On the occasion of his interview with Days of Art in Greece, Achilleas Zapranis, Director of the Distance Learning MSc Program “Financial Technology (Fintech)” and Professor of Finance & Neural Systems at the University of Macedonia, outlines the present and future of a field that is transforming financial services for the digital age.

In the discussion that follows, Mr. Zapranis explains the philosophy behind the program, his thirty-year research journey from neural networks to strategic artificial intelligence, and the challenges posed by digital financial innovation for Greece, the Balkans, and developing economies. Furthermore, he takes a critical stance toward overly optimistic narratives about cryptocurrencies and AI, emphasizing that technology does not eliminate fundamental economic constraints, while highlighting the need for financial literacy and human oversight.

Days of Art in Greece: Mr. Zapranis, you are the director of the distance learning MSc program in “Financial Technology (Fintech).” Please explain the term “financial technology” to us. Who is this master’s program intended for? What are the opportunities in the job market as well as for a potential academic career?

Achilles Zapranis: The term “financial technology,” or FinTech, is often misunderstood. It doesn’t simply mean that we’re using more computers in banks. It means that technology is changing the very way financial services are produced and delivered.

Think about how much has changed in just a few years. Payments are now made via cell phone, investment decisions are supported by algorithms and artificial intelligence, blockchain has enabled the creation of digital assets and decentralized financial services, and even money is beginning to take on new digital forms. Consequently, FinTech sits precisely at the intersection of three worlds: finance, technology, and the regulatory framework.

If I had to sum it up in one sentence, I would say that FinTech isn’t just another sector of the financial industry. It’s the way the financial industry is being redesigned for the digital age.

As for the job market, I believe that this interdisciplinary approach is precisely the major advantage. Banks are transforming into technology organizations, FinTech companies are emerging, and insurance and investment firms are increasingly using data and artificial intelligence, while new needs are emerging in risk management, blockchain, digital assets, payments, compliance, and regulation. So we’re not talking about a new profession called “FinTech.” We’re talking about a new set of skills that is becoming essential across a very large part of the financial sector.

Of course, there is also the academic dimension. FinTech has evolved into an extremely active interdisciplinary field of research. Artificial intelligence in financial markets, blockchain and DeFi, crypto-assets, central bank digital currencies, as well as issues of risk and governance, are currently raising very important research questions. A strong graduate can therefore pursue doctoral studies and a research career.

If I had to sum it up in one sentence, I would say that FinTech is not just another branch of finance. It is the way in which finance is being redesigned for the digital age.

D.A.: You are a professor of Finance and Neural Systems in the Department of Accounting and Finance at the University of Macedonia. Please explain what the term “Neural Systems” means and what the focus of the lab you direct is.

A.Z.: The term “Neural Systems” comes from a field of research I’ve been involved in for more than thirty years. When I began working with neural networks in the early 1990s, artificial intelligence certainly did not have the same level of public attention it does today. However, it was already an extremely interesting field of science.

Artificial neural systems are computational models inspired, at a very abstract level, by the way biological neural systems are organized. The major difference between them and conventional computational models is that we do not simply provide them with a set of rules to follow. They are trained on data, and through this process, they can recognize complex, nonlinear relationships.

In my case, my interest was financial from the very beginning. I was interested in using these models to identify nonlinear relationships in financial markets, to make forecasts, and to allocate capital. This line of research began during my time at University College London and the London Business School and became one of the main pillars of my subsequent academic work.

Today we have incomparably more powerful artificial intelligence systems… But the fundamental scientific question remains: What has the system actually learned, and to what extent can what it has learned be generalized beyond the data on which it was trained?

The really difficult question, however, was not whether a neural system could learn. We knew it could be trained and adapt to the data. The difficult question was what exactly it had learned.

Had it discovered the true relationship linking the variables, or had it learned some other relationship that simply described that particular sample well? Had it identified the true signal, or had it also learned the noise? And when different neural models could exhibit similar predictive performance, how could we know which of them had best approximated the actual mechanism that generated the data?

This is a fundamental problem that remains highly relevant today. Because today we have incomparably more powerful artificial intelligence systems, massive amounts of data, and computational power that would have been unimaginable thirty years ago. But the fundamental scientific question has not gone away: what has the system actually learned, and to what extent can what it has learned be generalized beyond the data on which it was trained?

And this is precisely where my earlier research on neural systems intersects with my current interest in the governance of artificial intelligence. To use a system responsibly, it’s not enough to know that it performs well. You need to know what it knows, what it doesn’t know, and, most importantly, when you shouldn’t trust it.

This development is also reflected in the lab I direct, the Financial Technology & Strategic Artificial Intelligence Laboratory (FTSAI Lab). The lab is situated at the intersection of financial technology, artificial intelligence, risk, and governance. We are interested not only in what artificial intelligence can do, but also in how it can be integrated into real-world organizations and systems in a reliable and controlled manner.

Our research ranges from FinTech, blockchain, and decentralized financial systems to artificial intelligence, risk management, and AI governance. We are particularly concerned with how to assess an organization’s readiness to use artificial intelligence, how to manage the risks of algorithmic decisions, and how to establish mechanisms for accountability and human oversight. These issues are now a central part of my research.

Therefore, if one looks at this progression from a distance, there is much greater continuity than might appear at first glance. Thirty years ago, we were trying to understand what a neural system had actually learned. Today, now that these systems have become incomparably more powerful and are involved in real-world decision-making, we also need to know how much we can trust them.

For me, this is the natural path from neural systems to today’s artificial intelligence and, from there, to its governance.

D.A.: Your academic career in the field of research has spanned more than three decades. Could you explain to us what your research focus was back then and what it is now? There is a widespread belief that in the coming years, anyone will be able to make money by investing online. What is the reality? What is the critical threshold for the parallel growth of financial investment and global production that could actually ensure financial returns for a broader public—that is, a form of income redistribution through financial markets?

A.Z.: As I mentioned earlier, my research journey began with the study of neural networks and their application to financial markets and has since evolved to address broader issues of artificial intelligence, risk, and governance. There is, however, one interesting constant throughout this entire period. Technology is constantly changing the tools at our disposal, but it does not eliminate the fundamental economic constraints.

In the long run, financial wealth cannot grow indefinitely independently of the real economy.

However, we must not confuse market access with the ability to achieve systematic outperformance. Simply put, the fact that we can all invest now does not mean that we can all consistently outperform everyone else. If everyone uses similar information and increasingly powerful computational tools, competition does not disappear; on the contrary, it becomes more intense. And in financial markets, higher expected returns are, as a rule, accompanied by greater risk.

Artificial intelligence does not change this fundamental relationship. It can provide us with better analytical tools, process enormous amounts of information, and reduce the cost of certain investment services. But if a tool that offers a real investment advantage becomes available to everyone, that advantage tends to diminish because the information is factored into prices. This is part of how markets function.

But there is an even deeper point. In the long run, financial wealth cannot grow indefinitely independently of the real economy.

Stocks, for example, are ultimately claims on a company’s future profits. And those profits depend on output, productivity, innovation, and actual economic activity. Of course, over long periods of time, the prices of financial assets may rise faster than output. But we cannot create lasting prosperity simply by raising asset prices.

We cannot all become wealthier by buying each other’s assets at ever-higher prices. Ultimately, someone has to produce more goods, more services, and more real value.

That is why I would be cautious about the term “income redistribution through the markets.” Capital markets were not designed as a mechanism for social redistribution. Their primary economic role is to channel savings toward investments, price risk, and finance productive activity.

However, they can contribute to something I consider very important: broadening the ownership of capital. If more people can, at low cost and with adequate financial literacy, participate over the long term in diversified portfolios of productive enterprises, then a larger portion of society can share in the value growth generated by the real economy.

And here, in my opinion, lies the truly interesting social dimension of financial technology. Not in the promise that we’ll all get rich by trading on our cell phones, but in the possibility that many more people will gain access to savings, investment, and ultimately ownership of productive capital.

However, this requires three things: real economic growth, broad access to capital ownership, and financial literacy. Technology can significantly reduce barriers to access. It cannot eliminate either risk or the laws of economics.

Technology can give all of us access to investments. It cannot guarantee that we will all earn more than others. However, it can enable far more people to participate in the actual creation of wealth over the long term.

D.A.: You are actively participating in the public dialogue on artificial intelligence, blockchain, and the regulation of the new financial system. We’d like you to tell us to what extent a country with a technological deficit in financial sector management can fall behind in terms of growth as well as key competitiveness indicators. Where does our country stand in this regard? How important is this technological convergence considered to be for the development of the developing world?

A.Z.: Today, the technological capability of a financial system is a factor in national competitiveness. It’s not just about whether a bank has a better mobile app. It’s about an economy’s ability to conduct cheaper and more secure transactions, leverage data, allocate capital more effectively, finance innovative businesses, and manage risk better.

A country that lags behind in these areas pays a kind of technological tax. Its transactions are more expensive, its businesses are less productive, access to financing is more difficult, and its economic system is less attractive to investors and human capital.

And now there is a second dimension: that of artificial intelligence. The more credit assessment, risk management, fraud detection, investment services, and regulatory compliance rely on data and algorithms, the greater the gap will become between economies that can leverage these technologies and those that merely import them as off-the-shelf services.

Greece currently finds itself in a transitional phase. It has made significant progress. 5G deployment is strong, fiber-optic networks are expanding, public digital services have improved significantly, and a robust infrastructure is now being built around artificial intelligence, such as the Greek AI Factory “PHAROS,” with the national supercomputer DAEDALUS at its computational core. This is a significant development because, for the first time, a domestic computing infrastructure is being created that can support research centers, universities, and startups in developing advanced artificial intelligence applications.

On the other hand, we must not confuse the digitization of the government with the overall digital transformation of the economy. The adoption of advanced digital technologies by Greek businesses, particularly small and medium-sized enterprises, continues to lag behind, while the country faces a significant shortage of ICT specialists and digital skills. The European Commission itself considers these factors to be obstacles to productivity and competitiveness.

In the 20th century, the economic gap between countries was measured primarily in terms of capital and industrial infrastructure. In the 21st century, it will be measured increasingly in terms of data, computing power, human capital, and the ability to govern technology.

So the crucial issue for Greece is no longer whether it will go digital. It is whether it will move from using technology to building technological capacity. It’s one thing to buy apps, but quite another to have the people, companies, and research centers capable of creating algorithms, platforms, financial products, and artificial intelligence systems.

When it comes to developing economies—a term I prefer to “Third World”—the significance of technological convergence is perhaps even greater. This is because digital financial technology has the potential to allow a country to bypass entire stages of infrastructure development.

That is why I believe that what really matters is not just access to technology, but technological sovereignty and capability: human capital, education, infrastructure, institutions, and good governance. And this applies equally to Greece.

In the 20th century, the economic gap between countries was measured primarily in terms of capital and industrial infrastructure. In the 21st century, it will be measured increasingly in terms of data, computing power, human capital, and the ability to govern technology.

D.A.: You have personally served as Rector and Vice Rector for Financial Planning and Development at the University of Macedonia and have an extensive international track record. What do you consider to be the role of your university in the international outreach of the country as well as the broader region of Northern Greece, given its distinct central role in trans-Balkan cooperation and its pivotal role in the country’s relations with Asia and the Far East? What could be done to accelerate the pace of growth and strengthen the presence of the Region of Northern Greece as a conduit for ideas, goods, and people between Europe and the East?

A.Z.: The University of Macedonia has a unique advantage stemming from its location, its history, and its academic profile. It is located in Thessaloniki, a city that has traditionally served as a crossroads between the Balkans, the Eastern Mediterranean, and the broader European hinterland. This means that the University should not view its role solely in national terms. It must function as a regional hub for knowledge, collaboration, and human capital.

When we talk about internationalization, we don’t just mean more Erasmus programs or more international agreements. We mean something deeper, such as, for example, international research networks, joint degree programs, attracting foreign students and researchers, collaborating with businesses and institutions outside Greece, and, above all, creating an academic identity that is recognizable beyond our borders.

In Northern Greece, there is the potential to create such an ecosystem, precisely because it brings together universities, research centers, business activity, a port, energy and transportation infrastructure, and a geographical proximity to the Balkans.

If we can bring together universities, businesses, regions, and international partners under a unified strategy, then Northern Greece can move beyond simply serving as a transit region and evolve into a region that fosters connections and creates value.

The key, however, is to move from a mindset of isolated actions to one of a systematic regional strategy. It is not enough for each university, each region, or each organization to engage in collaboration on its own. Coordination, shared goals, and continuity are needed.

In my opinion, Northern Greece can strengthen its role on three levels.

With regard specifically to Asia and the Far East, I believe there is significant room for much more systematic academic and business relationships. There, we must not rely solely on general cooperation agreements, but rather on targeted joint programs, research projects, summer schools, joint degrees, and partnerships with technology and financial centers.

If we succeed in connecting universities, businesses, regions, and international partners within a unified strategy, then Northern Greece can cease to function merely as a transit region and evolve into a region of interconnection and value creation.

In my opinion, this is what really matters: not just having goods and people pass through Northern Greece, but generating ideas, knowledge, technology, and partnerships here that have an international reach.

Geography provides an opportunity. It does not, in and of itself, confer an advantage. An advantage is created when geography is combined with knowledge, infrastructure, institutions, and international networks.

D.A.: In your opinion, tell us how you see the future of young people. What will the structure of the emerging economy look like, taking into account the role that cryptocurrencies and the various online financial possibilities brought about by AI will play? Could we potentially see greater economic justice and ensure that financial resources are made available to more vulnerable population groups based on social criteria? Which of your personal dreams have you seen come true? Recommend a work of art—a book, a movie, etc.—that explains, in artistic terms, what you see and would like to share with us.

A.Z.: I am optimistic about the future of young people, but not naive. I believe that the next generation will have opportunities at its disposal that our generation could not even imagine. At the same time, it will live in a world that is far less predictable.

Artificial intelligence will transform work, education, entrepreneurship, and ultimately the way economic value is produced and distributed. Blockchain and tokenization can create new forms of ownership and transactions. Digital financial systems can drastically reduce the cost of accessing services that were once available only to large organizations or people with significant capital.

However, I do not believe that cryptocurrencies or artificial intelligence, in and of themselves, constitute a mechanism for economic justice. Technology has no social agenda. It can expand access, but it can also concentrate wealth and power in even fewer hands.

That is why I believe that the major issue of the coming decades will not be merely what technology we can create, but how we will govern the technology we create.

There are indeed new opportunities for financial inclusion. A person who lives far from a bank branch can already access financial services via a cell phone. Small businesses can access financing in new ways. Artificial intelligence can dramatically reduce the cost of providing specialized services, from education to financial information.

But we must be very careful when moving from economic inclusion to economic redistribution. We do not create social justice simply by distributing more digital financial products. We need productivity, education, institutions, opportunities to participate in economic activity, and social policies where they are needed.

Greece currently finds itself in a transitional phase. It has made significant progress. The rollout of 5G is strong…for the first time, a domestic computing infrastructure is being established.

So I’m not so much afraid that artificial intelligence will replace young people. I’m more afraid that some young people will stop developing their own abilities because they’ll be able to delegate more and more to machines.

You also asked me about my personal dreams. When I look back, I feel that I’ve been fortunate enough to see quite a few of them come true. To become a university professor, to conduct research in a field that truly interested me, to see my work gain international recognition, to serve my university in positions of responsibility, and yet to continue, even after more than thirty years, to discover new scientific questions that fascinate me. This academic and institutional journey indeed encompasses more than three decades of research, international scholarly output, and terms as Rector and Vice Rector, and now as a member of the Governing Council.

But perhaps as one grows older, the definition of a dream changes as well. I’m now less interested in what else I can add to my résumé and more interested in what I can leave behind that will continue to have value when I’m no longer here. People I’ve helped grow, ideas, books, research tools, and institutions that have become a little better.

And since you’re asking me to choose a work of art, I’ll turn to music. I would suggest Franz Schubert’s String Quartet No. 14, “Death and the Maiden.” It is not, of course, a work about technology. It’s a piece about something much more important: the human condition. It contains order and a rigorous structure, but at the same time, tension, anxiety, beauty, and the awareness that our time is finite. And perhaps that’s why it moves me so deeply.

If I wanted to translate something from this line of thought into painting, I would turn to Jackson Pollock. At first glance, you see chaos. But if you stand in front of the painting, you begin to discover rhythm, density, relationships—a form of order that isn’t imposed on you from the start, but emerges from the complexity.

And then there’s the Bauhaus. There, order doesn’t emerge—it’s designed. Clarity, structure, function, and discipline of form.

Schubert, Pollock, and the Bauhaus are three very different worlds. But perhaps they express three things that I, too, seek in the way I view science and technology: the human dimension, the freedom of creation, and the need for structure.

We need Pollock’s creative freedom and complexity. We need the Bauhaus’s structure and discipline. But at the center, we must preserve what Schubert reminds us of: humanity.

Because, ultimately, progress cannot be measured solely by how intelligent our machines have become. It must also be measured by whether the world we have created with them has become a better place for people.