for sure have a lot of
investment into preparing this show.
If you needed to run ten such shows every
day and having five minutes to prepare it,
I’m very sure your life would be pretty
stressful.
Yes.
So, this type of change that you’re talking
about must feed into the ultimate thinking
of the business model, right?
So, maybe speak to us about your thinking
[on] the transformation of your business model,
and then this use of technology, and the adoption
of the different ways of thinking, and the
various ways of working; how does that feed
into your business model and your broader,
longer-term strategy?
Now, these are very broad questions.
Perhaps, I may need a few attempts to answer
it nicely.
First, I always tend to say, “The reinsurance
industry is probably the last one to get hit
by trends.”
First, you see it somewhere in commercial
retail, then very, very late in retail banking,
then investment banking, then retail insurance.
The last is reinsurance.
Why is it like this?
The answer is because we are a business with
very infrequent interaction.
Typically, the interactions are semi-pleasant.
At least, I don’t remember people saying,
“Oh, cool!
I crashed my car!
I can talk to my insurance!”
And also not, “Cool!
I need car insurance!” and “Cool!
I may pay a thousand bucks a year for that
insurance!
I’m so glad to do that!”
[Laughter] I once said to my accountant, “Doesn’t
he ever call with good news?”
[Laughter]
It’s the same kind of thing.
But, this is … We are in a, let’s say,
semi-positive, low-frequency-interaction business.
And, having said that, if you are then even
second in line, or supporting the companies
in such a challenging environment, it’s
even more extreme.
So, our business model probably evolves much
slower than most people would expect when
they look at the other end.
However, it becomes much more transformed
through what is around us.
And, “what is around us” means the understanding
of the world and how the word is changing.
If you look, for example, at the kind of risks
we have today, they are so different than
the risks we had twenty years ago.
If you think, for example, about cyber risks.
There is sophisticated risk that the global
economy goes down for several days because
of a cryptographic breach somewhere in cryptographic
functions.
No one knew about this weakness, and it could
have been exploited.
And then our economies are down.
If we go [back] into the early nineties, what
could have happened?
You could have had a local power outage.
You could have had some instruments that were
not produced well, so that [in] several areas,
or several places, you have issues.
But, this connectivity of the world changes
the whole thing.
So we suddenly need to understand that the
risk is much broader, much more interconnected.
And, this also asks us to think, “Can we offer
insurance, for example, that is more tailored,
more short-term, so that you don’t get very
long coverage?
Could we also partner more with our primary
insurers, and the bigger customers, and also
individual customers so that they prevent
things more?”
By the way, this is also a great chance for
us.
The digitization shortens the distance from
a reinsurance to the ultimate customer significantly.
Suddenly, we have a chance to get in touch
with them; get real handy signals from them;
and also shape their behavior ─ influence
them.
Because risk management [and] risk reductions
is a lot about influencing and assuring that
things do not happen that we don’t want to
happen.
And, as an insurer, we are most happy if we
can make sure that nothing goes wrong and
nobody has a bad experience.
So, when you say that digitization shortens
the path or the link to the end-customer,
maybe elaborate on that; because again, that
is another aspect of your business, which
is entirely different from the past, where
you didn’t touch those end-customers individually.
Now, let’s start, perhaps, from two angles.
The first is how we can influence claims,
and how we can leverage more of the potential
to get close to the customer.
On the claims side, it’s a lot about prevention.
In the past, we were highly dependent upon
insurance companies, or others to help with
prevention.
Today, we have some insights, whether from
the data we can get from our primary insurers
or other areas, that something’s not going
the way it should be.
So, let’s think again about industrial situations.
So, you have a company employing 10,000 workers
on chemical plants, and we find out that they
are running their chemical processes at very
much at the edge, which is pretty critical
not only towards risking the facility, but
also the employees there.
When we see that, we can suddenly reach out
to them.
In the past, we just had the statistics: “new;”
“old.”
Typically, if you produce these types of chemicals,
you’d have on average an explosion every thousand
years.
And if the facility is that big with that
blow-up rate, this is the premium.
Today, we see, “Oh!
The operator on that site is running the plant
pretty riskily.
He needs to cool it down more,” or whatever.
And, what I’m talking to you about now is
also very much the future.
Today, we don’t have that much insight ─ live
IoT data, but we’re getting closer.
And, I promised you before that I’d also
look at it from a second angle: How we can
address the individuals like you and me?
Interestingly enough, only about a third of
the insurable risks in the world, which would
be natural to insure, are insured.
So, I am not talking about the coverage for
mobile phone glass breakage.
I’m actually not sure how much sense it makes
to insure your mobile phones from breaking.
At least, the insurance industry is thankful
for the revenue you’re creating, but for you,
this is probably not worth insuring.
However, there are many things: if you get
disabled; if you die; or get seriously ill;
or if your house burns down; if there’s an
accident; or if you get into a legal dispute.
Here, we see a high level of underinsurance.
As such, we try to make the people aware of
this underinsurance because this is exposing
them significantly to risk.
You, living in the US, know well how the US
health care system today, or even in the future,
with the ongoing changes, will take care of
individuals, and how you need to take care
of your pensions.
Many states have different regulations.
But, this is your business, and if you don’t
take care of it, then you might have significant
challenges in the future.
I want to remind everybody that you’re watching
CxOTalk, and we’re speaking right now with
Rainer Baumann, who is the CIO for Information
at the large, Swiss insurance company called
Swiss Re.
And, I want to especially thank Harmon.ie
for sponsoring this episode.
You can send questions to Rainer using the
hashtag #cxotalk on Twitter right now.
Rainer, so we haven’t spoken directly about
the topic of innovation.
And, for a company that is 150 years old,
and that is in an industry that’s changing
so rapidly right now, I have to assume that
innovation is very top of your mind.
Can you talk about innovation, which I assume
also is the kind of glue that holds these
various pieces together that you’ve been
describing?
Innovation, in fact, is one of our core paths
in the company.
Innovation, for us, doesn’t come from tough,
stringent, managed research projects.
Innovation comes from the entire company.
We aim at creating a lot of freedom, and we
invite people to think about different opportunities.
So, collaboration, open problem-sharing discussions
are at the core.
You could even say that certain kinds of edgier
working environments, which some tech companies
have are not new to us since we try to foster
those kinds of exchanges.
And, when we then look into the individual
activities and how they worked, and very often
during a lunch talk, or during a discussion
with a primary insurer, we come to the idea
of, “Oh, we could, or should; and try out
something.”
And, with our expertise, we are typically
able to grow these things.
Naturally, we also have the typical top-down
challenges that you, for example, see, “Oh.
The population is aging more and more.
How do you understand this?”
And, because the aging of the population is
a huge financial risk to life insurance, [and]
pension funds, you need to be somehow able
to calculate it.
And then, you suddenly find yourself in more
tailored discussions.
And here, I think we can be fully transparent.
Whenever you have a very tailored focus, innovation
becomes much more difficult than when you
let it come through the organization.
And similar to what I referred to before,
tech companies.
Most of our biggest innovation came from smart
people in the ranks figuring out something
with their passion.
So again, that’s a cultural dimension.
How are you thinking about the culture, and
changing the culture of this 150-year-old
company to support this model of collaborative
and innovative thinking?
So, on one side, we are lucky that we already
have such a collaborative environment.
On the other hand, we also have these generation
changes, where some are coming in and wanting
us to work in new ways.
We have tons of digital tools supporting them.
However, they don’t fully meet the needs of
the more experienced generations.
So, we try to bring them together with most
of the best practices we find out there, whether
it is community events ─ physical [events],
not like a CxOTalk, but in the Beta science
community, for example, around smart analytics.
Every second week, we have a talk about a
certain topic.
In four weeks, we’re about to have a huge
conference in our main conference facility,
where we have several hundred people getting
together and thinking about different problems.
We sometimes have competitions, or we conduct
hackathons.
And, mixing these different elements allows
us to embark a bit onto this.
However, we also face a huge challenge here.
Since we have this more brain-heavy work,
there is a part of Swiss Re that is highly
transactional.
If you look into our service centers, there
are hundreds of people doing operational processing.
And we try as much as possible to pick their
brains, not just leaving them to dummy information
work.
We try to do this by engaging them more locally
into discussions and opening up the topics.
In just two weeks, I’m going to one of our
service centers, and have large, group discussions
[that] last the whole afternoon where everybody
might come and participate, and discuss how
we can evolve our processes, products, whatever.
So you’re basically trying to engage people
at every level of the organization, essentially.
Absolutely.
If you look to our Group CEO, Christian Mumenthaler,
who is one of the youngest group CEOs in the
industry … I hope he’s okay with me saying
that.
He’s really interested in tech and he always
wants to have the latest features.
[He] naturally breaks down these barriers,
and enables us to talk very directly on all
levels.
And, especially since we [have the attitude]
that everyone in this company can make a difference,
we are so open to doing that.
We have only a short time left, and there
are a few things that I still want to talk
with you about.
But, very briefly, can you share your thoughts
on some of the next generation of technologies
that relate to insurance, such as mobility,
the blockchain, things like that?
For sure, Michael.
First, most technologies will transform the
way we look at the world.
Or, in other words, the change of the world
itself is the transforming factor for us.
With IoT, we will get more and more insights
about how things are working.
With this, we understand risks much better
and can influence them much more closely.
Mobility, or what I would call “connectiveness,”
allows us to stay in touch with whatever is
out there.
Here, we talk very often also about connectiveness
of people, because IoT is more focusing on
devices, on industrial facilities.
Sometimes also people, but people, they have
much more than their fitness tracker and their
intelligent clothes.
They’re very social; they interact; they leave
tons of tracks everywhere.
By the way, just to give you one very nice
example: There was an insurance company in
the UK that shifted their model for estimating
your driver risk entirely just by analyzing
your social media footprint.
And if you were well-behaving, they assume
you’re also a good driver.
If you have been a “special” character in
social media, they assume this special character
will also be replicated on the street, and
priced accordingly.
This level of involvement is not especially
appreciated by the regulators, but those are
the kind of technology changes [I’m talking
about].
We also see a lot of financial technologies
from the fintech area, which influences the
asset management paying part, like blockchain.
And, what other technology have you been thinking
about, that would be interesting for you?
Well, maybe digital payments.
That seems like it might be relevant.
Digital payments are especially interesting,
from my perspective, in two areas.
One is that when you get more into insurance-as-a-service
on-the-go ─ so pay-as-you-drive, pay-as-you-live─
then you need to have very small payments
to capture these respective choices.
So, [through] electronic ways, new digital
payments enable that.
The other side, micro-payments ─ small payment
possibilities ─ are also the key to opening
up insurance solutions to areas of the world
which have very limited access to insurance.
So, if you think about Africa, how can you
provide microinsurance to farmers?
We’d offer respective payment solutions,
and besides this opportunities, this is always,
“Sorry, we are an insurer.
We are always thinking about the threat I
mentioned.”
It’s a huge threat, because if something goes
wrong with digital payments, you have a scalability
factor.
It’s much harder to copy notes from a national
bank.
Whereas doing something nasty with digital
currency, we can all go and be very creative
about that.
Yeah.
So, I can see the concern.
We have just about five minutes left, and
I know that you’re working with Harmon.ie,
and Harmon.ie sponsored this episode.
CXOTalk doesn’t have commercials, and so I’m
always very grateful to the people who support
it.
So, tell us about what you’re doing with
Harmon.ie?
So this is very funny, by the way, because
today, we had Harmon.ie exactly in this room,
and I didn’t know Harmon.ie was sponsoring
that session just an hour ago.
We have as part of our digital workplace program,
where we embark on adopting the latest technologies,
a strong journey on the Microsoft road with
respect to Exchange, SharePoint, and other
platforms.
And enabling simpler access and adoption to
these technologies is highly supported by
Harmon.ie.
So, how you can share documents in a simple
way, how you can interact with people.
Even on my mobile phone, I can get access
to all my documents wherever I am in the world
thanks to our Harmon.ie app.
So, the …
So, it even sounded like a commercial now.
[Laughter] Well!
So, the key is the simplicity of managing
the information, which sounds like it’s an
essential part of your broader strategy for
the business.
It is.
In fact, if you think about it, our business
is so diverse.
So, we have experts that focus on marine cargo
hull ─ meaning on the hull of a ship [carrying]
cargo.
You have a few folks focusing on this with
respect to documents, pricing, and a lot of
knowledge.
And, we have hundreds of thousands of such
specialized areas.
They need to manage their knowledge somehow
and evolve it.
Even though we rely highly on the individual
experts, naturally, a company like us also
wants to encode some of this knowledge to
sustain this [expertise] beyond just the individuals
upon which we rely heavily.
Fantastic!
Well, this has been an absorbing discussion
about an industry that is in the midst of
dramatic change.
We have been talking with Rainer Baumann,
who is the CIO for Information at the large,
Swiss insurer called Swiss Re.
Rainer, thank you for spending time with us
today!
It’s been a lot of fun!
And everybody, I hope you will come back on
Friday, because we have another installment
of CxOTalk, and we’ll see you then.
And, thanks again to Harmon.ie for sponsoring
this episode.
Bye-bye!

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