Good evening, ladies and gentlemen. Welcome to the Alpha Bank Innovation Day 2025, an event dedicated to the transformative world of artificial intelligence. We are here today because AI is fundamentally reshaping how we live, how we work, and how we do business. Throughout this evening, we'll exchange perspectives, explore real applications, and highlight how Alpha Bank is at the forefront of this shift. We are driving meaningful change and are strengthening our role as a leading force in a rapidly evolving economy. A special highlight, of course, of today's event is the final stage of FinQuest, Alpha Bank's International Innovation Competition. Our finalists will pitch their AI-powered solutions across diverse sectors, from agritech to fintech and beyond, showcasing bold thinking and actionable innovation. Later, we'll celebrate and reward the winning ideas, those with the potential not just to improve the future, but to actually redefine it. Our purpose today is to strengthen the innovation ecosystem through idea exchange, inspiration across industries, and international perspectives. We are very excited to welcome here today representatives from all major Greek banks, leading Greek organizations, members of the Greek government, innovative startups, venture capital firms, and partners such as Microsoft, IBM, EPAM, EY, Accenture, and Endeavor Greece. Innovation Day has been designed to spark dialogue, foster connections, and highlight where the true value of AI lies. We are pleased to begin today's program with a welcome from Alpha Bank Group CEO Vassilios Psaltis. Please join us on stage. Ladies and gentlemen, distinguished guests, partners, and friends, welcome to the Alpha Bank Innovation Day. This is our inaugural forum that builds on the success of the FinQuest competition to shape a broader dialogue on how the financial and fintech ecosystem can bring forward new ideas for making sure that we all are going to be enjoying a better future. This is where bright minds on the one hand and bold ideas on the other hand do come together to shape tomorrow. This year's theme, which is using AI power for the future of finance and all that, we're going to be doing this together. This, in my mind, it perfectly captures the notion, the spirit of where we're currently living in. It's a time when artificial intelligence and fintech innovation are converging, not only to transform how banks operate, but also to redefine the relationship between people, technology, and trust. Just a few years ago, Greece was taking its first step as far as the innovation arena is concerned. Today, look where we are. We're marching ahead in full speed. According actually to Endeavor Greece, the Greek tech ecosystem is now valued at about EUR 12 billion, which is quite something if you compare to where we were two or three years back. Greek founders have raised $1.3 billion, both in terms and equity, just in 2024, out of which $400 million were actually related to startups that were established and were run here in our country. This milestone marks a dynamic and resilient ecosystem. The innovation engine is running at a fast pace, and Alpha Bank is quite proud to provide the fuel to it. We are collaborating with Moveo, one of Greece's most promising innovators. We have acquired FlexFin, which is a digital factoring that provides factoring for SMEs. Our venture capital arm has already invested something like EUR 16 million directly and indirectly into the fintech space, but at the same time, we're looking forward towards tripling it to EUR 45 million by 2027. At the same time, we do actively test fintech solutions. For example, we have run over 15 pilots with emerging fintechs just over the last 18 months. Each of these steps is part of a clear vision. We want to blend the agility of the startup world on the one hand with the trust, scale, and experience of a systemic bank. This is to create a more efficient model of banking that moves faster and, most importantly, that moves smarter. At Alpha Bank, we do pursue innovation in two complementary ways. At first, we're building an innovation ecosystem that brings together talent, technology, but also procures for partnerships. We cultivate future skills of people, develop in-house innovation, and we collaborate with leading technology companies that place the customer at the very center of what we're doing. Inorganically, through our dedicated innovation and partnership team that looks forward to forming partnerships in order to unlock new capabilities and value, and on the other hand, organically, by building from within. Through a defined methodology that we're using that brings customer into the innovation process from day one, we are actually co-creating solutions with them, and this is something that resolves real-life problems. The my Alpha Vibe app is a prime example to that. This was the first digital pocket money product that we have launched, and that was an outcome of exactly that process. The feedback that we got into practice, and we got out with a unique product. It is already evolving into the next, even more innovative version. This is how we view innovation, not as a one-off disruption, but as a living process of learning and experimentation. Today's forum is as much about fostering the ideas that can upscale innovation in our sector as it is also about leveraging technology to create new experiences that empower our customer base. At the center of this transformation is artificial intelligence, which is fundamentally redefining how banks think, operate, but also connect to their customers. AI gives us the opportunity for radical repositioning. This practically means transforming banks from product providers into intelligent organizations that understand, that predict, and that respond to customer needs de facto in real time. That means that we are gradually evolving from being transactional to anticipating customer needs and proposing solutions, standing as a true partner vis-à-vis our customers. AI allows us to focus on what humans do best, and this is empathy, creativity, and user judgment. As we become an AI-first bank, the way to ensure this is by remaining a human-first organization. At Alpha Bank, our purpose is to empower progress in the life of business for a better tomorrow, and such progress needs partnerships. We do not believe that progress can be made simply in isolation. We believe in fostering partnerships that combine perspective, experiences, and ambitions to create new solutions. Today's initiative is pioneering because it aims to bring forward the power of collective intelligence. We have come together today to exchange ideas, to share experiences, and be inspired by what also other organizations are achieving, both within but also beyond the financial services industry. This forum brings together startups, industry leaders, and technology partners to imagine what comes next, but also to make it real. We are honored to host distinguished thought leaders and partners from global technology giants as Microsoft and IBM, technology integrators like EPAM, to senior banking executives from UniCredit, Eurobank, Piraeus Bank, National Bank of Greece. This demonstrates our commitment to collective advancement of the Greek financial ecosystem. From Greece's broader ecosystem, innovation leaders from TITAN, Kotsovolos, Moveo, and others who are bringing transformation across sectors. We're also welcoming academic partners from the Athens University of Economics and Business who ensure that we maintain the crucial link between research and practical application, as well as venture capital firms like Big Pi, who bring ideas to life. Special recognition goes also to Endeavor Greece, who has been an amazing partner in shaping this forum today, helping us to connect with broader parts of the innovation ecosystem. Of course, a warm welcome to the FinQuest 2025 finalists joining us from Switzerland, from Israel, from Spain, and Greece, an outstanding team that represents the creativity, agility, and global reach of the fintech world. Their AI-driven solutions show us how technology can create value across borders and across industries. Each of these individuals, they do bring unique perspective into how AI can be used not only to create efficiency, but also to create meaning and long-term value. This spirit of partnership, of openness and shaped ambition is what makes this initiative unique. Because the future of innovation would not be built behind closed doors. It will be built together across industries, across borders, across disciplines. We begin today's journey with inspiring discussions, visionary ideas, and the FinQuest 2025 finalists. Let's keep one principle in mind. The true promise of AI technology does not lie in technology itself, but rather in our capacity to use it wisely. At Alpha Bank, we are committed to leading this transformation, shaping a future where finance is not only smarter and faster, but also more human, more inclusive, and more sustainable. We are committed to support Greece's innovation ecosystem as an investor, as a partner, and as a creative testbed where innovative scale-ups can be put to practice. Thank you for being part of this journey. Welcome to the Alpha Bank Innovation Day and to the future we're building together. Thank you. Thank you, Mr. Psaltis, for sharing a vision anchored in purpose and innovation. Of course, a big thank you to all of you for joining us here today at Onassis Stegi and, of course, via live streaming at home. Now, let's please welcome our keynote speaker, Tey Bannerman, AI strategy leader and former McKinsey partner, in a fireside chat with Michalis Tsarbopoulos, Chief Digital and Technology Officer at Alpha Bank. Please join us on stage. Today, I have the pleasure of sharing the stage with Tey. Tey is one of the leading personalities and voices regarding AI. It's one of those people that has shown how to translate AI from hype into real value. Talking about that, you've been one of the key voices, one of the leading voices in social media talking about, you know, is it hype, is it value? Tey comes with over two decades of experience in product management, in engineering, in strategic consulting, and he has helped leading global organizations navigate the complexity of AI transformation with a truly human-centered approach. Tey, welcome, welcome to Greece. Welcome to Alpha Bank Innovation Day. Thank you so much for having me. It's an absolute honor to be here, and thank you all for being here as well. I'd like to start our conversation exactly by touching upon this topic around value. You know, everybody's talking about AI. Is it hype? Is it real value? I know you've spoken many times about the gap between what AI can do and what it really does or what it takes to generate real value. In your view, are we in a genuine value creation phase or are we, you know, in the hype and creating another bubble? It's a great question, and I think really we're in both at the same time. I think there is a lot of genuine value creation. I think if you look at specific domains like personalization, like customer service, like pattern matching at scale, you're seeing real kind of business and enterprise use cases for AI that are driving real value. At the same time, I'm sure you've all seen the numbers and reports. 75%, 90% of AI implementations are failing, and I think that's what's driving a lot of the hype. A combination of that plus a little bit of overpromising from vendors about the real-world capabilities and what's required. We have some of those vendors around. Hopefully, they're not the ones overpromising, right? I think the overpromising is interesting because I think if you take any topic in AI, like AGI, for example, you have a massive range of kind of estimations on timelines and disagreements between experts. It is a world where nothing is concrete, and I think that kind of adds to the hype cycle as well. Okay. So you're saying, yes, there is an exaggeration. Probably there is a bit of a hype, but there's certainly real value to it. Absolutely. Absolutely. Okay. Turning to another topic, which is about society and the workforce. Yeah. You know, when we talk about AI's accelerating capabilities, the conversation naturally expands beyond technology. We are entering a phase where AI is reshaping business models. It's reinventing ways of working, habits, what have you. The question is, you know, as AI is becoming more mature and more capable, what do you expect to happen to the workforce? Should we be expecting a replacement, an augmentation, a redefinition of roles? What do you see coming? I mean, I really think it's going to be a combination of all three, but I think ultimately it depends on organizations like the ones here and the leadership of those organizations deciding what's the right ratio for that future workforce that we want to create. Because there are definitely clear areas where it does make sense to have AI taking a lot of tasks and automating tasks that might be manual and tedious. There are many, many areas where it does not make sense to have AI doing tasks. I do think that there's a lot of very kind of decision-making with conviction that's going to happen now that really determines whether it's augmentation or replacement or a combination of the two. Okay. So you're saying it probably is going to be a combination of all three, right? Yeah. Yeah. Okay. Good. Turning to society, I think the main question is, at the end of the day, do you think AI is going to be beneficial for the society or to make it even more specific since most of us here are working for banks? You know, how can we build AI systems and solutions that serve humans and communities and that not just maximize shareholder value? Yeah. Yeah. Is there a way to strike that right balance? I think there is. Kind of, and I've seen both sides. My background, I was a software engineer for many years. I was within IT and technology teams. I was on the side where it was technology first. Kind of, you know, over time, it kind of really moved to, okay, how do we put the human at the center, as kind of Mr. Psaltis said at the beginning? I think it starts with design principles. If you design from the principle of asking the questions of who could be harmed, what could the human outcomes be as a result of what we're building, you tend to really approach the way you design AI solutions very differently. You have very different teams. It isn't purely technical talent and engineers. You have different kind of capabilities and decisions that you're making along the way, but you also kind of start to think about the second and third-order effects of some of these systems and decisions that you're making. I think it's all about the design principles that you start with that really determine the outcomes that you get to. Okay. All right. That's important to underline. It's not about focusing on the technology, but starting with the solution and the design and the kind of problem and the kind of objective that one needs to serve through AI. Yeah. Good. Turning now into another topic, which is about organizational transformation, organizational readiness, or AI readiness, as we call it. You know, typically, big organizations like banks, for example, I'd like to think of ourselves as being quite ready for it. That's not necessarily the case for the larger part of the industry, which is SMEs, for example, or medium-sized companies. What is your view on this? Do you see the industry, the broader, let's say, corporate world being ready for AI? If you were to advise medium-sized or small and medium-sized enterprises, what would be your advice as to, you know, the two, three things they should start looking at in order to get themselves prepared for AI? Yeah. I think it's a good question. I think the fact that we're in a room with a lot of people from banks, and banks have been at the forefront of this change for a long time. I remember the first bank I worked with on AI was eight years ago, and it was a bank building machine learning and customer lifetime value models to predict customer behavior. They also started building large data science teams and so on. I do think a lot of enterprise can learn from what banks are doing. I think kind of, you know, programs like this are a fantastic way to really publicize, okay, not only here's what we're doing, but here's what we're learning and how. I do think that a lot of examples from how organizations that are in the lead do things can really kind of serve as a template. I mean, if we take a practical example in wealth management, an area that I've done a lot of work, you're seeing a transformation where when you put that vision in place for what can a future wealth manager look like augmented by AI, it's AI doing a lot of the manual data entry and summarization work that a wealth manager might do now, but leaving the wealth manager more room to form deeper relationships with their customers to really look at helping customers through life transitions and so on. A lot of those kind of deeply embedded human elements that require complex problem-solving and relationship management. I think you can carry that across to other industries that also require those types of relationships to go, how can we do this, but also what are the foundations? What do we need from a data perspective? What do we need from an organization and culture perspective? Looking at you guys as a template for what they can start doing too. Okay. I think that's good advice. Can we get a little bit more specific? I mean, one thing is to tell them, you know, to advise smaller companies to look at what bigger ones are doing. But in more practical terms, would you say that they need to invest in people? Is it technology that they need to invest in? Is it frameworks? Yeah. Looking at AI, would your advice be to go, you know, to find problems for which to seek solutions? Is it to go maybe in certain domains and think about, rethink the whole domains? Yeah. How would you go about setting this up? I think what we really see is that it's very rarely the technology that's the biggest investment or that's the first thing that you need to do. I think if you look at the kind of percentage of companies that are really doing well with AI, what they tend to do is start with kind of what we call diagnostic honesty, really understanding where we are on data, where we are on our vision and strategy, and using that as a foundation to then decide what do we need to do first. Very rarely is it we need to bring in technology first because a lot of the foundations tend not to be there for a lot of organizations. I think that's kind of the first thing. I think the second thing is really not taking like a use case approach and going, okay, where are the AI use cases that we can actually begin, but looking at a whole domain. For example, looking at the marketing function of an organization and going. We have a lot of marketing people in the room. Fantastic. Good to meet you all. Looking at the marketing function and going, okay, what is our future vision for that marketing function? We look at what are the actual opportunities, what can AI do well, what can our people do well? Then you put together a vision where you go, okay, it's not just about, you know, bringing in AI image generation and AI copy generation here, but going to a world where, okay, our function looks fundamentally different because there are a set of tasks that AI is handling, there's a set of tasks where we're collaborating, but there are also kind of a set of tasks where data is being kind of managed automatically and customers are being communicated to automatically on the one hand, and on the other hand, we are having more one-to-one relationships, for example. It's really taking a slightly more visionary approach than individual use cases. Okay. Final question on this. What about culture and leadership? Do you think there are opportunities, catalysts, or obstacles when looking at, you know, the typical SME company or the typical medium-sized company wanting to enter into the AI space? Yeah. Yeah. I remember seeing a stat recently, and it showed that companies that have an innovation culture are nine times more likely to see benefit from AI initiatives than companies that do not. That involves things like giving your employees kind of flexibility and room to innovate, to actually experiment. It means kind of, you know, having feedback loops where employees can actually say, this is what's working and not working. It means kind of co-designing with employees and with people who actually know the problem space really well. Also, kind of not being afraid to kill an initiative that is not working because then you can divert resources to something that is working. I think that all requires leadership conviction, but it all requires change in terms of culture because, you know, not every organization is set up this way. Some organizations are far more rigid, and it does require that change if you really do want to reap the benefits. Yes. I would say this is not only typical for small organizations, but in some cases also large. Absolutely. Yeah. It takes a little bit of a leap of faith. Yeah. Definitely. To move ahead before you can have full proof. Definitely. Of the value. Great. Tey, it's been a pleasure talking to you. Thank you. Thank you very much, and it was an honor having you. Thank you so much for having me. It's been a pleasure. Thank you. We will both be around for the rest of the evening. Exactly. Thank you, Tey, for those fascinating insights. Michalis, welcome on stage. I will now call our first panel of experts to have our first conversation regarding AI in banking. Please welcome on stage Eleftherios Kororos from National Bank of Greece, Harry Margaritis from Piraeus Bank, and connecting remotely from Italy, Alessandro Barardi from UniCredit. The discussion. The AI couldn't help me today. The discussion will be moderated by George Nounesis from EY. Welcome. Hello. I'm really glad to be here, and I want to thank Alpha Bank for this invitation. This is the first panel I moderate, and it has to do with banks, I have to tell you. I am a partner in consulting at EY Greece since a couple of months ago. I'm starting a new career. At the same time, I am the chair of the National Council for Research, Technology, and Innovation. For the past eight years, I was the director and the chairman of the Board of Demokritos, the large research center of Greece, which actually got to be totally transformed from a dead-end nuclear center into now the AI epicenter of this country. In my experience in the research laboratories, it is breathtaking. The disruption that AI is bringing, it is breathtaking. The acceleration we see in various scientific fields has never been seen in the last decades. Anything from protein structure and drug discovery to telecommunications to advanced manufacturing. You see Nobel Prizes are won based on AI methodologies now and machine learning methodologies. This is all great, and I understand, and I'm very excited about this. Of course, in my everyday life, I use AI now, and I think I'm doing better and better, actually. I'm getting a little hooked. I'm sure you are. When it comes to banks, when it comes to banks, I think things are more of a particular substance. We all feel very, very conservative about banks adopting new stuff and adopting new things. Please just don't treat me as a moderator. Please treat me also as a bank customer. Be careful what you say. In that sense, it's really very interesting to describe how things are developing in the banking sector, whether there is progress or not. Because we are all tied up to these exponential changes in technology, we have to keep on changing ourselves. We have to keep transforming. I know you guys have finished big digital transformation the last 10 years, and you were very happy and very confident about this. Now here comes a new round of AI. Of course, the very first question that goes to all three of you, and I'll start with you, Mr. Tsarbopoulos, what do you think the challenge, how big do you think the challenge is? How ready are the banks today and your bank in adopting AI and actually in adopting AI in the various layers, from the administration to customer experience to security? What steps should be taken further? Where are we? How fast do we move? Right. Thank you. Look, I'd say the short answer is we're fairly ready because, you know, banks have been in AI for years, not generative AI or agentic AI, primarily traditional AI, but we've been there. You know, there's a lot of discussion about is it real, what we are listening about AI, is there real value? You know, we heard Tey tell us about how 70-95% of AI projects fail or they fail to bring real value. That being said, that does not mean that there's not real value. You know, when you look at analysts, they have split views. Half of them will tell you that the markets are exaggerating. The other half will tell you, no, there's real value because we see the big companies leading AI that are producing huge profits. If you ask me, I have no view on the financial markets, but what I can tell you from experience is that there is real value in AI, tangible value. We've seen that over the years. You talked about digital transformation. You're right. We have been running a lot of initiatives in digital transformation over the last few years. The majority of them have been concluded, and now we are entering a new wave, as you said, and they have been concluded with success. For example, in our case, we've managed to digitize the vast majority of simple products and daily interactions with clients. We are close to 100%. We have 98% of transactions happening outside branches these days. Clients have very few reasons to visit a branch these days. Maybe, you know, the main reason to visit a branch is to get advice, which you need a human to get. And we've managed to reach, you know, 30% sales of new products through digital channels. Now, you'll ask me, okay, but that's all about digital transformation. Where does AI fit into all this? An underlying enabler for all this success has been AI because over the years, what we did is, A, we developed machine learning, NLP, and NLU models, which we then leveraged to convince our clients to come and use the digital channels. You know, this does not happen by chance. It's not that when you digitize something, you have people running to use it. You use techniques, you use models, you use targeted and personalized communication, you used advanced segmentation analysis to entice people to use your digital offerings. On the internal front, on the operational model front, we have been transforming the way we operate. We started by centralizing activities, redesigning workflows and processes, simplifying those, automating, and now AI comes on top. AI is giving us the opportunity to drive additional value creation. I'll give you some examples. Currently, we are getting ready to launch a number of AI agents. A good number of them are essentially chatbots. These are agents that have been trained to answer specific questions to our staff regarding policy documents, product manuals, digital banking services, HR matters. There's another cohort of agents that are being produced that will execute certain tasks. For example, we are creating an AI agent that will support relationship managers in preparing for the next client meeting. We are developing an agent that will be helping audit when they're concluding an audit review to validate and conclude their report. Another one that will be bringing together all the information and data for a credit underwriting in a standardized format. What all that means is that once you have a number of workflows standardized, you can then apply an agent that what it does is it takes the trivial daily administrative tasks from the human, and it reduces the time spent on those tasks from minutes to seconds or from hours to minutes. You talked about customer experience and service. Another area of focus for us for years and especially these days. First thing we did is we leveraged NLP and NLU to process the feedback we collect from clients. Every year we collect through campaign mechanisms hundreds of thousands of open text feedback points from clients. If we were to process that with humans, we would need dozens, an army of people to process that. Instead, we have deployed an algorithm. The algorithm contextualizes the feedback, standardizes it, processes the feedback, and then turns that into actions, which we then take into our organization, and we execute on those actions to improve customer experience. Customer service. We were among the first banks globally that launched a GenAI-based chatbot beginning of this year. In our corporate site as a pilot, what we did since then is we expanded the knowledge base of the bot. We embedded it into web and mobile applications. What we are doing now is we are looking to connect that to the call center. Essentially, what we're trying to do, where we want to get at, is to have a system of customer support and service whereby customers will have seamless service, 24/7 support by using AI, remote servicing, and human agents. If I were to summarize, I'd say digital transformation has been supported by AI, and now is the time to scale it up and drive additional value by transforming the organization using AI at the core of the transformation initiatives. Wow. I'm impressed, seriously. I know customer experience, oops, this kind of transformation you're talking about, but indeed this is big progress. It is certainly the digitization, data, data, data. Mr. Kororos, you want to say as well about the National Bank of Greece? Thank you very much for the opportunity to be here and see friends and ex-colleagues and current colleagues. Thank you very much. Yes. Continuing from where Michalis left it, I'll say that data is very important. This is the underlying, the foundation of all of it. What we did is we centralized data. We built on the enterprise data warehouse. We tried to collect all sorts of useful and important information for the bank in one place. This is not only about the strictly bank-related data. We're talking also about processes, circulars, all sorts of things that support also the bank to operate. This is where we put the data. The next thing that we focus on, and I don't know if it has been a coincidence or it's the natural evolution of things, but it was the digital transformation, as we call it, where we digitalized a lot of the processes. We upgraded the systems. We are now at the final stage of this transformation by completing the core banking transformation we're doing. This has been very important. Why? Because by having the data centralized in one place, we can leverage GenAI to build chatbots where we can get information from the chatbot, ask questions, get the answers, etc. By having a modern architecture, it will enable us to have agents that can act upon this information. Not only I'll give a very simple example just for the audience to understand. We launched, like the rest of the bank, our own chatbot on the public portal. You can ask questions, you can get answers. How can I get a credit card, a loan? What are the documents I need, etc.? We saw that the majority of the interactions had been around certain areas. There are some things that unfortunately they still, our customers, need to do or need to consult by going to a branch. They started asking about how do I book an appointment or I want to book an appointment. What we did is we integrated the chatbot with the appointment booking application. Now they can book through the chatbot the appointment. They do not get information when is the next appointment. They can actually book it. They can say for what purpose they want to go there and get serviced. In order to do this, you need to have the technology that will enable the integrations with all the. How long has this process been taking you? Okay. When we talk about GenAI, again, because it's part of the GenAI, this is something that we've been working on seriously for about a year, a year and a half, not just for the chatbot. What we did is we took a significant part of an existing team. We built an AI and innovation team. From there, we created an AI governance framework. There are principles that we have to obey too. We have to prioritize the use cases to build the technology. We started producing, materializing. Your own opinion, your own feelings about value created? I think there is a significant value being created for our customers, but also for the bank itself. I will put an asterisk here because I think it's in the human nature not to trust each other very easily. Imagine what happens when we have to trust a digital colleague. Real value is to be proved, but we are all optimistic. I think that we will see the value. We will see the value, but it is a matter of trust on the tools that we have or the tools that we are building, the digital agents. I mean, in technology, we see it immediately. Of course. We build software using GenAI. Is it the most sophisticated or the best software engineer that you will find? No. How many of those can you have? You cannot have. If you compare this to a medium software engineer, then you have the expert to supervise it, then that's where you get the real value. It is not only in technology. That's what we have to do in the rest of the process of the bank. Okay. Thank you. Thank you. You, Mr. Margaritis, Piraeus Bank, what are the steps? What's the momentum there? First of all, thank you very much, Michalis. Mr. Psaltis, thank you for the opportunity to be part of this amazing discussion and congratulations on this great initiative. Under EU AI Act, I need to disclose the use of AI, so my responses have been prepared with a o3 OpenAI model. I think for us, big systemic organizations in the banking sector, this new wave of technology has been an amazing opportunity to improve the pace of innovation and the pace of change. Clearly, we are not, and we should not be satisfied with the level of customer experience, with the level of efficiency because there is a lot of legacy that we need to fight against. The legacy is not just technology, it is the mindset, it is the culture. I think this is also very, very important for us. We try to address this by being very clear about what we are pursuing in AI, what are our objectives. Our objectives do not have to do with headcount reduction. They have to do with growth. They have to do with efficiencies. They have to do with improving our risk management practices. Again, we have also followed a very methodical approach because if you do not have a solid foundation, you cannot do much. In all aspects, the infrastructure, the data, the governance. We also invested a lot in that. We were fortunate that this new wave of technology found us at a place where we had done a lot of the groundwork to be able and move faster. By the way, if you ask a chatbot to tell you about how to get a credit card and reply as a pirate, you'd be surprised by the answer or in Shakespearean English as well. These are the new things you can no one thinks about, but who knows? They might be relevant to a new way of talking to customers. Talent, skills and talent. Can you find talent easily to go to advance? No. I mean, I think the answer is no. How competitive is this? Everyone looks for the talented people. Actually, I'm very optimistic since yesterday I was at the GenAI Summit and there was a hackathlon and there were some very bright young students. I think they were just finishing their studies and they were given a set of data, reviews of the app. They were given two hours just using a tool called Lovable to build something that would understand the insights, visualize it. What they were able to produce in two hours was amazing. The way they also presented it and their level of their confidence, their strength. I'm very optimistic that we can, if we think, if we focus a little more on connecting the industry with the academia, I think we will. Are you happy so far with the production of talent by the universities, the expertise that young people get at the institute? The supply still cannot match the demand. It is very important to be able and train and upskill your talent pool as well. It is important for people to feel that they're part of this. I think there was a study by Oliver Wyman. The banking sector, financial services and communication, people are the most afraid of this change. The people are afraid they will lose their jobs at a percentage that reaches 67% globally. You need to, if you want people to be on board and be co-pilots with you in utilizing these amazing opportunities, you need to address their fears. Okay. Dr. Barardi, I'm coming to you now. You hear us well, I hope. Yes. You have heard the steps, you have heard the progress, and it did not take that long, like one and a half years, two years. What do you feel, these incremental steps and these small successes, the small wins, if you call it small win? I was very impressed, actually. Do you think this is healthy for the banks or they should go faster? Even for. Good evening. Good evening. Good evening, everyone. It's a pleasure to be in this Innovation Day. I'm sorry if I'm not there with you physically. It's a good point. I got this question from my management during last year when I started setting up this global team leading artificial intelligence across the group. The question was, are we going to be follower or leader? My experience during the last five years in UniCredit Group is that UniCredit, as my colleague was saying before, as artificial intelligence in UniCredit has contributed with different local and significant impact in different domains of the bank with predictive AI, deep learning, automation, computer vision, and so on. The moment AI, with generative AI and with large language model revolution, starts and gives me the chance here to go even farther in the future. The moment AI can start to create agents that can assist humans, augment humans with some superpowers, it is not fair planning how agentic AI may change our workflow as a population, how it will change the backend, the front-end office, but also the relationship with the final customer. Going to your point, coming to your point, in UniCredit, we started in the last five years building a lot of scattered use cases across the group. We learn, I would say, two years ago, that this approach does not scale because if AI transformation is actually digital transformation, it cannot happen in a fragmented way. It requires end-to-end process redesign in the back office to make processes AI-ready because today some processes are not even digitalized. You need to review the process, redesign the process to make them AI-ready. If you want to get the best outcome in terms of impact from artificial intelligence and agentic AI, you need also a new paradigm for customer interaction. We may expect in the future that our customer will want to have with the bank an endless, live, 24-hour context-aware conversation with the bank. As a customer, I would expect one day this to happen. You need to have the right foundation in place. Without foundation, you cannot go fast. You cannot reduce the time to delivery with the technology that is evolving so fast. You cannot scale and you cannot have governance by design that in an organization like UniCredit with more than 10 countries and with the regulation that is coming with the AI Act, it is impossible to manage and scale up in a way that is sustainable. In UniCredit, we have built an internal group-wide artificial intelligence platform shared standard to scale across country, avoiding the duplication and ensuring compliance. Yes, small wins can drive transformation, but only if they are connected to a bigger vision and they are backed by a strong foundation. Otherwise, the risk is that you risk a pilot trap and there is a long transition with, yes, scattered impact everywhere. I have some doubts that this business case will turn out to be positive, okay, over the time. Thank you. Thank you so much. Back to you, Mr. Tsarbopoulos. Michalis, please. Michalis. You've known Mr. some years now. Yes. It's okay, but it's a formal setting. All right. So Michalis, I know you must be one of the people in Alpha Bank feeling most of the pressure to move fast from all. Spot on. I know you do. We all feel, we all feel one way or another the same pressure, believe me. It is not so easy though. I mean, you are banks and we all expect that you comply with all sorts of regulations and risk aversions. How do you balance this? What is exactly the kind of dialogue you build? Yes. Let me start with the positives. All right. What we have going for us is readiness and technology and maturity. Why? Because of the things I mentioned. AI is not new to us. We are experienced. Agentic AI is a natural evolution to traditional AI, also from an organizational perspective. Also, we have great partners, right? You have Microsoft, you have IBM, EY, EPAM. You have huge technology providers that have been investing a lot of time and millions, if not billions of dollars or euros in not only developing the technology, but tailoring tools specifically for the financial services industry. Why is that so? Because apparently the potential value creation is enormous. We have a lot of tailwinds, if you like. On the other hand, as you. Nice. The tailwinds are nice. Correct. On the other hand, can we do that full speed without control? We cannot. We heard Alessandro talk about foundation. Lefteris talked about data. Let me talk about what I call foundation. In order to be able to scale AI and to do it in a productive and prudent manner, you need a foundation that consists of four parts. First of all, people, right? Right now, technology is progressing a lot faster than our people can catch up. So we need to make sure that we continuously educate and train and upgrade our people so that they do keep up with the progress in technology, number one. Number two, cost controlling and real value realization. We talk about value, all right. Value generation in any organization, especially in banks, is not about the technology itself. It's about corporate discipline and about tight governance. Unless you do the right things in the right way, again, we heard they talked to us about a domain-based approach. Alessandro also said the same thing, not go piecemeal case by case, but go in a certain domain and rethink, redesign the whole way of operating that domain. This is about design, about governance. This is about choosing what to do and how to do it. You need to measure what you built. You need to measure what it costs. You need to measure adoption and usage. Most of all, you need to measure real value generation. This is the second condition. The third is data. You need to ensure that you have all sorts of data, structured and unstructured, that you have it validated, curated, and readily available in a secure manner by all AI tools. That brings me to the fourth point of the foundation, which is security and AI responsibility. It goes without saying that unless your systems are fully secure and unless your data are fully encrypted and protected, there is only so much you can do. We are spending a lot of time and a lot of investment in elevating our defense systems against emerging cyber threats on one hand. On the other hand, we are putting in place the quality checks and controls that we need to have to ensure that AI is used responsibly, that models are unbiased, and in general, that we use the output of the models and the agents for creating value not only for the organization, but also for our clients. Now you're tempting me to ask a question that is not planned. How about your quantum readiness, post-quantum cryptography readiness? How many hours do you have? No, just give me two words. No one's ready for quantum. No, of course. No one really understands quantum. Are you preparing? If you are physicists, you do a little bit. Not really, not yet. It was one of the major topics at the Singapore FinTech Festival. Exactly. Yes, exactly. Quantum and agentic were the top. Maybe next year we will be talking about this. Maybe, or the year after. We'll see. To conclude, you were 100% correct. These are opposite forces. On one hand, there is technology that can and corporate pressure that pushes you to move forward fast. On the other hand, you need to have control in place to make sure that you do it in a productive manner, in a protected manner, in a prudent manner. Okay? I had a question from Mr. Kororos. Maybe I call you Eleftherios. Yes. Thank you. I don't make discrimination. Even though I suspect your answer, are you for stricter regulation? I think that regulation can provide clarity, so clarity on how we do things. We need to think of regulation as an enabler, not really an obstacle. It can give us clarity. It can also help us have a level playing of things like risk management, for example. Yeah. Of course, I'm not talking about only the Greek setting, but also Pan-European setting. Somehow we need to find the right balance not to block experimentation. Okay? The truth is that. There's competition. This is why I'm asking. There's competition. It's the U.S., it's Asia. The truth is that the world is so connected nowadays, okay, that even if it doesn't happen in Europe, it happens elsewhere and it affects us. Yeah. So I mean, this is, you know, these days in the domain of defense innovation, everybody talks about rapid adoption and there are regulations and ethics there as well that are maybe even much more important to consider. I think that regulation needs to be in place, but we need to regulate more what is more important and less what is less important. We need to follow a risk management approach. Where are the big risks of leaving it totally free? Regulate that. Where are things that we do not mind if we fail somewhere? We need to enable experimentation. The truth is that we see the U.S., we see Asia, we see China in Asia pushing experimentation with GenAI, etc. In Asia and China specifically, they have even open-sourced their GenAI models because they are afraid of the criticism of what may be in the model, etc. The black box. Yes, the black box. Things are moving fast. There is a danger of being stuck in Europe just trying to regulate everything. A question for Harry now. Customer experience. I was visiting an older relative and she was trying to make a bank transaction over the phone. She kept saying, "Anthropos, Anthropos." She wanted to be understood and she wanted to speak to a real person. How do you think? How is society perceiving? How are your customers? How are they responding? Not only in their everyday transactions, but for financial advice as well, which is the heavy stuff. I think customer experience is definitely the new frontier for all traditional banks because it's not just the people that are not so tech-savvy, the people that are older and they are now, they need to do everything digitally and you need to explain to them and make it easier for them. It's also the, I think the younger generation, they expect a totally different level of experience from banks. Who knows, maybe in a few years, I think Gen As, they only use their AI assistant now for everything. They will use it for shopping. They will use it for who knows what in the future. There is the rest of us also. Probably everyone shortly. How do banks address this? It's not a trivial matter. I think all the investments that we've done in the past, the infrastructure, the security, the trust, and of course in banking, trust is the currency. You spoke in the beginning about perhaps why aren't the banks innovating faster, which I think banks are doing many things that are not visible perhaps to the wider audience. One reason is that you cannot jeopardize trust and you need to find the balance. This is why this whole, I think this is why Alpha Bank is doing this program to try to innovate with young people, with partners. When the time comes to operationalize something, you need to follow a much more rigorous approach. Dr. Barardi, how ready are the customers to trust AI? Today, I mean, you think a bank can use as a marketing tool the fact that they are utilizing AI models and they're advancing their AI technology. Is this going to be an attractive marketing approach? If there is, yes, if there is clarity with our customer. Trust is earned not assumed. Commercial bank has UniCredit with other banks in a complex landscape with all the emerging fintechs. Trust is all for us. We need to be transparent with our customer, how we're using their data and how we are using the data to provide them insight for offering them customized or personalized service. We need to be transparent with the client. We need to start being transparent also with our employee and our front line of our customer. Artificial intelligence, we think, will not replace human, but I think that humans will be replaced by those humans that learn how to interact with agents. At UniCredit, to start being also transparent with the population, the front office, we're using AI copilot for bankers and start providing personalized digital experience for customer. As my colleague was saying before, predictive AI in the past, five years ago or more, six years ago, already powered the next best offer churn model, helping our colleagues in the branch, helping bankers anticipate the needs. Agentic AI will take this farther because we'll enable hyper-personalized product, maybe one day real-time pricing, but transparency and explainability are not negotiable, not only for our customer, but also for our front office. It happens to my team in the past to have more difficulties in having machine learning model outcome adopted by the front office than building the machine learning model itself. Because whenever you're providing a tool to our customer, to our front office in the branches, you need to explain then the outcome of that model. With agentic AI that will be using this model as a tool, this matter of transparency and explainability will be even more important. The governance framework that we put in place also in line with the AI Act principle ensures that every, let's say, AI recommendation, not directly to the customer, but to the front office before going to the customer, is traceable, fair, and compliant. Will customer trust AI? Yes, when it delivers value and clarity. Thank you so much. Today. I'm sorry, I think we've run out of time. I'm giving notice that we've overrun actually our time for quite a few minutes. Thank you so much for participating. It is true that the competition is going to be fierce. Resource is limited. The talent, very difficult to get. I believe next year, I'm hoping to see you guys more involved with AI factories. We'll be writing code ourselves. Huge AI factories. We will go back to writing code because there's not enough time. You think so? Of course. There are many incredible startups being created that can be a valuable ally for you. Of course. Cloud sovereignty, you know what you do with your data. All these are very, very, and of course, post-quantum cryptography and cyber. See you soon for more discussion. Thank you very, very much. Thank you. Thank you. Thank you all for this panel. Now, a small change in the program. We will now invite the Minister of State, Akis Skertsos, on stage to discuss on AI and how it reflects on society. Please. Good evening. I'm by no means an expert on fintech, so don't expect from me advice on how we can develop AI applications in the banking sector, but I will try to guide you through the government's technology, AI, and digital strategy and how we have developed this since 2019. It is a pleasure to be here today, and I would like to congratulate Alpha Bank for convening us to discuss an issue that is not only timely, but also urgent. A few days ago, I had the privilege to visit Singapore with the Prime Minister, a country not larger than the Island of Ceph alonia, with double the GDP of Greece and a per capita income of $100,000 per year that has embarked on its digital journey 10 years ago. As we were told by the ministers, digitalization and artificial intelligence are not an option. They are a necessity for survival. I was impressed by their broad support program for 250,000 small and medium-sized businesses aiming at their digital transformation and utilization of artificial intelligence. I was also impressed by the government's strategic decision to create a strong mechanism for digitalization of the state in the field of GovTech, which employs nearly 4,000 IT engineers and product developers as civil servants, very well paid. Their mission is to transition the state and economy into the era of AI in a way that leaves no one behind. AI for the Public Good is their slogan, and it fits perfectly to the concept of responsible innovation that we also share and apply here in Greece as our own national digital vision. In the context of government, responsible innovation is measured by how efficiently it distributes the tech dividend, the societal and economic benefits generated by technology across all citizens, regardless of social class, access to power, or background. This aspiration is deeply rooted in our Prime Minister Kyriakos Mitsotakis' vision, one that has guided Greece from the brink of exiting the Eurozone only a few years ago to becoming one of the continent's best-performing economies today. The results speak for themselves. Greece is now consistently outpacing the Eurozone in growth, restoring fiscal credibility, and building an environment where businesses can thrive and citizens can prosper. In an uncertain global landscape, and while the Eurozone grew by just 0.9% in 2024, our economy expanded by 2.3%, more than double the average. Unlike much of Europe, our growth is anchored in fiscal discipline, creating a primary surplus of 4.8% of GDP in 2024, while the Eurozone remained in deficit and also the fastest declining public debt in Europe. Fiscal stability and economic progress, of course, is just one part of the equation. The state has undergone a profound digital transformation since 2019. With the launch of Gov.gr, we have digitized public services and made the interaction between the state, citizens, and businesses seamless. Building on this digital revolution, we are now implementing our AI strategy, a blueprint designed to help us leapfrog in sectors such as education, healthcare, innovation. It is no coincidence that Greece has been selected as one of the first countries to host an EU AI factory. Over the last 6.5 years, Greece has moved through three interconnected phases of digital transformation. First, digitization, building digital rails, universal connectivity, and a unified government portal. Second, productization, turning services into user-centric digital products and mobile applications, redesigning state services around citizens and not bureaucracy. Now, today, we enter the AI phase, embedding artificial intelligence on top of this digital foundation to deliver proactive, personalized, and secure public services, modernize the state, strengthen democratic capacity, and above all, strengthen institutional trust. These layers reinforce each other. Strong digital infrastructure enables applications, and applications become exponentially more powerful when they are AI-enhanced. We have executed one of Europe's fastest digital transformations by consolidating the state into one digital front door, Gov.gr, scaling from near zero in 2019 to more than 2,500 services and 8 million users today. Citizens can now access their certificates, prescriptions, tax services, notarial functions, and licenses digitally. This front-end shift was supported by nationwide infrastructure upgrades, 99% of 5G coverage as we speak, 46% of fiber to the premises by 2024, enabled by EUR 3.3 billion under our RRF Greece 2.0 digital investments plan, including major support for public sector services and SME digitization. Greece's ICT market has grown to EUR 8.7 billion and is on track to reach EUR 15 billion by 2030. The country is becoming a regional data infrastructure hub by attracting major cloud and data center investments by Microsoft, Amazon, Google, DATA4, and others, which are establishing, as we speak, 18 and more facilities across Attica, Thessaloniki, Crete, leveraging Greece's strategic geolocation and upgrading digital infrastructure. Greece is building also our own sovereign AI capabilities through DAEDALUS, an 89 petaflop national supercomputer, and Pharos, one of Europe's first national AI factories, EUR 30 million cost, co-founded with EuroHPC. These assets support research, support startups, public sector AI pilots, and critical sector uses such as healthcare, education, and civil protection, ensuring Greece is not only a consumer of global AI technology, but also a producer of sovereign solutions. Greece is also transitioning from building portals to delivering scalable digital products and AI-powered citizen services. In our digital government ecosystem, there is a prominent place for our publicly produced digital identity and citizen services. The Gov.gr Wallet delivers secure mobile identity and verifiable credentials, ID, driver's licenses, vehicle registries, health insurance, academic and disability IDs used by more than 4 million citizens. This is the basis for a mobile-first state integrated with daily life and economic transactions and secure by design. Other AI applications in public services like Wildfire AI, a real-time AI fire detection system via cameras and drones identifying more than 100 wildfire events last year, or AI legal checks for our land registry mechanism, which automates and cuts review time from two hours to just 10 minutes, reducing costs from EUR 15 to EUR 0.14. Finally, our newest baby, a government-owned AI incubator for in-house AI delivery, like the Singaporeans are doing, which is recruiting private sector technical talent to develop sovereign tools for the state. Its mission is to internalize AI production by building high-impact AI applications for national needs, citizen interaction, and policy delivery at a fraction of the cost, at a fraction of the time. No, it isn't all rosy. The state is only one piece of this puzzle. The rise of artificial intelligence has revealed something unprecedented up to now: the concentration of extraordinary power in the handful of technology companies, power and capitalization that in many cases exceeds the strategic capabilities of nation-states themselves. These firms operate as digital nations in their own rights. They command vast compute resources. They shape global information flows. They influence daily lives, even the mental health of billions of citizens. Their reach is immeasurable, and their decisions, technical, commercial, ethical, can have geopolitical consequences and can affect the fabric of our democracy. That is why we need strong, modern, independent state institutions, institutions that are capable of understanding the technology, that regulate fairly, and keep the balance of power in check. There's a fine balance, indeed, to be kept between over-regulation that suffocates innovation and complete deregulation that over-dominates the public sphere by the interests of the very few and the very mighty. We stand in the middle. We need the private sector. We need academia. We need civil society. We also need a smart state. Still, we are not blind to the challenges ahead. Greece and Europe have a long way to go. We do face fundamental questions, and some of these are the following. Will we remain as Europeans, consumers of technology, or producers of technology? Will we limit ourselves to being regulators, or will we also be innovators? Will Europe lag behind, or will we lead? While the answers to many of these questions may seem obvious to those in this room, the reality is that institutional and regulatory roadblocks persist. The EU single market is far from complete. Inter-European barriers act as a de facto tariff of 44% on goods based on the IMF report. This is why deepening the capital markets must become a European priority. For our companies to compete globally, they need access to deeper pools of interest, of capital, of more efficient cross-border investment, and the regulatory environment that supports rather than constrains risk-taking. The idea of a 28th regime, a harmonized framework that coexists with national rules, should be embraced as a practical step forward. It would also allow innovative firms to operate seamlessly across the EU without being burdened by 27 different sets of rules, accelerating growth, integration, and innovation. Here, I believe we do have a compass. Mario Draghi's report offers Europe nothing less than a roadmap for competitiveness in the age of technological disruption. His call for a more unified single market, greater investment in frontier technologies, and the creation of a European-scale set of champions must not remain words on paper. It must become our shared agenda. Greece is ready to contribute to this vision, leveraging our own reforms, our own AI strategy, our own digital transformation, our own growing tech ecosystem to ensure that Europe does not merely adapt to the future, but actively shapes it. Ladies and gentlemen, artificial intelligence is no longer a distant promise. It is here. It is already reshaping all sectors of life, state, and the economy, the financial sector, your sector, at remarkable speed as well. Across Europe and here in Greece, banks are integrating AI into risk management, lending, investment decisions, customer service, fraud detection, and back-office operations. These technologies enable financial institutions to process vast volumes of data, take faster and more accurate decisions, and automate high-cost procedures that once required extensive manpower. The benefits are, of course, substantial. AI reduces operational costs and increases productivity by streamlining onboarding, KYC services, and compliance checks. These opportunities also come with new risks. Complex and opaque models challenge transparency and challenge explainability. Poorly designed algorithms may embed bias or lead to inadequate predictions. Over-reliance on automated decision-making can erode human judgment and can erode the trust in institutions. This is precisely why the European Union adopted the AI Act, the world's first comprehensive regulatory framework. It promotes innovation while safeguarding citizens' rights. For banks, it establishes clear obligations, transparency, risk assessment, accuracy, robustness, and continuous monitoring of AI models. Dear friends, I truly believe in the emancipating power of technology and artificial intelligence. Digital technology will remain aligned with the public interest only if its development is anchored in integrity, in transparency, in accountability, and a sense of higher purpose for the collective good. These are not abstract ideals. These are the guardrails which ensure that innovation expands opportunity rather than inequality, that innovation strengthens democracy rather than undermines democracy, and that innovation serves humanity rather than shaping it in ways we did not choose. Thank you for your attention. Thank you, Minister, for honoring us today with your presence. Now, it's time for the Pass of FinQuest, the pitch event where innovation meets opportunity. FinQuest by Alpha Bank is an international innovation competition that supports startups and scale-ups worldwide, fostering innovation in banking and beyond. This year, FinQuest 2025 focuses on artificial intelligence across five key themes: banking innovation, family ecosystem, travel, smart living financial tools, and employee training. After carefully reviewing applications from over 20 countries, seven finalists were selected to take part in a two-month accelerator program where they received mentoring and guidance from Alpha Bank executives, venture capital partners, and experienced founders. Our seven finalists represent the very best of this year's cohort, visionary teams harnessing artificial intelligence to redefine how technology empowers people and transforms industries. The top three teams will receive awards from our partners, while the grand winner will have the opportunity to pilot their solution in Alpha Bank. Before we meet the teams, let's take a moment to introduce the FinQuest Judging Committee, the panel of experts who will be evaluating today's pitches. These distinguished professionals have dedicated their time and experience to assess some of the most promising AI-driven innovations. Let's roll the video and meet this year's Judging Committee. Now, please, let's give a warm round of applause for our Judging Committee. We are honored to have you here today with us, and thank you for guiding the next generation of innovators. Come on, let's cheer up a bit. This is the best part. Let's get started. Please welcome on stage our first finalist. Sorry, [Foreign language]. Decode represented by Michalis Rikakis, the CEO. Michalis, please. Good evening, everyone. Hope you can hear me. Thank you for being here. My name is Michalis Rikakis. I'm CEO of Decode, the company that has built Dikaio.ai. I have 25 years of experience in product, having worked in companies like Google and I'm an ex-CPO also of Workable. Tonight, for the next few minutes, I'm going to discuss with you the transformation that AI is bringing for the legal and the compliance teams for smaller and larger organizations and financial institutions. Before we dive in, just a quick word about our team. We combine deep legal experience, advanced AI engineering, and product leadership for over 100 years of collective experience across world-class companies such as McKinsey, Google, Ernst & Young, Amazon, MasterCard, Interim, to name a few. This blend is what is allowing us to build both reliable and innovative products at scale. Across Europe, regulatory and policy complexity is accelerating. Legal, risk, and operation teams face a consistent inflow of new EU rules, and at the same time, they need to accommodate internal mandates, board director decisions, policies, etc. Most of this work is still, unfortunately, being done manually, which means that research, drafting, and reviewing needs to happen by the teams by themselves manually. There is no unified source of truth. There is very little automation and no reuse of past work. The result is predictable, as most of you have certainly experienced: overloaded teams, long cycles, bottlenecks in procurement and credit, delays in strategic initiatives like M&As or portfolio transfers. With these problems in mind, we designed Dikaio.ai. Dikaio.ai is the first AI legal assistant trained in Greek and EU legislation. Dikaio offers instant answers, drafting, and compliance checks. It keeps legislation updated daily. It allows organizations to bring their own data, offers smart prompt libraries, and supports necessary workflows for the work of lawyers and professionals, things like translation of legal documents, anonymization of legal documents, or validation of the compliance. Most importantly, it ensures enterprise-grade security with EU hosting, encryption, and zero use of customer data for training. For legal and compliance teams, Dikaio accelerates legal research, automates drafting, and standardizes reviews. This leads to up to 50% faster research and document creation, reduced external counsel hours, and stronger compliance with fewer errors or penalties. When we are looking at other teams, operational teams like procurement, credit, risk, treasury, those teams handle heavy regulation workflows. Dikaio enables them to bring internal data into AI workflows, to collaborate in private workspaces, and use automated redlining and approval checks. This results in shorter contract cycles, fewer policy breaches, and faster, more consistent decisions across the organization. In large organizations, such as financial institutions, for example, significant friction often appears in strategic projects, things like M&As, carve-outs, portfolio sales, and spin-offs. For those types of projects, Dikaio can support cross-document research, can summarize obligations and risks, and can enable structured collaboration. The outcome? Faster deal readiness, fewer legal omissions, and optimized terms. There are three key differentiators that make Dikaio.ai unique. We have built the only self-updating legislative infrastructure for Greece. This means that the infrastructure we have built is also automatically codifying legislation, which is one of the big challenges when it comes to Greek regulation. It is also doing the same for EU. It is updating daily all Greek and EU legislation and core decisions. This ensures that the answers that the legal system provides are hallucination-free. Secondly, it provides rich workflow features, things that the modern lawyers and professionals need, things like translation of legal documents, anonymization, redlines, policy checks, etc. Last but not least, it's built with high security standards in mind by supporting encryption everywhere, only EU hosting, capability to anonymize documents, sensitive documents before publishing them, and capability to deploy customer on-prem. This is why organizations trust Dikaio for mission-critical legal work. We have only been around since March 2025, so less than 10 months now, and things have scaled rapidly thanks to our lean but exceptional team. We have more than 12,000 right now lawyers and professionals using Dikaio.ai, hundreds of companies of all sizes. It has answered a quarter of a million legal questions, and it's answering at a rate of 3,000 legal questions per day, which is close to what the GovAI legal assistant also is answering. We have also drafted tens of thousands of legal documents. This is not the end of our story, probably just the beginning. In 2026, we plan to provide more tailored compliance features for SMEs, corporates, and the public sector. We will launch AI-powered compliance as a service modules, starting with the AI Act and GDPR, which is being redefined now with the new reality of AI data. In order to allow companies to offload them from the compliance hassle and let them focus on growing and innovating. In 2026, we plan to bring this service to Southern Europe countries. In 2027, we want to add more compliance modules such as DORA or the European Accessibility Act and expand across Central and Eastern Europe. By 2028, we aim to have the full EU coverage for most of the compliance needs for all sizes of companies. Our mission is to give every organization a reliable, secure, and deeply knowledgeable AI assistant to remove legal friction, to accelerate operations, and improve compliance at scale. Thank you for your time. You can try Dikaio.ai, and I'm happy to take any questions. Congratulations. We will start with a question from Mr. Tzamtzis. Here we are. Congratulations for the idea, the application, and of course, your presentation. I have two fundamental, I would say, questions. The first one is, which do you believe is your competitive advantage in order to avoid a heavy competition by copiers, by followers of good ideas in the future? The second one is, why do you consider a bank, Alpha Bank in our case, a good candidate for this participation? Okay, thank you. I will answer first the first part of the question. Initially, for Greece, for example, we have built the infrastructure which can update legislation automatically, something which is not trivial. It has not been done before, and we have spent actually months and months of manpower to actually implement this. Going further and looking at further applications also in EU, not only in Greece, we have a great experience when it comes to product, how to build, how to make things that are hard, are not sexy, how to make them easy for organizations and remove the friction there. For example, the AI Act is going to take everyone by storm. It is going to impact all the companies. They are going to need, and there are not any decent solutions right now. They're going to need solutions which can help them offload this work easily without needing to spend hours and hours deep diving into the processes and their documentation. We believe that our experience in product, which has already been tested in the Greek market, as well as the different applications, tailored applications that we are able to build in order to provide a superior user experience, is what would differentiate us from the rest of the competition. For your second question, we are actually in different stages with quite a few financial institutions when it comes to trying different use cases for our service. The compliance, the legal and the compliance departments are probably the easiest scenario where there's not a lot of implementation needed for those departments. They're able to check policy compliance, regulatory compliance for big volumes of data. They're able to ingest all of the different contracts, for example, that they may have or customer data and be able, from out of those, to pull reports and generate, I don't know, credit risks or things like that. There are different applications that can be used with this type of infrastructure, which is always up to date with regulation, is able to process large amounts of data and large amounts of company files and be able to process those into legal documents and artifacts. Okay. Thank you. One more question. Just let me—I will go to Mr. Doukidis because he raised his hand first. Let me clarify, dear judges, we can have up to two questions, okay, from the team. Okay, thank you. Congratulations. Based on the use of your system till now, can you report any productivity improvements or resource savings? This is tough. It's a tough question because you need people to actually be able to measure those and report back, and they need to already have this data available. I could throw around numbers, but reality is that this is still early days, especially for larger organizations. They have larger sales cycles. We started six months ago, so it's only the beginning still. I don't. We have anecdotal feedback that things have been much easier for them, but yeah, you have to take my word or their word for that. We don't have any data as of today for that. Okay. A big round of applause for Decode. Thank you, Michalis. Okay. Now, let's welcome our second team, Dry runZ, represented via video link by Yariv Erel, Founder and CEO. Your applause, please. I have five minutes, and I will try to make the best out of it. I will share my screen, and we can do it. Okay. What do we do at Dry runZ? It's actually very simple. We make people better. We believe AI is not going to replace us all, and for sure not in the upcoming years, but we believe that the opportunity or the real hidden gem opportunity in AI lies in making people better. Let's start with the team. I founded Dry runZ just a bit of like 18 months ago when GenAI started to pick up. Before that, I created a different startup by the name of justAd. I was co-founder and CEO when we sold it. Some of the people from justAd continue with me. Some are new. We are a small, nimble team from all over the world: U.S. people handling go-to-market and sales, people from Europe, Sweden. We just hired a temp in Greece, and we also have someone from Africa. We are really global in the things that we are looking. We are going to continue to be nimble. I believe any AI startup will be like that because we are all augmented. We are all using AI not just to develop it, but as part of our job, and I think it's part of the promise. Now, what are we trying to solve in the world? You have salespeople, right? Alpha Bank would have salespeople. You would have support people doing their job. Normally, big organizations will have quite a lot of people there. How do you make them better? To cut to the chase, I have five minutes. If you can bring a live coach to speak with them one-on-one, that would be great. They will understand what they're doing wrong, where did they struggle, and they can give them hands-on treatment for a long period of time. That's amazing. However, it's extremely cost-effective. It's very expensive, right, to put a coach for every person. Just to schedule all of that, it's an enormous cost. Many organizations worldwide, and we're seeing it on a daily basis, have amazing plans of how to make their sales teams better, how to save costs on support, but it gets stuck rolling into the market. Another good example is think about communication skills. Can you say to your manager, "Be empathetic," and they will be empathetic? Can you say to your salespeople, "Just ask questions. Make sure you ask more than you listen"? You can say it, but it will not happen. Why? Not because they are bad employees, just because it takes time, and time is money. The overall improvement of human beings at the workplace, of course, in sales, we also see it in support. We also work with healthcare institutions, which is less relevant for fintech and other industries, is huge. What do we do? We have a simulator, an AI role simulator. Instead of talking with your prospect or with an angry customer that cannot do something, you speak with an AI. You speak freely in Greek or in any other language, and it responds not like an AI would respond in a mature, coherent way, but as a customer. When you create a simulation, you can create a disoriented customer, and the customer would speak as a disoriented person or as an angry person, or I really love the passive-aggressive ones that always stick it to you, all of those guys. You speak, and people love it. The real thing, I do not have a lot of time, so I will focus on the main thing. It is the instant feedback. We give feedback to employees on something like 20 different metrics. How much time did you speak versus the customer? How many questions did you ask in that time zone? How many filler words like did you use? So many other things. A lot about communication skill, your empathy level, testing understanding, confidence level. All of those things are being reported back with suggestions on how to improve it. When you finish a Dry runZ simulation, you get missed opportunities, which is one of the features people love about us. They go in, they click missed opportunities, they see the transcript and what they can do better. We have abilities to help them create better sentences, etc., and they all do it on their own pace. If one salesperson is struggling with something like that, with a specific topic, they can get direct help on that. If some support person is struggling on a different topic, they will get their own support for that. Everyone learns on their own time based on their own tools and their own progress, making the promise of AI making people better a reality. Let's talk a bit about the challenge behind the scenes. When we created Dry runZ, the first decision, the tech team, of course, really pushed for creating our own LLM. At the time, it was very fancy. We thought about it. Eventually, we chose to work with off-the-shelf large models, and I think it was a very good and economic decision. The challenge, however, is to use those heavily trained models to act against their nature. You want the AI to be angry. You want the AI to deliberately misunderstand what you're trying to say to make sure that you do it. We do it all in live speech-to-speech. Speech-to-speech is a very new technology. It is less than 12 years in the market. We cannot use live prompts because it is live. The training that we can do for the model is very unique, and we can do more unique things in problem-solving, etc. That is really what is making our company successful from the tech point of view, where we are able to harness all the great features of the large models and really apply them for the simulation. Some results. What do you get if you use Dry runZ? You close more deals. I think it is very simple. If you have a large sales team and you work with us, you can do better objection handling, better rapport building across the teams. You make more calls for sales calls each time, more calls for customer rep each time. It is a huge dollar saving. We decrease. We decrease the tendency to rely only on live coaches. Live coaches are still part of the puzzle, but they are not the Achilles' heel. They are not the main—you do not need to schedule with this person and that person. You are on vacation. They are on vacation. It never ends. Live costs, training costs, which are huge, are out. All of the organization and travel time. You do not need to travel to the main office to get training. You can do it in your own place at your own time where you have the time to train about what you need to train. This is what we do. We offer the technology. We empower the team within the bank or any other organization to create their own simulation on their own. We focus on creating the best tech, the easiest to use, the scalable, and of course, the data part. That is it. We have multiple customers today. We have launched something like eight months ago. We are already working across multiple industries from multiple hospitals, credit card companies, food service companies, and more. If you would tell me six months ago that we were being in this place, even presenting to you guys, I would not believe it. It is an amazing rise, and I am very happy to be able to share it with you. This is everything I can have to say in five minutes, and now I can answer any question that you have. Okay. Congratulations, Yariv. Thank you. Thank you. I am so sorry I was not there. Again, I have so many friends there in the audience. Sorry. I am really sorry. Okay. Let's please take one question because time is up. Mr. Primpas, thank you. Thank you and congratulations for the idea and the product that you have developed. I have answered your question. You said that you are eight months in production. Two questions. How much time do you get to train this model for a client? How fast can they put it into production? Also, with this, you redefine actually the sales schools or the education process that they are doing in their company. Give us one concrete example from one industry that you have tried. What is the real return on investment that they did using this solution? Perfect. I'll try to be as transparent as possible. If it's a model that we know, like cold calling or selling something or an angry customer, you can create your own simulation in five minutes. If it's something new, we are now working on a new, more high-end knowledge process where, for example, for doctors, about making sure people, when they sell to doctors, they know a new scientific article, etc. There are differences between this and the other models. Each model is a bit different. We trained the model in the backstage for quite a long time, but when we reached production, for a user to launch a simulation, it is five minutes, maybe 10 minutes if you are first time, but it is easy, and you can change it each time. I will share with you a use case that is relevant for fintech for the second question. We are working with a large credit card company. You all know it and use it. Fortunately, I cannot use their name. They use that for two use cases. One of them, they sold a loan. I cannot say what type of a loan, but the loan was a bit troubling for the local authorities, and they wanted to make sure that when they sell this loan, they are not too pushy and they are not trying to really manipulate the users because the loan has multiple tricky parts, let's call it like this. They used our AI to certify their salespeople to make sure that the person that sells this new innovative, let's call it, loan, is not being pushy. They adhere to all the rules. We are talking about many people on multiple occasions. To do it manually or with the manager for each one would take months. It would be a very long and expensive process. With us, they did it in three weeks, maybe four weeks if I am counting also some early tries. Within four weeks, they trained hundreds of people in this way. If you did not pass your certificate, you can do it over and over until you passed it, and you get the feedback, and of course, you get better as part of it. That is an example of how you can scale training. In this case, it was certification. It was not training just to make sure that they followed the rules of that specific case. Thank you very much, Yariv. Thank you. Thank you. Thank you. Now, before we pass to the next company, I will say it on top. Please know that we have five minutes for every pitching and two minutes in total for the questions. Okay? Now we are ready to have with us here the third team, Nettle, represented by Richard Guga, Co-Founder and Executive Director. Richard, please. Hello. My name is Richard Guga, and I'm with Nettle. For the first time in history, humanity doesn't scale. In fact, every day, every single second, we're losing billions of dollars. It's not because of supply chain issues or interest rates. It's because of one simple, unsolvable human problem: latency, the gap between a need and the perfect human response. The banking industry also suffers the same kind of challenge: lack of availability of specialist resources, the reduced operational hours of the banks. This causes engagement friction and lack of personalization. When you couple this with the competition of online banking, only online banking, and the high cost of operations, it becomes a real challenge for the banking industry. What if we could scale humanity, our creativity, our intelligence, our empathy? Not replacing people, but extending them. At Nettle, we build digital human solutions that do exactly that. It shares the best of who we are: our knowledge, our values, our presence. Let me show you. Hi, I'm Laia, a digital persona created by Nettle. How can I help you? Oh, I'm interested in a loan. Thank you for your interest in a live personal loan. You can apply for a loan of up to AED 200,000 with flexible 10 years and a competitive interest rate, provided you meet the eligibility criteria, such as a minimum salary transfer of AED 5,000 for at least three months. Would you like more details on the application process or eligibility requirements? That is not just a demonstration. That is reality. Let me introduce you to Vesna. Vesna was the first digital human banking assistant ever deployed in Europe. She was deployed in 2021, and she is still active today. She's part of a larger solution that was deployed to the Slovak Savings Bank. It was an integrated solution that had a core conversational AI platform as the engine, and it was deployed to three different user channels. One was an online avatar that was used for support of campaigns and product launches. The other was a mailbot that was deployed and synchronized with the bank's Genesys contact center. It actually delivers 85% of all incoming emails successfully, so hugely successful for the bank. The third channel was the digital avatar. It was a 3D holographic image displayed at the bank in the branch, and it provided frequently asked questions and general guidance. It was also trained for transactional banking, meaning that it could block a stolen credit card for customers and also provide banking activities for that customer. Our recommendation is for Alpha Bank to use a similar kind of solution and gain the same kinds of benefits. We would recommend a more measured approach, one that builds trust over time. We would recommend that the avatar be deployed at the branch level as a meet-and-greet avatar, answering frequently asked questions, possibly scheduling appointments, and even providing general guidance. A phase two could be extending the capabilities of that avatar so that it would have specialized support for operations and guidance at the digital corner. Ultimately, we would be able to extend that avatar even further for a specialized training or onboarding avatar for customers and employees. We believe the benefits to Alpha Bank would be huge. The two most important ones would be by deploying this really modern and innovative customer engagement experience, the bank would skyrocket in terms of brand identification and availability in the marketplace. Most importantly, excuse me, most importantly, by deploying the solution you build once and deploy everywhere, meaning that not only are you maximizing the efficiency and the impact, but you're also maximizing the investment of the solution because you could deploy it to multiple channels and multiple branches at the same time, reducing costs as well. This should be the music to the ears of every banker in this room. A little bit about Nettle. We are five co-founders, three IT people, and two business people. It's the perfect blend of business acumen and IT expertise. Really pleased to be invited as strategic partners with some of the largest AI vendors in the global space: Google, Microsoft, NVIDIA. Also really proud of our customers who are not only testing, but they are actually transforming their engagement models using our technology. Finally, just an example of a couple of the platforms and form factors. Here we have Digital Holobox as a brand ambassador. We have a digital insurance agent or a broker on a mobile phone. We have a multilingual, multicultural avatar that maximizes inclusivity. My favorite here, a live digital avatar deployed to your TV. Adam was deployed during the World Hockey Championship in May. He was interviewed on a regular basis daily for over two weeks. It was a live avatar interacting with the live co-interviewers on the television screen. Very, very impressive as well. I would like to thank everyone, and I encourage and welcome Alpha Bank to join us on this innovation journey. Thank you very much. Thank you, Richard. Thank you. Congratulations. Now, let's start with a question. Thank you. Thank you very much for the presentation. A couple of questions from my side. How much is your product scalable cross-country? Because I am from Italy, UniCredit, so I would be interested to understand also if you have a plan in this perspective. Also consider that competition, you mentioned in the paper, chatbots and similar solutions is very, very high. What is the real advantage that you see vis-à-vis your peers? Sure, sure. We can provide support for over 42 languages. Our technology was actually built in 2018, 2019 before generative AI. We had to use very good knowledge and data scientists on our team to develop our natural language proprietary engine so we could actually integrate easily to the other languages in our region. We're LLM agnostic, so we can integrate with any kind of LLM. We have serious business domain expertise because we've been delivering to the finance space and the online consumer space. From a competition perspective, not very many people have that capability. Also, our digital avatar is also proprietary, very nicely synchronized and complementing our NLP engine. Most importantly, when it comes to interoperability and the innovation that we provide, nobody can do this. We have speech-to-text, which is pretty common, but we also have gesture recognition, eye tracking recognition. We have a browser-based rendering model that increases scalability tremendously. We have already got pilots that we are working on that are tens of thousands of avatars across different markets. We are also providing other capabilities in that space: emotional intelligence. When you bring that entire solution together, nobody can compete with us. We are really pleased about that. Thank you. Okay. I see we have very, very little time for a very quick question. What is the actual customer satisfaction beyond the element of surprise that the customer has facing an avatar? What are the actual gains in productivity and in financial terms for an institution that uses this AI avatar? Much like some of the other guests, I cannot share the actual numbers of some of our customers. Like I mentioned, the back office avatar actually successfully deployed or successfully answered 85% of all questions. Interestingly enough, Slovak Savings Bank, the largest bank in Slovakia, largest bank branch in Slovakia, largest bank in Slovakia, had the largest customer base, but it was considered a dinosaur when it came to technology. From an anecdotal perspective, when we deployed this, all of the other banks in our market and in our region started looking at this, and they wanted something. Within the year, they all had their own capable avatars that were either online, and some of them are actually experimenting with in-branch avatars as well. The feedback from the bank has been very positive. It's, like I said, increased the brand identity in the marketplace. Now Slovak Savings Bank is considered probably the top two innovators in the market space. From that perspective, it's great. As I mentioned, you can offset the capabilities of, and you can extend the capabilities of your team. Because of the lack of availability of specialists, you can actually have the avatar do the redundant, recurring work and have the specialists actually doing the work. You are saving time and money. There is a lot of churn in the banking industry as well. You are able to save money from that perspective. If you deploy this also as a training and onboarding tool, you would be able to actually, like I said, create one logic engine with your business domain knowledge and then actually deploy it to multiple channels. You could have multiple branches. You could have it in the back office training employees. You could have it in the front office onboarding clients. The potential is huge. Thank you very much. Thank you, Richard. Thank you. Okay. Let's welcome now Agrinow, represented by Korina Chatzigeorgiou. Korina? Hello everyone. I'm very happy to be here and introduce you to my world. From fruits to vegetables to olives, in order for them to get to our table, they need care from human hands. You know what? Farmers can't do everything alone. They need help. That's why they hire agricultural workers. Can you imagine what happens when farmers don't have the staff that they need? It's a disaster. In Greece, in one year, only from the olive oil farmers, we lost EUR 27 million. This is a problem reported in other countries as well, such as Italy, Germany, France, U.K., across various crops. You might be wondering why this happens. There are four different aspects. First of all, farmers still use outdated methods of finding workers, such as phone calls. Furthermore, because those workers are coming from different nationalities, communicating in a different language is challenging for them. Without proper communication and with different hiring paths that they have to follow based on the nationality, it is very hard to navigate what to do for the paperwork. As you can imagine, without proper paperwork, we have informal payments. How do we know all this? Because both me and the co-founder, Kyriakos, are coming from farming families. Those were not just numbers and facts for us. It is the reality that we are facing every day. We decided to do something about it. We did research, thousands of interviews, and we combined our educational and professional background across programming, economics, and agricultural engineering. Together with a team of nine, we are set to solve this problem in the simplest way as it was possible. I'm happy to introduce you to Agrinow, a conversational engineering AI system, a place where farmers can find, hire, and pay their staff, a place where workers can maximize their employment and feel safe. Let's see how it works. Everything starts from a chat. Farmers simply have to explain what they need, and the job listing is created. They are automatically matched with available vetted workers, and language is no longer a barrier because everything is translated and transcribed in real time. Paperwork is streamlined and easy to follow steps that everyone can do even if they have never done it before. Also, the AI collects, checks, and submits the document to the relevant public authorities when it is time to pay. Again, it is one tap away. Just like that, we made hiring as simple as chatting with a friend. Now AI can take care of the complexity, and users just have to follow a chat. So far, we have gathered feedback for over 75,000 unique MVP users. We received the first place at Panathēnea together with the support of Visa and Microsoft that supports our AI with the credits that they offered. We were featured in The Economist magazine, collaborated with the Greek Ministry of Agriculture, and funded in cash by Google. The market is huge. In the world, one out of four people work in agriculture. We focus on the European Union and especially in middle-aged people that use the internet, and particularly in the countries Germany, Italy, France. That is not it because according to this year's Future of Job Reports by the World Economic Forum, it is projected that the number one largest growing job for the next five years are going to be farm workers and laborers. This makes sense because as the population grows, so does our need for food, medicine, clothes, all of which come from farms. There is also another aspect. AI is silently replacing millions of jobs. While some people will be able to get reskilled, some of them will turn to occupations that it is harder for the AI to reach, which is laboring jobs. What about Greece? Farmers pay each year more than EUR 1.2 billion in wages. The systems supporting them are stuck in the past. The majority of the payments are in cash. Just like Uber, that totally transformed the taxi industry, that it was primarily cash, Agrinow is set to do the same thing and unify and streamline the entire hiring process, something that farmers and workers are eager to adopt. We are getting a fee for our services that includes the matchmaking and the paperwork, and also the payroll transition. We are here seeking a partnership with a financial institution or a bank in order to help people create bank accounts, to exchange remittances, and get access to microloans. With the right partner, we foresee having EUR 60 million of revenue in the next five years. What I want you to remember is that we're not solving only a laboring problem. We are building the infrastructure to bring banking services close to people that want to but can't access it. We are reducing inequalities among different nations. We are promoting decent employment on the fields, and we are ending the food waste that starts from the field. Because we are using AI to empower, not to eliminate our communities, we are backed already by 75,000 people and by major companies. We are here asking you for the FinQuest Prize because we help banks get to the beating heart of agriculture. We honestly believe that with your support, we can help thousands of people that at the end of the day bring the food to our tables. Thank you. Congratulations, Korina. Congratulations. Do we have a first question? Okay. Ms. Andronopoulou. Congratulations and the holistic approach. My question has to do with a specific segment. Usually, the workers and the farmers, they are not so digitally savvy. How do you plan to ensure adoption? Thank you so much for your question. Indeed, and that is why AI fits in, because it can simplify complicated workflows. It feels almost like talking with a human. That is our competitive edge. So far, we have much more than 1,500 job placements, which looks like we are on the right path to do something extraordinary. Thank you. Okay. Thank you. We have time for a second question. Thank you. Mr. Tzamtzis, You presented congratulations. First, you presented yourself and your partner in building this excellent application. However, which is the team that supports that? Thank you for your question. We are a team of nine, actually. We have the background in economics and agricultural engineering, but we also have a team that helps in product, UI/UX design, legal, payments, etc. We offer a holistic approach to this problem. Okay. Thank you so much for your time. Thank you. Thank you, Korina. Thank you. Congratulations. Okay. Now let's move on with Travelr. Yannis Dinapoyias will come on stage now. Thank you. Hi everyone. My name is Yannis Dinapoyias. I'm the Founder and CEO of Travelr. The hyperlocal infrastructure for business travel, expenses, and benefits powered by AI. I will tell you a little bit about our backstory and how we are here today. Starting from my childhood, I grew up in a family, in a travel family, basically, a family-owned traditional travel agency based in Athens that was founded in 1974. My experience was there since I was a little kid. My road took me to the U.S. to study economics and finance in Chicago. On my way back here, actually, home was calling me back. I came here thinking, business travel is a $3 trillion business, a $3 trillion global business, and it's working on fragmented systems. I was crazy enough to say the challenge accepted. I was lucky enough to find this amazing team. Actually, out of all of them, only [Eleni] is here, but we pulled together decades of experience in travel and tech. We've all been basically traveling for life because of our different backgrounds and different works before joining altogether the team. We've been rocking it, according to us and according to some competitions, domestic competitions and international ones as well. We've been certified by all the needed bodies to act as an infrastructure layer for travel. We've been doing it since the beginning of last year. Actually, in less than a year, we've achieved EUR 1 million in GMV in sales, and we have projected a confirmed sales pipeline for next year of north of EUR 12 million. We are still having 11 months ahead for sales. Let's dig deeper into the business model and our vision, as well as our mission. Business travel is begging for optimization. It's a global problem that touches us all. We've all been needing to travel for work, also travel for leisure. The problem is when traveling for work, lots of people in our organizations need to mingle in the process. We need to research the travel options, then get approvals for it, as well as pass by budget controls. Usually, prices change using the old way, the traditional way, the ways of emails and calls. At the same time, it's super frustrating. It's a complex thing that involves different providers, different vendors, as well as suppliers. It's very costly. You need to outsource it to an agency, which means that we need to pay for fees or do it on our own, which is hours and hours and hours, pre and post-trip. At the same time, it's a huge opportunity because out of hundreds of thousands of SMEs and mid-market companies in our area of Mediterranean and Southeastern Europe, three out of four people are still using calls and emails to manage travel internally in the organization until we were born. We're presenting Travelr, the super app with zero friction in travel, in managing travel expenses and benefits all in one travel ecosystem. We unite business travel, corporate expenses, and employee benefits into one single unified environment. We do it in style. It is the first AI-powered travel expense and benefits super app in SME in Southeastern Europe and Mediterranean, reducing costs, saving time, and automating ESG reporting for the whole organization. We are source agnostic. Basically, we function as the infrastructure on which the whole ecosystem and the whole supply chain of travel works on. Hotel chains, airlines, ferries, transfer providers, you name it. We are providing the platform on which the value chain of travel builds on. We have integrated roles, reporting, and we are super user-friendly if an organization chooses to use us via our UI. Our expertise and our specialty does not end there. We have some screens of the UI that you can see how simple it is to search something or manage bookings if you are a travel manager, and of course, make sure that everything happens on budget. We integrate with different third-party platforms as well. We need to be working together with ERPs and HR platforms that organizations are using already. We are having a two-way sync capability with those. The business model is very simple. We are acting, we're positioning ourselves as the infrastructure layer for suppliers, international suppliers, and partners such as Alpha Bank, as well as different partners on the supply side. We meet them, we merge them together with end users, meaning people from different sizes of organizations, from SMEs up to mid-market, as well as enterprise. How do we reach them? Of course, that's why we are here. We reach them via the AI agents that they can work on. If people are working on Slack or Teams or Microsoft Copilot, they are using our APIs to serve travel content. How do we do that? By acting as the brain. Business, benefits, and expense, all is embedded by design into the Travelr brain. We are basically converging the brain for approvals, the brain for HR and expense into one embeddable AI brain that is being served to the companies via the AI agent of their choice. We combine global B2B rails, so global products, syncing different providers from all around the world, as well as our hyperlocal service network in Greece, as well as the Mediterranean, in ferries, etc. Superstars with Travel Copilot AI, which is the backbone of our solution, is that we are source agnostic, meaning that we can serve our travel content via all different AIs out there. Competition is traditional travel agencies, but we are inviting also travel agencies via our white-label solutions as well. We are also enterprise. We're on the other side of enterprise solutions like SAP Concur, Egencia, BCD, and TravelPerk because we are the only solution in the market with AI-first design serving banks, fintechs, as well as corporates of all sizes. We have done 80 customers are already channeling their travel buying through us in Greece, as well as abroad. We are a deal amount of north of EUR 12 million channeling it next year. Our revenues are coming from three sides. Supplier-side revenues, commissions based on the people, on the companies that are participating in our ecosystem from the supply side, meaning airlines, hotels, etc. We have a subscription model for clients and companies, as well as a payments rail coming from interchange. Market is huge. We have shown that already in this current year, in a few months, we've channeled north of EUR 1 million in GMV, having confirmed north of EUR 12 million for next year. Our mission is high, and our calling is huge to be answering to a $3 billion market by 2030, achieving $1 billion in sales by 2030. I would like to thank you for your attention. Sorry for my stress. Thank you. We're building for travel. We're building for Greece. Thank you so much. Thank you. Now two minutes for questions. Thank you. Two quick questions. The first is my impression is this is a very crowded market globally. You said something about Mediterranean. You need to explain to me why this needs to be local. There are companies like, obviously, SAP Concur or Ramp or Expensify. There are tons of companies in this space. That's the first question. Second question is when you say EUR 12 million GMV, this is total merchandise that goes through your system. I mean, what is your margin on that? I would expect very, very little. That's the second question you have to wait for until I answer the first one. Yes, actually, it's a very competitive market. It's actually why our team is so passionate about working on this problem. It's a global problem, and lots of companies from different angles are trying to answer it. Expense platforms is one. Ramp, like you mentioned, is one of the expense platforms that are Expensify or Payhawk. There's all these different even Revolut businesses doing that. It's actually trying to answer how to manage expenses for business, categorizing them, auditing them. What they're missing is content. What they're missing is, most of them, the brain behind the transactions. We are positioning ourselves in front of them, actually, sorry, behind them, serving them with travel content globally. Ramp is actually pulling from travel vendors. Payhawk as well. We are in the position of serving that content and the brain that each company has of who is working in which department, who is approving whom, who is checking whom, who is the auditor of the travel budget of each department. That is information that has to live in a tied-up manner in one platform. That is what we are doing. Okay? Yes. Second question is, of course, profitability. Because it has been a bootstrap company since the beginning, since our birth. We are very focused on being lean and having a really rock-solid unit economics strategy. Actually, we have managed to be self-funded and client-funded in some ways of changing the model from a credit facility, which is the norm in business travel, to prepayment. We have been able to do that because of our value proposition and, of course, because of our competitiveness. We are keeping our margins low. It is a volume business for us. We want to be the alternative for hotels and travel providers to big OTAs. That is why when we are onboarding a hotel chain or we are onboarding an airline, we do not take a big chunk out of their selling rate as Booking.com does, for example, which is something that they are very angry about. We go in a more fair model. Thank you. Thank you. Thank you. I believe we ran out of time. Thank you very much. Congratulations. Okay. Let us have myTeam represented by Dimitris Sereleas. Dimitris, the stage is yours. Hello everyone. I'm really happy to be here. My name is Dimitris Sereleas, and I'm the founder of myTeam. myTeam is an ecosystem that helps sports clubs and academies to optimize their daily operations. Either this has to do with registration of athletes, either this has to do with communication with parents, coaches, and the whole other stakeholders, or this has to do with money collection, which is one of their main problems. Some things about our traction. Until now, we have a bit more than 400 clubs that work and use our main application, myTeam. Among them, we have Panathinaikos F. C., Olympiacos. We have Eurohoops Ac ademies of Thodoris Papaloukas. We have 160,000 profiles within the app and 100 different families. We have EUR 20 million of GMV happening within the platform, and we managed last season to convert EUR 1 million of this amount of subscription paid toward the clubs in online payments through our marketplace system. One other metric that is important for our business is the number of page views that we have on our mobile app, which is about 2.5 million per month. It is similar to a mid-size news portal. Our team, the founding team, is George Malamatlis and myself. We have worked with George more than 10 years together. We have worked in several tech business projects, either with startups or with large corporations, even with banks. We have been funded from VCs in Greece and abroad. We have raised until now EUR 1.6 million. In our cap table, we also have Lars Rasmussen, who is the co-founder of Google Maps, sorry, which is the problem that we identified. There is a huge market. Only in Greece and Cyprus, we have more than 12,000 potential clients, including academies, sports clubs, dance studios, pilates, and stuff like that. All these organizations use outdated systems to manage their daily operations. They use not only outdated systems, but they do not interconnect these things together. They need to have one Excel sheet for the registration and another one for the payments. They need to use Viber to communicate with the parents or the athletes. I think that you get the point. What we decided to do, we decided to build an ecosystem where myTeam application is the operating system. It's where every detail goes, and it's spread among other applications and things where we share knowledge and data. Where is the innovation and AI edge in our company? We use AI internally, of course. Our sales rep is an AI-built system that sends more than 500 cold emails every day to potential clients, while we also have synthetic AI editors who create customized content for our users, either it's an athlete, a parent, or a coach or a sports manager. We also use AI to create product value, like we do with the AI Copilot that helps within the myTeam app the users of the sports clubs to optimize their daily operations and create useful reports, to talk to the AI agent and create trainings, create subscriptions, get reminders about payments, and having an assistant that they would otherwise pay even EUR 500 per month. We also have SportsLab. SportsLab is a second application we created in our ecosystem that is mostly focused on the athletic development of the kids that use myTeam. It is sharing details based on their performance, analyzing them, and then provides tips and tricks on how to develop their skills where it is needed. Last, sorry, another application that is under development right now is CoachLab, where we help coaches to optimize trainings. We do that by creating smart coaching plans where they can add the number of athletes and the number of people that they are going to have in the training, the equipment, and what they want to work on. They create the custom plan, as you see. Last but not least is the myTeam Hub, which is a hub that we create customized content based on the user, sport, age, type, different for athletes, different for coaches or sports managers. It creates daily articles that we share through our platform. Thank you very much. Thank you, myTeam. Let's have our first question. Since I am a football fan, I believe I'm a fan of yours also. However, at the end, in which way do you believe that cooperation with a bank will amplify your idea and your initiative? Yeah. There are many ways that we can work with a bank. The most obvious is the payment gateway. We already manage more than EUR 1 million in online payments, and we expect this number to grow even more. We have seen opportunities like buy now, pay later. When a parent goes to pay for a camp, he might have to pay upfront even EUR 1,000. We see even opportunities with something like a factoring system. When you have to organize a camp as a sports academy, you need money upfront. There are huge numbers of opportunities for myTeam to make a pilot with a bank. Okay. Thank you. We still have time for one more question. I'm using your app, actually. It was really interesting in terms of organizing when it comes to young children, especially getting on board on the team. Apart from the organization and the logistics, I don't understand what is your value proposition in order to continue and make this bigger with regards to all the content that you shared. What is your vision on the next day? Okay. Our vision is not to sell just the initial product, which was a software as a service and was serving the sports clubs. Our vision is to become an ecosystem of interconnected applications that will provide information to myTeam and will help the whole ecosystem of the sports community, parents, athletes, everyone involved, to be easily connected, to find opportunities and easily manage their days. We want to help more kids to be active and to spend time in the sports field instead of their mobile phones and tablets. In order to achieve this, we need to help these organizations to become sustainable. Okay. Thank you very much. Thank you, Ms. Sokou. Thank you, Dimitris. Now, our last team for today e Frontiers, represented by David Giron, the Co-Founder. David. Right. Hello, everybody. My name is David Giron. I am the CEO and Co-Founder of eFrontiers. I believe Mr. Psaltis said it best. With all this AI, banks need to innovate. We need to predict customer demands. How can we do that if we do not understand the customer? That is exactly what eFrontiers is doing. Before I dive in, I want to highlight one of the biggest issues in the industry today. Today, over 67% of the world's population is financially illiterate. On the other side, financial institutions are spending over $90 billion every year trying to promote products that people do not understand. We have a massive disconnect. People do not understand their financial options, and financial institutions do not understand how to better reach them and educate them. The key is in education. We created a gamified learning platform that makes it easy and fun to learn about money. You can think about it as a Duolingo of finance that is white-labeled by financial institutions to promote financial education and boost customer acquisition. It follows a progressive learning journey where users will go on lock-in levels, starting with the basics, going to more complex topics. Everything is built in a modular micro-learning methodology so that users can learn at their own pace and at their own time. We have gamified the entire experience so that as users are learning, they're collecting trophies, achievements, and they collect points that allow them to compete in real competitions that offer real rewards sponsored by the financial institutions, like credit card points, loyalty programs like the bonus, for example, or even books. Let's look at a real case study to understand how this works. We partnered with a large financial institution that was struggling with high customer acquisition costs and, of course, struggling with engagement. The first thing they did is they selected features they want to include into their platform. We then built a curriculum that is tailored to their market and built around their financial products. There, our AI engine content allows us to create all the content optimized for our platform. Even better, we can leverage any of your existing materials, blogs, articles, videos to train the model and put that content into the platform. In less than two months, we can be live. Now, we offer this as a standalone solution. It can also be embedded. What we've seen is that there are two main reasons to have it as a standalone. First, there's no tech integration needed, so it allows it to be fast and simple. Most importantly, for new users to avoid friction, if it is a standalone, people do not have to download another banking app from another competitor to start learning about finance. What we've learned is that our partners, by promoting our platform, have seen up to 20 times better performance in their marketing efforts. The best part is that once a user is learning about finance, they also start learning organically about the products of our partners. The real magic is what happens in the background. Our platform is learning about users, studying over 100 different data points to understand better their goals, their needs, and their future requests. It's not just about new customers. It's also about your existing ones. What we have seen time and over again is that most clients have a checking and a savings account, and they do not really understand any of the other products that you offer. Through education, we can help them find the right product for their financial journey. What we get is this result. An unfair advantage. While your competitors need to have market research, generic personas, we have a real-time data layer that allows you to understand your audience. For example, what is the demographic? What assets are they interested in? What are the goals? What is the risk appetite? We take it a step beyond, and we actually narrow it down more granular to each user, understanding each user so that we can serve them with the content that they need so that they can improve their financial journey. We have a straightforward business model, which has a licensing fee on a monthly basis and a performance fee based on the number of users and conversions. Today, we have over 100,000 users across platforms. We have tripled our ARR over the last eight months, and we're now working with three financial institutions. The best part is it's not just about business, but we're helping our partners achieve their social impact goals. My co-founder, Javier Chan, is a two-time EdTech entrepreneur. He was building business simulations for institutions like Yale and MIT. My background is in wealth management and venture capital. Today, we are also backed up by two venture capital firms that are helping us with our growth. Today, we're here because we want to connect with more financial institutions who might want to learn and use financial education as the next big marketing tool. Thank you. Okay. Congrats, David. Okay. First question. It's very clear what you do, and of course, you have good success. My question is a bit different. Can you develop a system that actually can be used for training kids? Great question. Because in Greece, we have a problem with financial literacy at that age. Great question. That is exactly what we do with our partners. What we do is try to understand what audience they want to target. In some cases, they can say, "Well, we want an academy for the younger generation and one for the older generation." What we will do is that our AI will tweak it so they will make easier content. You start something like, "Johnny has two apples," and so on, right? For the older generations, we can use more difficult content. Yes, we can tailor it. Okay. I see we have time for one more question. Okay. Congratulations for the application. The business case is clear. What is your ambition for the future? What is your goal for the next three years? What do you want to achieve? Awesome. Today, we hit 100,000 users. We're very lucky. This has been an amazing year. I think next year, we're talking to some of the leading institutions in Europe. I want to take this worldwide. I'm actually originally from Guatemala, and I built this with the ambition that we can actually let anybody in the world learn about finance. If we really want to get people out of poverty and from difficult situations, donations are only like a tape. It doesn't really solve the problem. We need to give them the knowledge so that they can make better decisions and have a better future. For me, it's to get to millions of people, especially in those countries that need it the most. Okay. Thank you very much, David. Thank you. A big round of applause for all our finalists. Our judges will now deliberate to decide this year's top three winners, and we will take a short 10-minute break. Get ready, because the best is yet to come.