Megaladata Participates in the First FINNOWAY Armenia Forum

Megaladata Participates in the First FINNOWAY Armenia Forum
In November, Megaladata took part in FINNOWAY Armenia, a PLUS Forum event, focused on fintech innovation, digital banking, and financial technologies.

The conference, held in Yerevan on November 11-12, gathered leading figures from finance and tech. Megaladata’s Managing Director, Samvel Arustamov and Marketing Manager Mattia Vercelli attended, showcasing the platform and connecting with industry experts.
 

Driving data innovation in Finance 

At the forum, we connected with financial institutions, tech providers, and key decision-makers, showcasing how Megaladata enables organizations to transform complex financial data into clear insights.

We demonstrated how low-code analytics and automated data processing help financial organizations to:

  • Accelerate digital transformation initiatives
  • Build intelligent decision-making systems powered by data and AI
  • Improve adaptability in areas such as lending, risk management, and ESG analytics

We also discussed how predictive analytics can help financial institutions:

  • Forecast performance and track key metrics in real time
  • Spot anomalies and potential fraud
  • Automate reporting, all while making development simpler and delivering faster results
     

Connecting with industry leaders

FINNOWAY Armenia provided a valuable platform for exchanging ideas, addressing real financial challenges, and exploring how modern analytics are transforming the industry. The event highlighted the fast-paced growth of fintech innovation in the region and the growing role of data and AI in shaping financial systems.

We’re grateful to the organizers and participants for making the first FINNOWAY Armenia a meaningful and forward-looking event.

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About Megaladata

Megaladata is a low code platform for advanced analytics

A solution for a wide range of business problems that require processing large volumes of data, implementing complex logic, and applying machine learning methods.
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