Q&A: Hrvoje Smolic on how no-code AI could be a game-changer for the fintech industry

The integration of no-code AI into fintech has emerged as a significant catalyst for innovation and transformation within the financial services sector. This technology is not only streamlining data processes but also making advanced analytics accessible to a wider audience, reshaping traditional business models in the process.

During Dublin Tech Summit in May, Bobsguide had the opportunity to sit down with Hrvoje Smolic, Founder and CEO of Graphite Note, to explore the revolutionary potential of no-code AI.

Graphite Note is a pioneering company in the no-code AI space, offering a platform that enables businesses to implement machine learning without the need for coding expertise. By simplifying the complex processes involved in AI and machine learning, Graphite Note aims to democratise access to advanced data analytics, making it possible for non-technical users to leverage AI for business insights and decision-making.

Smolic discussed the ability of no-code AI to democratise machine learning, the impact on small and medium-sized businesses (SMBs), and future trends that could further drive this transformation.

From data silos to actionable insights

The concept of no-code AI, as described by Smolic, revolves around the idea of simplifying machine learning processes to the extent that even non-technical users can harness its power. This democratisation is crucial in a world where data is abundant, but the means to interpret it are not.

“Technically, we developed a no-code machine learning platform because there is a shortage of data scientists out there,” Smolic explains.

“These are the guys and girls that can actually give meaning to data. So on one hand, there is a shortage of data scientists; at the same time, there is a big pressure and demand for making sense of your data because everyone is producing more and more data every day.”

Traditional methods of building and deploying machine learning models require extensive time and expertise.

“When I started with data science seven or eight years ago, it was always a painful process to go from data, then data preparation for machine learning, then trying out different algorithms. The process was lengthy and expensive,” Smolic says.

However, no-code platforms can streamline this process, reducing the time from weeks to mere minutes. Smolic notes that with just a few clicks of a button, organisations can train a machine learning model without writing actual code, and get the results in 5-10 minutes – as opposed to weeks.

No-code AI’s edge for SMBs

For fintech companies, especially small and medium-sized businesses (SMBs), the advantages of no-code AI are numerous. Smolic emphasises that these platforms can significantly reduce the data processing cycle, allowing businesses to swiftly gain insights and make data-driven decisions.

“In fintech, there are many use cases. Typically, you can predict different metrics like revenue, number of users, or usage of your app. Or you can understand key drivers driving your KPIs to go up or down,” he explains.

No-code AI platforms also enable businesses to leverage predictive analytics without the need for specialised data scientists. This capability is particularly transformative for SMBs that may not have the resources to hire a full-fledged data science team.

“We intended our product to be used by non-technical people, so suddenly, completely non-technical people, maybe Power Excel users, but non-technical in the sense that they cannot develop Python code for AI, can now utilise machine learning,” says Smolic.

“A chief marketing officer, for instance, can upload their Google Ads data to understand what’s driving the number of clicks.”

No-code AI: The next frontier in business data

The shift towards no-code AI is unlikely to be a fleeting trend but a significant movement within the tech industry.

Smolic highlights that the initial focus of no-code AI was on image and video recognition. However, the current trend is veering towards business data applications, which have more direct and tangible business impacts.

“The first no-code AI applications were around video and image recognition. And now it’s turning towards business data, and I think that’s perfect,” he says.

From predictions to prescriptions

One of the most exciting trends is the move towards prescriptive analytics. Unlike predictive analytics, which forecasts future events based on historical data, prescriptive analytics provides actionable recommendations. This progression towards a more prescriptive approach could revolutionise how fintech companies strategise and operate.

By leveraging generative AI, businesses can obtain detailed, actionable insights tailored to their specific data and objectives.

“We are telling our customers exactly what they should do based on their data in order to achieve something, and they choose that something can be reduced churn, increased lead conversion, or increased revenue,” Smolic explains.

Challenges and misconceptions

Despite the promising outlook, Smolic acknowledges the challenges in implementing no-code AI. One significant hurdle is the education and awareness gap.

Even though no-code platforms are designed to be user-friendly, non-technical users still need to understand their capabilities and potential.

“Education is crucial. We have been blogging for the last two to three years, constantly educating people about what’s possible with a no-code ML or AI approach,” Smolic notes.

Another common misconception is that no-code AI can solve all business problems. Smolic is candid about the limitations, explaining that while no-code AI can handle many scenarios, there are still complex cases that require traditional data science approaches.

“A typical challenge can be that no AI can completely solve all business use cases and replace data scientists. There are always more complex projects that require really fine-tuning, and that’s not something that no-code AI can be prepared for any potential business use case,” he admits.

No-code AI in action

The practical applications of no-code AI in fintech are vast. Smolic shares a compelling example of a fintech company using no-code AI to improve their forecasting accuracy.

“Yesterday, a lady from a fintech company visited our booth and was interested in forecasting. They are constantly forecasting something based on the data they have, like balance accounts. It’s fairly easy for no-code AI to predict, based on five years of data, what’s going to happen next year,” he explains.

This kind of application underscores the potential of no-code AI to transform routine business processes into more efficient and insightful operations.

“It’s about making strategies around key drivers in your data. If you have 20 columns of business data, you might find that five of them are the most important for your KPI. We will tell you how they are important and what the sweet spots are.”

Paving the way for data-driven success

The rise of no-code AI signifies a shift towards more inclusive and accessible advanced analytics, empowering businesses of all sizes to harness the power of their data. As the industry continues to evolve, the focus will likely remain on developing practical, real-world applications that drive business value and operational efficiency.

For fintech companies, embracing no-code AI could be the key to staying competitive and innovative in a data-driven world.

“The ultimate goal of data analytics is not just to understand something but to make some actions based on your understanding and be better. If something can tell you this is what you should do to increase your revenue, and you listen to the recipe and you are better, that’s the ultimate goal,” Smolic concludes.

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