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How Tech Transforms Finance and Banking
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TechnologieTranscription
00:02Merci, Ariane, pour cette introduction.
00:05Hello et bienvenue.
00:08Nous allons parler de la technologie de la finance.
00:13Et c'est straight à la pointe.
00:16Laurent David, je voudrais savoir
00:21de la technologie de la technologie de l'entreprise
00:24qui changeait le secteur de l'entreprise.
00:29Merci pour la question.
00:30Je ne vais pas parler de la technologie de la technologie de l'entreprise.
00:38En fait, quand vous regardez ce que nous faisons,
00:42la technologie de l'entreprise n'est pas vraiment dans le « quoi ».
00:45C'est pas vraiment dans le produit que nous offrons.
00:47Un crédit a été un crédit pour centraux.
00:51C'est plus dans le « how ».
00:53So, how do we interact with our customers,
00:57how do we build our offers,
01:00how do we manage our risks,
01:02and how we do improve and increase our computing capabilities
01:08in a day-to-day business
01:11where we handle so many transactions,
01:13so many contacts,
01:15and we own so many datas.
01:19This being said,
01:21there are waves of innovations,
01:24waves of new technologies.
01:26Not all of them deliver their promises
01:28for the banking sector,
01:29but if you ask me
01:32what is the real one at the moment,
01:34I would definitely say
01:36that everything that is going around
01:38data,
01:40traditional AI,
01:41and generative AI
01:43is the game changer
01:45where we do invest
01:46and where we do expect
01:48a lot of changes
01:49for the years to come.
01:52Okay.
01:53And how is BNP Paribas
01:55keeping up with these changes
01:57to stay ahead in the industry?
01:59So, and how in practical terms,
02:02how and where do you use innovation,
02:04technical innovations?
02:05And third question,
02:07how to make sure
02:08that these new technologies
02:10are environmentally friendly,
02:12social responsible,
02:14and safe?
02:19To start with,
02:20I mean,
02:21what is technology for a bank?
02:23It's really,
02:23it's really important.
02:25There are not many sectors
02:26except the tech sector
02:28by itself,
02:30where,
02:31I mean,
02:32if you just want an idea,
02:34every time we spend 4 euros,
02:37one is for tech.
02:39Every time we earn 6 euros,
02:42one is used.
02:45to finance our tech needs.
02:47Okay?
02:48So,
02:48and what is tech?
02:50It's people,
02:50hardware,
02:51software,
02:53services.
02:54So,
02:54one euro spent out of four.
02:56So,
02:57when I am asked
02:58if we are a tech company,
02:59in that respect,
03:02I'm very close
03:03to what we usually consider
03:05as a tech company.
03:08This being said,
03:11we are not,
03:13generally speaking,
03:15an innovator
03:16in the sense
03:17that we invent
03:18new technologies.
03:22It's perhaps
03:22easier,
03:23better,
03:23yeah,
03:24go on.
03:24we intend to be a smart user,
03:29smart user of new technologies.
03:32And this can bring us close
03:34to where we are expected.
03:36For instance,
03:37when we partner with Microsoft,
03:38I mean,
03:40it's really,
03:40it seems like obvious.
03:43Sometimes,
03:44it brings it a bit longer,
03:46a bit further
03:47from our expected grounds
03:49when we partner
03:50with Kairos
03:52and the exceptional capabilities
03:55that we share with them.
03:58and when we say smart user,
04:01I want to insist
04:03on what I mean
04:04by smart.
04:07Smart for us
04:09means that
04:09we need
04:11to protect
04:13the data
04:14that our customers
04:17give to us.
04:19we have
04:20an awful lot of data
04:21about our customers,
04:22but we consider
04:23that
04:24more than the data
04:25itself,
04:26the trust
04:27that we share
04:29with customers
04:30has more value.
04:32So,
04:33we do,
04:33part of our investment
04:35is dedicated
04:35to securing
04:36everything
04:37that we do,
04:39not only against
04:40the cyber threats,
04:41but also
04:41in terms of
04:43data privacy,
04:44data protection.
04:46and we do invest
04:48a lot as well
04:48in transparency
04:49so that
04:51every use
04:51that we do
04:52is really
04:53well balanced
04:54in terms of
04:55interest for the customers
04:56and interest
04:57for the bank.
04:59So,
04:59that's the
04:59main item
05:01that I would
05:03put behind
05:04the smart
05:05of smart user.
05:06Thank you very much
05:07for your valuable insights,
05:08Laurent.
05:09Now,
05:09let's move on to you.
05:10Antoine Rostand
05:11from Kairos,
05:12a company
05:14which use
05:14AI
05:15and remote sensing
05:16to combat
05:17methane emissions.
05:19First,
05:20could you start
05:21by giving us
05:22an overview
05:22of Kairos?
05:23What is your
05:24core business
05:24and how do you
05:25integrate
05:26environmental AI
05:28to make an impact
05:29in the finance sector?
05:32At Kairos,
05:34we use AI
05:35and satellite imagery,
05:36a lot of satellite imagery.
05:38We process millions
05:38of imagery every day
05:40to provide
05:42an unprecedented
05:44transparency
05:46on economic activity
05:48and environmental footprint
05:50of companies
05:51on a global basis.
05:53I believe
05:54it's a
05:55Copernican revolution
05:56because
05:58we live in a world
06:00where we know
06:00very well
06:01where we stand
06:01but not really
06:02what's outside
06:03and here,
06:04with this technology,
06:05anyone who has access
06:06to Kairos
06:07can see anything
06:08anywhere in real time.
06:10It's a complete change
06:12of perspective
06:12and this is going
06:13to fundamentally
06:14change a lot
06:15of decision-making
06:16processes,
06:16in particular in finance.
06:19Today,
06:20we can measure
06:21with an existing fleet
06:22of satellites
06:23around 50 parameters,
06:24physical parameters
06:26which are around
06:26greenhouse gas emissions
06:28so we can measure
06:28CO2 and methane.
06:30It was impossible
06:31to know where methane
06:32was coming from
06:32before this technology
06:33was developed
06:34so it's a major change
06:35to be able
06:36for climate action.
06:39We can look
06:40at the losses
06:41of biomass
06:41and biodiversities.
06:43We can lose
06:44at physical risk
06:45flooding,
06:47wildfires
06:47and how it can have
06:48an impact
06:49on the economic activity.
06:51And finally,
06:51we have a lot
06:52of economic indicators
06:53that we can gather
06:54at plant level.
06:56So that's
06:57a formidable technology
06:59that was used
06:59at the beginning
07:00by hedge funds.
07:01They made a lot
07:02of money out of it
07:03because they had
07:03an informational advantage.
07:05Then we move
07:06to government.
07:07We have a landmark
07:08contract
07:09with the United Nations
07:10with UNEP.
07:11Basically,
07:12we scan the full world
07:13every day
07:13and when we detect
07:14a methane leak,
07:15we inform
07:16the United Nations,
07:18they inform
07:18the country
07:19and then they send
07:20a fine to the polluters.
07:22So it's the first time
07:23there is a feedback loop
07:24on methane emission.
07:26It's very powerful
07:27for climate change.
07:31So hedge funds,
07:32government,
07:33insurer after that
07:34and now we are moving
07:35to banks
07:36and asset managers
07:38thanks to Gen AI
07:39because before
07:40the technology
07:40was not ready
07:41but now thanks to Gen AI
07:43we can not only
07:44do this 50 measurement
07:45on assets,
07:46we track 5 million assets
07:48every day
07:48but we can link
07:49this asset
07:49to a company.
07:50So we can have
07:51a tickerized view,
07:52a company view
07:53of environmental performance
07:55and economic performance
07:56which is really
07:57what banks
07:58and asset managers
08:00are looking for.
08:00and Laurent just mentioned
08:02a significant R&D partnership
08:05between you
08:06and BNP Paribas
08:07so how critical
08:09is this collaboration
08:10in tackling
08:11your technological challenges
08:13what positive effects
08:16have you seen
08:17and expect you to see
08:19in the coming years?
08:21So we are experts
08:23in geospatial
08:23but we don't necessarily
08:24know the domain
08:26so we always
08:26partner with hedge fund
08:28to define
08:28a product for hedge fund
08:30and with AXA
08:31for insurance
08:32and in bank and insurance
08:33we've chosen BNP
08:34because they are the best
08:36one in data
08:37and environment
08:38clearly you have
08:39a leadership there
08:40which fits completely
08:42our own positioning.
08:45We've met
08:46around 200 people
08:47from all over the bank
08:50also in risk
08:51in CIB
08:52in credit
08:52in data
08:53in ESG
08:54so it's not
08:54purely ESG
08:55so across
08:56also revenue generating
08:58teams
08:59and we have
09:00around 20 projects
09:01so we work
09:02project by project
09:04and if it's successful
09:05then it becomes
09:06a data stream
09:07and we are discussing
09:07things like
09:08Bessane
09:10biodiversity losses
09:11for a go-no-go
09:12decision
09:13whether the bank
09:13will lend
09:14to the owner
09:15to the operator
09:17we can look at
09:18climate risk
09:18for credit risk
09:20stress testing
09:21we can look at
09:22infrastructure
09:23for project financing
09:24and milestone management
09:26so there is a bunch
09:27of projects
09:28that we are
09:30working with
09:31the team
09:32and I thank
09:34the BNP Paribas
09:36team for the
09:36great collaboration
09:37and we think
09:39with that
09:40we'll be able
09:40to create
09:41data streams
09:41that are extremely
09:42valuable
09:43for the world
09:44because this
09:45would have an impact
09:45and for BNP Paribas
09:48and for us as well.
09:49Okay, yeah.
09:50Just I would like
09:51to have your comments
09:51on this partnership
09:52Laurent
09:53so what benefits
09:54does this partnership
09:55provide to you?
09:56How does it help you
09:57to accelerate
09:58to speed up
09:59the adoption
09:59of new technologies
10:00in the bank
10:01and improve
10:02perhaps your capability
10:03in address
10:04environmental impacts?
10:09Please believe me
10:10when I say that
10:10when we tackle
10:12environmental
10:13and climate change
10:14issues in BNP Paribas
10:16we do it for good
10:19which means that
10:20we invest in that
10:21because we definitely
10:22believe that
10:24I mean as Jean-Laurent
10:26Bonafé said
10:26at least once
10:28that there is no winning
10:29company in a losing world
10:31so either in the medium term
10:33we have to choose
10:34the right companies
10:36the right technologies
10:37and in the long term
10:38we all need
10:38I mean to be able
10:41to tackle
10:42and dominate
10:43this climate change
10:44issue
10:45so when I say
10:45we do it for good
10:46that means that
10:47we tend to open
10:49our eyes
10:50and our ears
10:51to what is possible
10:52to do
10:52we take commitments
10:54in terms of
10:56redirecting
10:57our financings
10:58or
10:58I mean
11:00our credit
11:00to
11:01new sectors
11:03to new energies
11:05etc
11:05but when we do that
11:07what we
11:08very quickly understand
11:11is that
11:11there is a critical issue
11:12that is data
11:14I mean
11:15when
11:16I mean
11:16CO2
11:17is very easy
11:191 kilo
11:1910 kilos
11:201 ton
11:21etc
11:21but at the end of the day
11:22I mean
11:23what is the real emission
11:24of the different sectors
11:26what is the difference
11:27between what they do
11:28and what they say
11:29what is the difference
11:30I mean
11:30data is critical
11:32and with Keroz
11:34we
11:35found
11:36ways
11:36and that's part of the things
11:38we're definitely working on
11:39to give
11:41real
11:43really assessed
11:44data
11:45that help us
11:46make decisions
11:47you were
11:48talking about credit
11:49about risk
11:51management
11:51about asset management
11:53and that's really
11:54key to
11:55us
11:56to be able
11:57to be sure
11:58and convince
11:59ourselves
11:59that we
12:02rely on good data
12:04to make
12:04good investment
12:06and credit decisions
12:07so a lot of benefits
12:09from this partnership
12:10so perhaps
12:11you plan
12:12many other partnerships
12:13and it's kind of
12:15a very
12:16I mean
12:16as I said
12:17I mean
12:17it was not
12:18easy to guess
12:20at the beginning
12:20that we would partner
12:21with a
12:23space
12:23satellite
12:24imaging
12:25company
12:26and certainly
12:28and certainly
12:28there will be
12:29some other
12:29partnerships
12:30in the years
12:30to come
12:31that I mean
12:32might seem
12:33a little bit
12:34strange
12:35at the first look
12:36but are very
12:37easy to understand
12:38when we explain it
12:40thank you
12:41thank you
12:41and now
12:42over to you
12:43Marie-Benoît
12:43Baroin
12:45head of technology
12:46strategy
12:46at Microsoft
12:48so
12:48we all heard
12:50about the
12:50huge announcements
12:52of Microsoft
12:53of significant
12:54investments
12:56notably in
12:57in France
12:57so
12:58how do you
12:59see
12:59these
13:00influencing
13:01new innovations
13:02in the banking
13:03sector
13:03yes
13:04indeed
13:04we've seen
13:06and we've
13:07announced
13:07a few days ago
13:09that we will
13:10invest
13:104 billion
13:12in France
13:12to build
13:14state-of-the-art
13:15AI
13:16and cloud
13:17infrastructure
13:18in
13:19near
13:20Moulouse
13:21in France
13:21and this is
13:22a real
13:23good
13:23news
13:24in fact
13:25for banks
13:25who
13:26already
13:27mostly
13:31decided
13:31to use
13:32our technologies
13:33to transition
13:35to the cloud
13:36and most of
13:37them are using
13:38today our
13:38French data
13:39centers
13:40so this is
13:41really
13:41an insurance
13:42for them
13:43that they will
13:44be able to
13:45leverage
13:46state-of-the-art
13:47technologies
13:48and
13:48notably
13:50Gen.AI
13:51to create
13:52new applications
13:53new services
13:56and leverage
13:57their existing
13:58work that
14:00they have done
14:01already
14:01to protect
14:02data
14:03or big
14:04volumes of
14:04data
14:05to also
14:07set up
14:09a governance
14:10and also
14:12set up a
14:13framework
14:14for security
14:15to protect
14:16all their
14:17information
14:18systems
14:18so today
14:20banks
14:21know
14:22that we have
14:24a responsible
14:25approach
14:25on AI
14:27we have set up
14:28what we call
14:30a responsible
14:31AI framework
14:32which is
14:33really a
14:34human-centered
14:35approach
14:36that
14:37make sure
14:38that when
14:39we develop
14:39new technologies
14:40we don't
14:42develop
14:44things that
14:45can harm
14:46people
14:46companies
14:47or society
14:48for instance
14:51we integrated
14:53Gen.AI
14:54into our
14:54own solutions
14:55like M365
14:57or
14:57teams
14:58that a lot
14:59of people
15:00are using
15:00and when
15:03you use
15:03these technologies
15:04with Gen.AI
15:06a user
15:07that is
15:07presented
15:08with
15:08content
15:10that has
15:11been generated
15:11by AI
15:12explicitly
15:14knows
15:14that it
15:15is such
15:15kind of
15:16content
15:17he also
15:18can
15:19have
15:21access
15:22to the
15:23source
15:23of the
15:24data
15:24that has
15:26been used
15:26to produce
15:28this content
15:29on top
15:30of that
15:31we
15:32committed
15:33to not
15:34use
15:34the data
15:35of our
15:35customers
15:35to train
15:36our own
15:38models
15:38this is
15:39quite important
15:40for a
15:40bank
15:42and
15:43on top
15:43of that
15:44on security
15:45we also
15:46committed
15:46to
15:49adapt
15:50and
15:52set up
15:53the
15:54NIST
15:55risk
15:56assessment
15:56framework
15:57which
15:59is
15:59very
16:00important
16:00too
16:00so
16:01this
16:01was
16:02the
16:02first
16:02announcement
16:03and
16:04the
16:04second
16:05one
16:05is
16:05really
16:05about
16:06skilling
16:06because
16:07banks
16:07like
16:07many
16:08other
16:08companies
16:09are
16:10lacking
16:11AI
16:12skills
16:12today
16:12they
16:14try
16:14to
16:15attract
16:16and
16:16retain
16:16those
16:17skills
16:17but
16:17this
16:18is
16:18quite
16:18difficult
16:18because
16:19we
16:20are
16:20not
16:20training
16:20enough
16:21people
16:22on
16:22these
16:23topics
16:23so
16:24Microsoft
16:25committed
16:26to
16:26train
16:27with
16:27France
16:28Emploi
16:29and
16:29Saint-Plon
16:301 million
16:31French
16:31people
16:32in
16:33AI
16:33and
16:35we
16:35have
16:35already
16:36started
16:37a few
16:38experimentations
16:41to
16:41skill
16:41people
16:42with
16:43also
16:44with
16:44banks
16:45and
16:45we
16:45have
16:46built
16:47some
16:47AI
16:48schools
16:48in
16:49France
16:50already
16:50so
16:51we
16:51will
16:51amplify
16:52this
16:52movement
16:55and
16:55the
16:56last
16:56one
16:56is
16:58about
17:00investing
17:01in
17:01a
17:02full
17:02startup
17:03ecosystem
17:04in
17:04France
17:04that
17:05will
17:05also
17:06benefit
17:08for
17:09banks
17:10it's
17:11great
17:11news
17:11thank
17:11you
17:12so
17:12much
17:12transformation
17:13must
17:13be
17:14accompanied
17:14for
17:15sure
17:15thank
17:16you
17:16and
17:17now
17:17could
17:17you
17:17discuss
17:18a bit
17:18this
17:19upcoming
17:20tech
17:20innovations
17:21from
17:21Microsoft
17:22that
17:23you
17:23believe
17:24will
17:24be
17:24game
17:24changers
17:25in
17:25the
17:26finance
17:26sector
17:27and
17:28perhaps
17:28at
17:29the
17:29same
17:30time
17:30make
17:31sure
17:31the
17:32security
17:33compliance
17:33of
17:34its
17:34financial
17:35solutions
17:35in
17:37fact
17:37we
17:38have
17:38participated
17:39a lot
17:39of
17:40experimentations
17:41by now
17:41on
17:42gen
17:42AI
17:42we
17:43believe
17:43that
17:43gen
17:43AI
17:43will
17:44be
17:44a
17:45real
17:45game
17:46changer
17:47but
17:48we
17:48believe
17:49that
17:49the
17:50next
17:50step
17:50will
17:51be
17:51really
17:51to
17:52not
17:52only
17:53use
17:54AI
17:54to
17:56reduce
17:57costs
17:57operational
17:58costs
17:58but
17:59also
17:59to
18:01improve
18:02customer
18:03satisfaction
18:03in
18:04bank
18:06for
18:08instance
18:08we
18:09can
18:09use
18:10gen
18:10AI
18:10to
18:11enhance
18:12capabilities
18:13of
18:14advisors
18:15or
18:15contact
18:16centers
18:16staff
18:18we
18:20know
18:20that
18:20documentation
18:22product
18:23financial
18:23documentation
18:24can be
18:25very
18:26big
18:27very
18:27complex
18:28to
18:28search
18:29and
18:30gen
18:30AI
18:31will
18:31definitely
18:31assist
18:33and
18:33coach
18:34advisors
18:34or
18:35contact
18:36centers
18:36staff
18:37in
18:38finding
18:39the
18:39relevant
18:40and
18:40accurate
18:40information
18:41we
18:42use
18:42some
18:43technologies
18:43we
18:45are
18:45setting
18:45up
18:45some
18:46technologies
18:46based
18:47on
18:50speech
18:51to
18:52text
18:52for
18:52instance
18:53to
18:53capture
18:54some
18:54conversations
18:55that
18:55an
18:56advisor
18:57can
18:57have
18:57with
18:57his
18:58customer
18:58we
19:00will
19:00use
19:01some
19:01of
19:02AI
19:02services
19:04to
19:04analyze
19:05the
19:05content
19:05to
19:06fetch
19:06some
19:07information
19:08from
19:09the
19:09customer
19:09who
19:10is
19:10part
19:11of
19:11his
19:11conversation
19:12maybe
19:13in
19:13the
19:13core
19:13banking
19:14system
19:14and
19:15we
19:16will
19:16correlate
19:17all
19:17this
19:18information
19:18with
19:19AI
19:19in
19:20order
19:21to
19:21find
19:22the
19:22relevant
19:23documentation
19:24that
19:25applies
19:26to
19:26this
19:26particular
19:27use
19:29case
19:29and
19:30in
19:31this
19:31document
19:31find
19:32the
19:32exact
19:32relevant
19:33information
19:33and
19:34all
19:35of
19:35this
19:35can
19:36be
19:36done
19:37in
19:37real
19:37time
19:38to
19:39support
19:39the
19:41advisor
19:42in
19:43his
19:44customer
19:44relationship
19:45Thank
19:47you
19:47Thank
19:48you
19:49very
19:49much
19:49Marie
19:50Benoît
19:50Over
19:51to
19:51you
19:51because
19:52time
19:52is
19:52running
19:53out
19:53so
19:53could
19:54you
19:54summarize
19:55in
19:56a few
19:56words
19:56the
19:56key
19:56takeaways
19:57of
19:57this
19:58round
19:58table
20:01before
20:02doing
20:02that
20:02I
20:02would
20:04give
20:07some
20:08support
20:09to
20:09one
20:09other
20:09thing
20:10that
20:10you
20:10said
20:10the
20:10necessity
20:11to
20:12skill
20:12people
20:12towards
20:13AI
20:18The
20:18conviction
20:19that
20:19we
20:19get
20:19is
20:19that
20:19AI
20:20is
20:21going
20:21to
20:21change
20:22many
20:23many
20:23things
20:23in
20:24many
20:24many
20:24sectors
20:25and
20:26it
20:27has
20:27the
20:27capability
20:28to
20:28be
20:28a
20:28real
20:29strong
20:30revolution
20:30but
20:31to
20:31do
20:31so
20:31it
20:32needs
20:33to
20:33be
20:33accepted
20:33there
20:34is
20:35a
20:35general
20:35acceptance
20:35and
20:36we
20:36see
20:36here
20:37or
20:38there
20:38some
20:39kind
20:39of
20:39reluctant
20:40opinion
20:41about
20:41AI
20:43the
20:44dark
20:44face
20:45of
20:45AI
20:46and
20:47to
20:47be
20:47accepted
20:47I mean
20:48it cannot
20:48be
20:48accepted
20:49against
20:49people
20:50it has
20:50to
20:50be
20:50accepted
20:50with
20:51people
20:51and
20:52out
20:53of
20:53these
20:53people
20:53that
20:54all
20:54those
20:54that
20:55work
20:55in
20:56the
20:56IT
20:56environment
20:57today
20:57that
20:5845
20:59years
20:59old
21:00so
21:0020
21:01years
21:01after
21:02they
21:03were
21:04they
21:05got
21:06their
21:06diploma
21:07I mean
21:08there
21:08was
21:08no
21:08AI
21:08at
21:09the
21:09time
21:09not
21:10as
21:10so
21:11they
21:12need
21:12to
21:12be
21:12up
21:12skilled
21:13otherwise
21:14what
21:15are we
21:15going
21:15to
21:16do
21:16with
21:16all
21:16these
21:16people
21:17when
21:17they
21:17get
21:1850
21:1855
21:1860
21:19so
21:20it's
21:20really
21:20a
21:21very
21:21big
21:23challenge
21:23for
21:24companies
21:25for
21:25these
21:25people
21:26themselves
21:26and
21:27all
21:27the
21:27initiatives
21:27that
21:28we
21:28can
21:28get
21:28that
21:29we
21:29can
21:30put
21:30together
21:30in
21:30order
21:31to
21:31upskill
21:31them
21:32is
21:32really
21:32welcome
21:34then
21:34to
21:35come
21:35to
21:35the
21:37conclusion
21:40I
21:40like
21:41from
21:41time
21:41to
21:41time
21:42to
21:42look
21:42back
21:44not
21:44at
21:45the
21:45innovation
21:46that
21:46we
21:46put
21:46in
21:47place
21:47one
21:47month
21:48ago
21:48three
21:48months
21:48ago
21:49but
21:49just
21:49to
21:49look
21:50back
21:50at
21:50what
21:50was
21:50the
21:51bank
21:5110
21:51years
21:51ago
21:52and
21:53what
21:53it
21:53is
21:54today
21:54and
21:55to
21:55measure
21:55how
21:56much
21:56is
21:56different
21:57I
21:58mean
21:58everything
21:59is
21:59app
22:00based
22:01today
22:01it's
22:01true
22:02for
22:02individuals
22:02it's
22:03more
22:03and
22:03more
22:03true
22:04for
22:04companies
22:06everything
22:06is
22:07getting
22:07towards
22:07real
22:08time
22:08I
22:09mean
22:09when
22:09I
22:10was
22:10young
22:11there
22:11was
22:12at
22:12least
22:12a
22:12couple
22:12of
22:13days
22:13before
22:13you
22:13send
22:14money
22:14and
22:14the
22:15end
22:16of
22:17the
22:17chain
22:17receives
22:18it
22:18now
22:18it's
22:18all
22:20real
22:20time
22:21it's
22:21all
22:21secure
22:22I
22:23mean
22:23it's
22:24really
22:24a
22:24revolution
22:24that
22:25has
22:25been
22:25made
22:25once
22:26again
22:26not
22:26in
22:26the
22:27what
22:27but
22:27in
22:28the
22:28how
22:29I'm
22:30deeply
22:31convinced
22:31that
22:32we
22:33can
22:33make
22:33it
22:33better
22:34with
22:35AI
22:37smarter
22:37for
22:38the
22:38customer
22:39more
22:40cost
22:40efficient
22:41for
22:42the
22:42bank
22:42more
22:43personalized
22:46and
22:47there
22:47will be
22:47no
22:48choice
22:48we
22:48have
22:49to
22:50do
22:50it
22:50but
22:50once
22:50again
22:51such
22:51a
22:51revolution
22:52needs
22:52to
22:52be
22:52accepted
22:53which
22:54means
22:54that
22:54we
22:54need
22:55the
22:56adoption
22:57by
22:57customers
22:58the
22:58adoption
22:59by
22:59employees
23:00and the
23:01general
23:02supervision
23:02that
23:03gives
23:03this
23:04revolution
23:05the
23:05trust
23:06that
23:06it
23:07requires
23:07and
23:08I mean
23:10all
23:10together
23:11the
23:12stakes are
23:12such that
23:13we make
23:13it
23:13yeah
23:14we have
23:15to
23:15continue
23:15to
23:16dialogue
23:16to
23:16collaborate
23:17to
23:18advance
23:18towards
23:18to
23:19a
23:19more
23:19efficient
23:19more
23:20sustainable
23:20more
23:21secure
23:22financial
23:23future
23:23thank you
23:24so much
23:25for
23:25these
23:26valuable
23:26insights
23:27interesting
23:27talk
23:28thank you
23:29very much
23:29for your
23:29attention
23:30thank you
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