Who Did Mastercard Lighthouse x EBRD Choose?

Last week, the projects selected for the Mastercard Lighthouse Turkey x EBRD Star Venture program were announced. Unsurprisingly, AI projects dominated the list.

Looking a bit closer at the projects, you can feel the financial perspective that Mastercard brings, but there’s no clear common thread in terms of concept or technology. There’s data analytics, AI, and blockchain.

There’s financial analysis, operational processes, and even tokenization.

Take F-Ray Financial Technologies, for example. It’s an analysis platform for stock markets that combines fundamental and technical data, mainly targeting end users.

Finsmart AI, on the other hand, approaches the topic not from the investment side, but from internal financial management processes. It’s a project aimed at companies, supporting finance teams in decision-making through cash flow, budgeting, and performance indicators.

With Hardal, data analytics, privacy, and architectural choices take the spotlight. It’s a project that values data security.

Then there’s Freya (YC S25), a “Voice AI” platform that brings AI to customer interactions through voice and speech, focusing on call centers and similar corporate areas.

Novus offers a platform targeting companies, allowing them to create multi-step AI workflows and AI agents that can be integrated into their business processes.

Rudiq, a blockchain-based platform, focuses on the tokenization of real-world assets. It aims to support different scenarios for the digital representation of assets on the blockchain.

Rudiq caught my interest the most, partly because it’s blockchain-based and about tokenization, and partly because I know some of the team. Their selection for the program and recent investments they’ve secured show that the traditional finance world is quite interested in transparent and network-based tokenization. That makes me happy.

When we take a look at the project selections for the program, a common theme stands out: instead of “general-purpose” solutions targeting every user or company, they’ve preferred approaches that are clearly positioned around a specific problem area, focusing on collaboration and compatibility. None of the chosen projects present technology as an end in itself; they’re more about how they integrate into existing business processes and financial systems.

This picture offers an important clue about how large, regulated institutions view innovation. They’re not looking for the newest technology or the most ambitious vision; they want portable, controllable solutions.

Mentorship and acceleration programs seem to act more as an early-stage filter than a showcase. But, of course, we shouldn’t interpret this filter as a sign that the projects left out are lacking or not promising. Each program and institution naturally gravitates towards projects that align with their own field, knowledge, and experience. It’s exciting to see the growing number of incubation programs within the ecosystem and how they’re finding different supporters from various sectors.

And on top of that, seeing that traditional financial institutions are still interested not just in AI projects, but also in technologies like data analytics and blockchain, honestly brightened my week.