Nu Videocast IR | Artificial Intelligence at Nu
الملخص
TLDRI denne video præsenterer York Fredman, investor relations officer hos New Bank, CTO Victor, der deler sin indsigt i AI's rolle i banksektoren. De diskuterer, hvordan New Bank har integreret AI i kreditvurdering og kundeservice og viser hvordan avanceret teknologi kan forbedre effektivitet og kundeinteraktion. Victor fremhæver også betydningen af Open Finance, hvor deling af data kan berige beslutningsprocesser. AI præsenteres som en vital mulighed snarere end en blot en omkostning, med potentiale til at give værdi for både kunder og banken.
الوجبات الجاهزة
- 🤖 AI er en transformativ kraft for banker.
- 📊 Kreditvurdering kan forbedres gennem dyb læring.
- 💬 AI forbedrer kundeservice gennem hurtigere respons.
- 🔍 Open Finance muliggør bedre dataudnyttelse.
- 📈 AI ses som en eksistentiel mulighed for værdi.
- ⚖️ Der er risici ved AI, som skal håndteres omhyggeligt.
- 💼 AI kan skabe nye jobmuligheder i stedet for at fjerne dem.
- ⏱️ Hurtigere beslutningstagning er gavnligt for kundeoplevelsen.
- 🌐 Kunder skal kunne stole på, at deres data deles ansvarligt.
- 🔄 Automatisk betalingsstyring er en del af fremtidens banktjenester.
الجدول الزمني
- 00:00:00 - 00:05:00
Videoen præsenterer en samtale om AI-muligheder og risici i NewBank, ledet af investorrelationsofficer York Fredman og CTO Victor. De diskuterer, hvordan AI har været en central del af NewBanks strategi siden begyndelsen og dens anvendelse i områder som kreditvurdering og svindeldetektion.
- 00:05:00 - 00:10:00
Victor forklarer, hvordan NewBank anvender AI til at forbedre kundeservice og interne processer, og han præsenterer nogle KPI'er. Der er set signifikante forbedringer i kundeservicesystemer, samt hurtigere og mere præcise processer til intern softwareudvikling og analyse.
- 00:10:00 - 00:15:00
Samtalen omhandler også de potentielle risici ved brugen af AI, især i en reguleret finanssektor. Victor understreger vigtigheden af at have kontrolmekanismer på plads for at sikre, at AI-systemer fungerer ordentligt, og at der ikke er negative konsekvenser for kunderne.
- 00:15:00 - 00:20:00
Yderligere diskuteres, hvordan AI kan forbedre serviceniveauet på kundeservicesagen. Det forventes, at AI vil generere hurtigere svar og sikre, at kunderne får en bedre oplevelse ved at reducere behovet for manuelle interaktioner.
- 00:20:00 - 00:27:16
Victor afslutter med at tale om vigtigheden af at integrere AI i NewBanks forretningsmodeller, herunder i forhold til kreditvurderingsprocesser og øget effektivitet gennem data fra åbne finansieringsmodeller. Fokus for NewBank er at skabe værdi for kunderne gennem deres innovative AI-tilgang.
الخريطة الذهنية
فيديو أسئلة وأجوبة
Hvad er formålet med videoen?
At diskutere betydningen af AI for New Banks og dens strategier.
Hvilke områder ser New Bank som de mest påvirkede af AI?
Kreditvurdering og kundeservice.
Hvordan håndterer New Bank de risici, der er forbundet med AI?
Ved at implementere strenge overvågnings- og valideringsmekanismer.
Hvad er 'Open Finance'?
En platform, der tillader finansielle institutioner at dele kundedata med deres samtykke.
Hvad er potentialet for AI i banksektoren ifølge Victor?
At revolutionere operations og forbedre kundeoplevelser markant.
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- 00:00:05hello and welcome to the first edition
- 00:00:07of our new video cast today's session is
- 00:00:10powered by the team and I am York
- 00:00:13fredman new bank's investor relations
- 00:00:15officer starting today I will begin a
- 00:00:18series of conversations with new bank's
- 00:00:20leaders to talk about hot topics for our
- 00:00:22investor community and it is my pleasure
- 00:00:25to have here VOR leier who is our chief
- 00:00:28technology officer V is a longtime no
- 00:00:31Banker one of the first Engineers to
- 00:00:33join the company and he's here to talk
- 00:00:35about our vision for AR diffusion TS
- 00:00:38thank you Victor thank you so much y
- 00:00:40it's a pleasure to be here pleasure to
- 00:00:41talk to you about AI our first question
- 00:00:45is about you know a topic that has
- 00:00:48gained great relevance in
- 00:00:502023 and should continue to grow in
- 00:00:53interest and applications so how does
- 00:00:55new see AI in terms of opportunities and
- 00:00:59risks that's a great question and a
- 00:01:02question that for us is not a 2023
- 00:01:04question it's a question that we've
- 00:01:06started thinking about since the very
- 00:01:08early days of newbank we came from an
- 00:01:12idea that we needed to really accelerate
- 00:01:14our learnings as we started from scratch
- 00:01:17you know a tech company in financial
- 00:01:18services we didn't have decades to catch
- 00:01:21up with incumbents on the abilities to
- 00:01:23detect fraud to underwrite credit so we
- 00:01:26saw the ability to create a system that
- 00:01:29was Cloud native that had a lot of rigor
- 00:01:32on how we defined and created our data
- 00:01:35infrastructure that was highly scalable
- 00:01:37that leverage machine learning to make
- 00:01:39the best decisions possible so we could
- 00:01:41really accelerate that Loop of learning
- 00:01:44thought that was the only way that we
- 00:01:45can compete in this market and that's
- 00:01:47what we did so we've applied machine
- 00:01:49learning techniques at scale in several
- 00:01:52parts of our business so in credit
- 00:01:54underwriting and fraud and operations
- 00:01:56and for us what we see now is just
- 00:01:58another chapter in that Journey whereas
- 00:02:01in the past we used a lot of the
- 00:02:02supervised learning types of techniques
- 00:02:05we see with llms and the massive impact
- 00:02:08they've had this year that we can
- 00:02:10actually apply new types of Technologies
- 00:02:14to a broad set of problems in our
- 00:02:15business and we're early adopters and
- 00:02:17we're ready to really leverage it in a
- 00:02:19in a way that we believe is going to be
- 00:02:21transformational for our customers and
- 00:02:23for Value creation for new bank as well
- 00:02:26Victor what is the area of Fu in which
- 00:02:28we see the fastest largest effects of AI
- 00:02:33implementation so first I'll I'll start
- 00:02:36by defining a bit of how we see AI right
- 00:02:39so artificial intelligence we see it as
- 00:02:42automated systems that leverage some
- 00:02:45sort of machine learning to make
- 00:02:46intelligent Intelligent Decisions right
- 00:02:48to be able to make decisions that create
- 00:02:50a lot of value and in that we also see
- 00:02:54uh the subset that's more recent right
- 00:02:57now which is generative AI so so when we
- 00:03:00see think about AI as a whole immense
- 00:03:03amount of value creation comes from
- 00:03:05credit underwriting and we see our
- 00:03:06differentiation our ability to
- 00:03:07underwrite at scale so quickly and
- 00:03:09iterating our models in a way that is
- 00:03:12really impactful in our ability to grow
- 00:03:15safely in a way we can detect fraudsters
- 00:03:18in a way that we can route chats route
- 00:03:22uh calls that we can understand what the
- 00:03:24customer needs when they need it so this
- 00:03:27is something that we have a lot of AI
- 00:03:28systems in the backgound
- 00:03:30but now when we see look at llms and
- 00:03:33generative AI we basically believe that
- 00:03:37there's a a new set of possibilities
- 00:03:41right that we can can use and the first
- 00:03:44early adopter pie really will be in the
- 00:03:47operation side that's where we're
- 00:03:49talking to customers it's natural
- 00:03:51language there's a lot of back and forth
- 00:03:53it's something that requires a lot of
- 00:03:55precision and something that's very time
- 00:03:57sensitive so being able to use llms in a
- 00:04:00very smart way to improve that
- 00:04:03interaction with customers we think it's
- 00:04:05transformational but it's just not just
- 00:04:07the interface of the customer we also
- 00:04:09see the co-pilot concept permeating
- 00:04:12every aspect of our productivity so it
- 00:04:14starts with agents agents having a
- 00:04:17co-pilot a new bank co-pilot that can
- 00:04:19help answer a ticket faster or parse the
- 00:04:23information better or categorize a type
- 00:04:25of back office flow in a you know a more
- 00:04:28precise way or in a faster way and a
- 00:04:31more assertive way and then we also go
- 00:04:33to productivity touching U software
- 00:04:36development and that's something that
- 00:04:38we're also experimenting with can we use
- 00:04:40copilot to write softer that's more
- 00:04:42assertive that's better can we use it
- 00:04:43also as a sanity check for us to make
- 00:04:45sure that there are no bugs there no
- 00:04:47issues right we also see it on the
- 00:04:49analytics side and and and and and how
- 00:04:52we basically can parse information A
- 00:04:54Better Way come to better decisions vtor
- 00:04:57can you share some kpis of how no has
- 00:05:00been using AI in client facing and
- 00:05:03non-client facing activities and how you
- 00:05:05expect these kpis to evolve over time
- 00:05:09yeah so for example on clients facing
- 00:05:12usages of AI we've had deep learning
- 00:05:15models interacting with customers for a
- 00:05:17while now and it's something that we
- 00:05:20have been working on even before the
- 00:05:22explosion of geni uh last year but as we
- 00:05:26now use gen into these models we see
- 00:05:31sometimes doubling tripling of
- 00:05:34deflections or self-surface rates from
- 00:05:37customers so it means that the customer
- 00:05:39is basically just by talking to the AI
- 00:05:42they can Self Service in a rate that's a
- 00:05:44step change from where we we're getting
- 00:05:46before so it's a it's a really
- 00:05:49significant change and when we answer
- 00:05:51the customer and they're satisfied with
- 00:05:52it and we do it quickly you know it's a
- 00:05:55it's a magical type of experience and
- 00:05:58then when we look internally at when we
- 00:06:00look at things like code generation test
- 00:06:02generation we're seeing also a great
- 00:06:04speed up in our ability to generate that
- 00:06:06code and that those types of things
- 00:06:09they're not 5% improvements here 3%
- 00:06:11improvements there these are step change
- 00:06:14improvements where deploy it in back
- 00:06:16office processes for example the ability
- 00:06:18to get a customer report around
- 00:06:20something that they deem to be a a
- 00:06:23transaction they don't don't recognize
- 00:06:25or a fraudulent Behavior we can much
- 00:06:27more assertively again step change in
- 00:06:30Precision dictate is this what type of
- 00:06:33reason is this what type ofo it is and
- 00:06:36with that we can really drastically
- 00:06:38speed up our Asser our ability to
- 00:06:40service our customers and to solve the
- 00:06:43problem so we're seeing really big step
- 00:06:45changes everywhere we deploy them and
- 00:06:47naturally these comes with small tests
- 00:06:49and then at okay we see different things
- 00:06:51but it's always something that uh it's
- 00:06:53really exciting it's not something
- 00:06:55around the edge it's not a marginal
- 00:06:56thing it it does feel like a platform
- 00:06:58shift but just to address one point that
- 00:07:02I think we can't you know talk about AI
- 00:07:04without talking about risks and some of
- 00:07:06the risks that might exist so I think to
- 00:07:09get the obvious out first right we're a
- 00:07:11regulated entity that means that we need
- 00:07:14and must abide by all regulatory
- 00:07:16framework that's you know on on top of
- 00:07:18us and we do that uh with a lot of rigor
- 00:07:22and a lot of precision not just for AI
- 00:07:23for everything that we operate in uh but
- 00:07:26then AI I think introduces new types of
- 00:07:28risks right so there there's the risk of
- 00:07:31just what is this new system that humans
- 00:07:34it's not just us but like humans don't
- 00:07:36fully understand how it works and how to
- 00:07:39explain it and it's for us it's all
- 00:07:42about having a very rigorous approach to
- 00:07:45evaluate the input the output of these
- 00:07:48models and to have guard rails and
- 00:07:50mechanisms to make sure that it's not
- 00:07:52behaving in a way that can be value
- 00:07:54destructive and that's a series of tests
- 00:07:57it's a Ser series of monitoring
- 00:07:58mechanisms it's also part of manual
- 00:08:01validation so that's something that's
- 00:08:04really really important for us for us is
- 00:08:06to even in an environment in which fully
- 00:08:08explainability is not there we can
- 00:08:10ensure safety by having other mechanisms
- 00:08:14in place to ensure that safety the other
- 00:08:17point that I think is also very
- 00:08:18important to to highlight here is when
- 00:08:20we talk about AI there's a bit of oh the
- 00:08:23machines are going to take over the
- 00:08:25world and what happens to humans
- 00:08:27and my belief
- 00:08:30is every technology shift that Humanity
- 00:08:34has faced down the line met meant the
- 00:08:37creation of new jobs meant the a change
- 00:08:40of new types of work it me meant the
- 00:08:42elevation of humans to be able to have
- 00:08:46even more leverage to impact more people
- 00:08:49to create more value and I truly believe
- 00:08:52especially coming from a very human
- 00:08:54Centric company because we're human from
- 00:08:56with how we treat our customers with how
- 00:08:58we treat our uh our team we believe that
- 00:09:02having that thoughtful approach on how
- 00:09:03we use AI that puts human as you know
- 00:09:07the technology service of humans that we
- 00:09:10can Empower our teams that we can
- 00:09:13service our customers that people can
- 00:09:15get more exciting work get more leverage
- 00:09:18right so that value the human as a human
- 00:09:21The Human Experience the human work for
- 00:09:23us is not contradictory to valueing AI
- 00:09:27but it's a part of that wheel and
- 00:09:30something that we we're very very
- 00:09:32focused on and going to continue
- 00:09:33investing in this direction as well you
- 00:09:36touched upon very in interesting uh
- 00:09:39different levels you touched upon uh
- 00:09:43credit under writing customer
- 00:09:46interaction is there evidence that the
- 00:09:48level of service for instance can
- 00:09:51improve with the use of AI absolutely
- 00:09:54absolutely so we see that AI provides a
- 00:10:00few things that are quite powerful right
- 00:10:03AI is going to be able to one answer
- 00:10:05things immediately not requiring a queue
- 00:10:08of people to actually get to your ticket
- 00:10:11so a lot of the times for customers that
- 00:10:13ability to get some input some feedback
- 00:10:16in a timely fashion that's really really
- 00:10:19valuable right so just First Response
- 00:10:22times is something that can drastically
- 00:10:23change but on top of that we're working
- 00:10:26to get to very high levels of
- 00:10:28conservativeness and so we can get to
- 00:10:30self-resolution to the customer to
- 00:10:32actually get in one contact be able to
- 00:10:36fully answer and deal with their issue
- 00:10:39in a way that with a human would take a
- 00:10:41lot more interactions just naturally
- 00:10:42just because of the nature of how it is
- 00:10:44to communicate with the human so we
- 00:10:46think that uh AI can provide us uh a
- 00:10:49speed that we can create it in a way in
- 00:10:51a thoughtful way that will not uh
- 00:10:53decrease quality and that we can create
- 00:10:56mechanisms that we can iterate tests in
- 00:10:58a safe way in a safe envir environment
- 00:11:00and part of that safety and part of the
- 00:11:02the way we think about it is if it can
- 00:11:04create scape valves if the customer is
- 00:11:07not we don't want anyone to be trapped
- 00:11:09talking to an AI right we want people to
- 00:11:11use the AI as as long as it's providing
- 00:11:14more value than the alternative waiting
- 00:11:16for someone to deal with that
- 00:11:18interaction so that combo really is a a
- 00:11:21way that we can only we only aim to get
- 00:11:23the upside right so that's how we're
- 00:11:25thinking about that the deploying that
- 00:11:27value to to to our customers without
- 00:11:29even talking about uh you know
- 00:11:31efficiency which is one of the pillars
- 00:11:33of the company things that I would be
- 00:11:35doing uh in dollar terms you are now
- 00:11:39doing in cents I guess so we the way we
- 00:11:44think about using Ai and know I think
- 00:11:46even the way we make revenue and cost
- 00:11:49decisions is there there are one
- 00:11:52decision which is ultimately uh what's
- 00:11:55most value ACC creative to the customer
- 00:11:57and we're going to see that what the
- 00:11:59customer cares about is for their issues
- 00:12:01to be resolved quickly uh in a way that
- 00:12:04they don't have a lot of friction you
- 00:12:06don't have a lot of points of contact
- 00:12:07and when we think about having to wait
- 00:12:09on a queue for a human having to get the
- 00:12:11wrong answer for some reason and so on
- 00:12:14uh a lot of the things that have are
- 00:12:17related to costs are actually just
- 00:12:19inefficiencies of the system so if I can
- 00:12:22plug in technology to remove those
- 00:12:23inefficiencies I would get a better
- 00:12:25service and by removing the
- 00:12:28inefficiencies I remove some of these
- 00:12:29costs and I get a much more engaged
- 00:12:32customer which is ultimately what we
- 00:12:34care and a more engaged customer is a
- 00:12:35customer that will do more business with
- 00:12:37us come back with us we come back to to
- 00:12:40engage with us in a daily basis and then
- 00:12:42we can have a much much deeper
- 00:12:43connection with them and yes we aim to
- 00:12:46be a price leader because we think in
- 00:12:48financial services being a price leader
- 00:12:50is crucial but we don't do it at the
- 00:12:52cost of quality or we don't do it at the
- 00:12:54cost of the experience we think it's
- 00:12:55actually uh the right path is to have
- 00:12:58the two to have the lower cost with a
- 00:13:00better experience conversely we are
- 00:13:03actually increasing primary banking
- 00:13:05accounts which is one of the main levels
- 00:13:08for our value creation right while
- 00:13:10decreasing cost to serve per customer so
- 00:13:13that is exactly the formula that we
- 00:13:14believe is a winning formula let's I
- 00:13:17know touch upon now um the credit
- 00:13:21underwriting improvements that AI might
- 00:13:24provide customer scoring and credit
- 00:13:27modeling are some of the potential
- 00:13:29successful applications of AI as well so
- 00:13:32how do we see this evolving so that is a
- 00:13:35really interesting piece uh we might be
- 00:13:39one of the largest uh machine learning
- 00:13:41systems that does credit underw writing
- 00:13:44just out of the sheer scale scale of the
- 00:13:46number of customers we have number of
- 00:13:48decisions we have right every day we
- 00:13:49make a decision on do I increase your
- 00:13:51limit or not so it's a hundreds of
- 00:13:53millions billions of decisions that we
- 00:13:55make uh and what we saw is that with a
- 00:13:59combo of having the right data ensuring
- 00:14:02data quality ensuring uh the processing
- 00:14:04of the data in an efficient way in a
- 00:14:06scalable way that we can deploy models
- 00:14:09machine learning models using uh
- 00:14:12supervised learning techniques or
- 00:14:14regression classifications the more
- 00:14:16standard approaches we show that we can
- 00:14:20do that in a way that we can iterate
- 00:14:22quickly we can underwrite with quality
- 00:14:25understand what we're doing and do so in
- 00:14:27a way that in 10 years can catch up and
- 00:14:30often times surpass the ability to
- 00:14:32underwrite of O other
- 00:14:34competitors what we see now with deep
- 00:14:37learning and generative AI is a big
- 00:14:39opportunity to wait those traditional
- 00:14:43techniques they brought a lot of
- 00:14:45Leverage for us can we also apply these
- 00:14:48new technologies can we also use deep
- 00:14:51learning with unstructured data to a to
- 00:14:55get a credit scoring to make credit
- 00:14:57decisions as well
- 00:14:59and that has a component around
- 00:15:01rethinking how we plug our data the
- 00:15:04types of models we create and how we
- 00:15:06validate the models and how we also uh
- 00:15:09explain those models it would be
- 00:15:11basically models against models to
- 00:15:13understand the types of decisions we're
- 00:15:14making because just of the very nature
- 00:15:16of deep learning so that's a journey
- 00:15:18that we're just beginning and it's a
- 00:15:20journey that we believe is can be a step
- 00:15:22change when we see in adtech we see that
- 00:15:26deep learning meant often times a step
- 00:15:28change in ability to convert customers
- 00:15:31so that increased Precision that we see
- 00:15:33saw in deep learning and advertising we
- 00:15:35think that perhaps that also exists in
- 00:15:37credit but the co the implications of a
- 00:15:40wrong decision in credit are much higher
- 00:15:43than a wrong decision around what ad
- 00:15:45you're showing a customer so we take
- 00:15:49absolute the the seriousness of
- 00:15:52deploying a new credit model it's
- 00:15:55absolutely something that we take very
- 00:15:57seriously so we are our early adopters
- 00:15:59we experiment with things but we fully
- 00:16:03appreciate the seriousness of making any
- 00:16:05one decision that's wrong with credit so
- 00:16:06we're taking that in a very thoughtful
- 00:16:08experimenting in a very you know careful
- 00:16:11way uh deliberate way but if you're
- 00:16:14going to ask me is there that jump I
- 00:16:16think it's it's possible Right but I
- 00:16:18think we need more time to iterate and
- 00:16:20see that happening in in in production
- 00:16:23in reality and it's going to take a
- 00:16:24little while and the bar is set higher
- 00:16:26because we already are overall better
- 00:16:30than most of our peers in terms of asset
- 00:16:34quality on an income level basis on a
- 00:16:36like for like basis so I think when you
- 00:16:39mention about uh you know uh improving
- 00:16:43further I you are considering probably
- 00:16:46other benchmarks elsewhere yes so we we
- 00:16:49are and I think it's just not just about
- 00:16:51being better on a you know cost or risk
- 00:16:55basis per income it's about
- 00:16:57understanding your decision decisions
- 00:16:58it's about being able to say this is the
- 00:17:01strategy to the optimizes value creation
- 00:17:04for my customer base often times
- 00:17:07optimizing value creation doesn't
- 00:17:08necessarily mean reducing risk right or
- 00:17:10getting to zero risk it's about being
- 00:17:13intentional it's understanding and
- 00:17:15saying this is what I want to happen I
- 00:17:17have the tools that allow me to do that
- 00:17:19and then seeing it play out so we have
- 00:17:21been able to do that with more
- 00:17:23traditional machine learning and we if
- 00:17:26and when we deploy deep learning
- 00:17:28techniques in credit it it has to be
- 00:17:31like that or better so vctor let's
- 00:17:35switch gears and talk about open finance
- 00:17:38for those that watching us uh in other
- 00:17:40countries open finance is a platform in
- 00:17:42Brazil through which financial
- 00:17:44institutions are allowed to share
- 00:17:46customers financial data under their
- 00:17:49consent SII implementation requires
- 00:17:53quality data in high volumes those two
- 00:17:56go hand in hand can give me a few
- 00:17:59examples of how we can leverage open
- 00:18:01financing Ai and the other way around
- 00:18:05that is a phenomenal question so one of
- 00:18:07the things that I mentioned earlier
- 00:18:09was our need to accelerate our learnings
- 00:18:12because we were starting you know
- 00:18:14recently versus having decades uh as a
- 00:18:17company part of the issue there is we
- 00:18:19didn't have that history of the data and
- 00:18:22what the data feels like for the you
- 00:18:24know the customer elsewhere the past
- 00:18:26decade the past two decades their family
- 00:18:29and so on so that richness of data was
- 00:18:31something that was very hard to get to
- 00:18:33and we had to infer through other
- 00:18:34mechanisms and and and find ways to
- 00:18:36mitigate that absence with open
- 00:18:39finance we now get more access if the
- 00:18:42customer grants us that access the
- 00:18:44customer trusts us to that which should
- 00:18:46we believe it's one of the most big the
- 00:18:49biggest signs of that we have a strong
- 00:18:50connection with the customer is there
- 00:18:52willingness to share that information
- 00:18:53with us because it shows hey I trust you
- 00:18:55with my data I trust you with my salary
- 00:18:57this is a you know a relationship that
- 00:18:59it's going to be value Creator let's do
- 00:19:01it so we really value and take it very
- 00:19:02seriously that decision by the way we
- 00:19:05are Market leaders right now in consents
- 00:19:0813.5 million consents right yes yes yes
- 00:19:11that's right that's right so that now
- 00:19:14that we have the data then the data can
- 00:19:17feed into into our models and a model is
- 00:19:20only as good as the quality of the data
- 00:19:21that you have and being able to tap into
- 00:19:24this high volume of data this history uh
- 00:19:27really enriches our models enriches our
- 00:19:30ability to differentiate risk enriches
- 00:19:31our ability to tailor the right product
- 00:19:33to the right customer so that's a a huge
- 00:19:36element of our open finance strategy
- 00:19:39which is only part of the strategy the
- 00:19:41other piece is open AI allows us to also
- 00:19:44have side effects that connect to uh the
- 00:19:47open finance examples I can initiate a
- 00:19:49payment elsewhere and move make money
- 00:19:52movements uh and we believe that because
- 00:19:55of that we can start being the the main
- 00:19:57interface with the cust customer they go
- 00:19:59to new because the experience is
- 00:20:00smoother because they trust us they feel
- 00:20:02like it's a it's a better way to bank
- 00:20:04and they can Bank on other Banks through
- 00:20:08us so that's something that we want to
- 00:20:09more and more push it's like come to us
- 00:20:12and we will help you Bank through other
- 00:20:14mechanisms but as I mentioned trust one
- 00:20:17of the things that we're committed and
- 00:20:18we're working on is we believe that
- 00:20:22AI is has the potential of bringing
- 00:20:25bringing the private Banker to the
- 00:20:27everyone's pocket
- 00:20:29if the mobile phone brought the bank to
- 00:20:31everyone's pocket AI can bring the
- 00:20:33private bank someone can help you assist
- 00:20:35andone so Our intention is that this
- 00:20:38semester we want to launch the first
- 00:20:39version of this and part of the the the
- 00:20:43goal of this AI is not to just offer
- 00:20:47what's the right product for the
- 00:20:49customer within new bank it's to go
- 00:20:51beyond the the boundaries of new bank
- 00:20:52and say what's out there what exists and
- 00:20:55can get open finance data we can get
- 00:20:58data that's publicly available and say
- 00:20:59what's the best product for this
- 00:21:02customer at this point in time and we
- 00:21:04will offer that product even if it's not
- 00:21:06our product because our goal here is to
- 00:21:09establish a relationship of trust and we
- 00:21:12believe that you know the long-term uh
- 00:21:16shareholder value customer value all the
- 00:21:18interests are aligned it's just a matter
- 00:21:20of looking in a really longterm priz the
- 00:21:23early days of our money platform right
- 00:21:25yes internally how do you think since
- 00:21:28you touched uh on revenues uh about
- 00:21:31whether AI is a cost or a revenue
- 00:21:35opportunity I think AI is is more of
- 00:21:39like an existential opportunity it's a
- 00:21:42transformational opportunity and it will
- 00:21:43touch fundamentally rethinking uh our
- 00:21:47revenue streams it will fundamentally
- 00:21:49change our interface with users and will
- 00:21:53fundamentally change our uh operations
- 00:21:56and our ability to service ticket
- 00:21:58tickets and make decisions and deploy
- 00:22:00code and so on so I think it's a
- 00:22:02platform shift that have implications
- 00:22:04that touch every business in different
- 00:22:07degrees it's the cultural aspect of
- 00:22:09making sure that the customer is at the
- 00:22:11Forefront that we think will
- 00:22:12differentiate who uh the companies that
- 00:22:15will win uh this platform shift and the
- 00:22:17companies that will uh not be able to
- 00:22:19Leverage The Tool as much listening to
- 00:22:22you it Rems me about pixs which was
- 00:22:25faced by many parts of the industry as
- 00:22:30uh you know a cost only yes we always
- 00:22:32face that as uh you know savings
- 00:22:35opportunity first and then as a revenue
- 00:22:39driver and I think it's proven right as
- 00:22:41P financing scales every day right
- 00:22:43that's that's the best uh comparison
- 00:22:46that we we can give which is pix
- 00:22:50objectively is good for customers if you
- 00:22:53can make a transfer that's free 247
- 00:22:59reliable that changes your relationship
- 00:23:01with money it enables businesses that
- 00:23:04couldn't exist it remove like reduces
- 00:23:07the the the barrier of entry into the
- 00:23:10the uh you know formal economy in a way
- 00:23:13that no technolog is ever done it's a
- 00:23:16transformational
- 00:23:17technology and if you just look at it as
- 00:23:20the first step oh the first step is oh
- 00:23:24the the players that were're charging
- 00:23:25for for wire transfers we never charge
- 00:23:27for all right but there are players that
- 00:23:30were charging for it the first thing
- 00:23:31they're going to look at is like oh this
- 00:23:33is a big cut in my Revenue line need to
- 00:23:35fight against it or we're not going to
- 00:23:37adopt it or this is not going to be good
- 00:23:38and so and it's understandable why
- 00:23:40that's the gut reaction but we we saw it
- 00:23:44as it's good for customers great they'll
- 00:23:47come back they need to love it they need
- 00:23:49to love doing it with us and if they do
- 00:23:51it with us then maybe we earned the
- 00:23:55right to offer something else all value
- 00:23:58propositions around it right and I think
- 00:24:00pix financing is a great example of that
- 00:24:03uh I think it's not the last example I
- 00:24:05think there will be a lot of other
- 00:24:06Innovations we can do on top of that and
- 00:24:08I think open finance is a machine of
- 00:24:10doing exactly
- 00:24:12that how engaged in AI do you see new
- 00:24:17Banks management team are they really
- 00:24:20investing in it yes yes uh it is a daily
- 00:24:26basis concern it's something that Davids
- 00:24:28on top of all the time every new uh
- 00:24:32announcement every new uh technology
- 00:24:34coming out every new project you know
- 00:24:37our M team is engaged trying to
- 00:24:39understand how to leverage it trying to
- 00:24:40really stress test our strategy obsessed
- 00:24:43about our ability to get the best talent
- 00:24:45to work on this uh making sure that we
- 00:24:47have the appropriate uh funds to support
- 00:24:51that growth speaking of our future
- 00:24:53Vision now we already touched upon the
- 00:24:57concept of the money platform new is no
- 00:24:59for pushing the industry forward with
- 00:25:02Innovative technology how do we stay
- 00:25:04relevant and ahead in a topic that's
- 00:25:06been so heavily studied are there any
- 00:25:09less obvious applications that you think
- 00:25:12someone might be
- 00:25:13missing so I I don't think it's about
- 00:25:17less obvious I think it's about creating
- 00:25:21applications that feel obvious but
- 00:25:24they're hard and they require a lot of
- 00:25:27non sexy work and they require the right
- 00:25:31cultural alignment and obsession with
- 00:25:34the customer so when we talk about money
- 00:25:37platform which you know in a very
- 00:25:39simplified way it's about getting the
- 00:25:41private Banker into everyone's pocket
- 00:25:44it's about things like a self-driving
- 00:25:45Bank the idea that you don't need to be
- 00:25:50consciously aware of the actions that we
- 00:25:52you're taking all the time to make sound
- 00:25:55financial
- 00:25:56decisions and and to be able to do that
- 00:26:00it's a lot of Plumbing work it's about
- 00:26:02making sure that auto payments work it's
- 00:26:04about making sure that you have all the
- 00:26:07payment mechanisms in place that you can
- 00:26:10create the experiences that allow the
- 00:26:12customer to make the right decisions at
- 00:26:14the right time and each customer has
- 00:26:16their own pattern of behavior and we
- 00:26:18need to talk to a bunch of customers to
- 00:26:20understand what's your mental model what
- 00:26:21works for you what do you understand
- 00:26:23this how do I position it how do I show
- 00:26:25it so what we see is every time we build
- 00:26:30a new feature after we deploy it it
- 00:26:33becomes oh that's so obvious but it's
- 00:26:36only obvious because it's obvious for
- 00:26:38the customer there's a clear pain that
- 00:26:40they had and after you do it well at
- 00:26:44scale it becomes obvious for everyone so
- 00:26:47when we think about money platform when
- 00:26:48we think about self-driving Bank when we
- 00:26:50think about how we're deploying open
- 00:26:52finance at scale I believe it will feel
- 00:26:54a lot like that you'll feel like oh it's
- 00:26:57obvious after we do it that's so
- 00:27:00inspiring can't wait to see what is next
- 00:27:04so thank you so much for your time today
- 00:27:06vctor and thank you for watching as well
- 00:27:09thank you thank you so much thank you so
- 00:27:14much
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