
Insights from Mastercard’s Nima Sepasy on finance use cases for solutions built with emerging tech like generative AI.
Finance leaders who may be a tad tired of questions from senior executives or board members on how they plan to put the magic of generative artificial intelligence to work should not let the hype overshadow this reality: Gen AI has “inherent superpowers” they can tap to reach higher levels of accuracy, efficiency and transformation in areas including cash forecasting, liquidity management and cross-border payments.
That’s one takeaway from a Strategic Finance Lab podcast interview about AI, machine learning and other emerging technologies with Mastercard Senior Vice President for Innovation, Insight and Engagement Nima Sepasy. You can listen to his conversation with NeuGroup Insights editor Antony Michels now on Apple and Spotify.

Nima Sepasy, Mastercard
Mr. Sepasy leads a team within Mastercard’s innovation arm devoted to identifying use cases and finding opportunities to apply Gen AI and other cutting-edge technology to create solutions that solve problems and deliver real value to merchants, consumers, banks, fintechs, and governments. Mastercard currently has about 300 AI models in production.
Despite fatigue with AI hype, “we’re seeing enterprises really understand there are practical applications they could be building today,” Mr. Sepasy says. “Enterprises are experimenting in a multitude of ways and they’re really understanding where we are in terms of market readiness; they’re starting to move away from too many pilots into a much more focused development zone. And we’re seeing real value being delivered both within Mastercard and with our partners and customers.”
The key to reaching a zone where technology enables innovation that leads to practical solutions, is—as many NeuGroup members are well aware—data. Mr. Sepasy calls the overarching concept “datafication”—the transformation of every aspect of an organization so it can use, analyze and leverage data for insights in new ways.
“Quite often when I have conversations with our customers and they ask me, ‘how do I get more AI-enabled,’ really the first step is investing in data quality, security and integration,” he says in the podcast. “Then, use that bedrock to be able to really leverage AI into these new solutions and capabilities.”