What Awaits the Financial Industry in 2026: Outlook and Reflections (Part I)
By Ray Ruga – Co‑founder of Fintech Americas, contributor at ITBS.
Latin America’s financial industry is entering a new phase. Not one of “transformation”—a word that has lost all meaning—but one of definition. A phase of separation between those who will lead and those who will disappear.
2025 was the year everyone played with Generative AI. Proofs of concept, pilots, press releases announcing “strategic alliances” with technology providers. Very nice. The problem is that playing is not competing. And 2026 will be the year the industry discovers who actually knows what they’re doing.
The question is no longer “Are you using AI?”—everyone is using it, or at least says they are. The real question is: Are you capturing real value, or are you subsidizing your vendors’ learning curve?
According to Deloitte, nearly 7 out of 10 CEOs say AI will be key to redefining their strategy. Of course they say that. It’s what they’re supposed to say. What’s interesting is not what they declare, but what they are building. And that’s where the real conversation begins.
To understand what is truly happening, Fintech Americas has just published its 3rd Annual Financial Industry Outlook Report. It’s not an academic exercise. It’s 88 industry leaders—from Bancolombia to Nubank, from the Banco Central do Brasil to BBVA—sharing what they see coming. No corporate filters. Twenty‑four categories. The trends that will define who wins and who loses.
Artificial Intelligence: Recess Is Over
In 2025, having a generative AI chatbot was innovation. In 2026, it’s the bare minimum. The playing field has shifted.
The focus now moves toward AI agents—systems that not only answer questions but make decisions, execute processes, act. And that changes everything: data architecture, governance models, the way institutions think about risk.
The biggest challenge won’t be technological. It will be leadership. Who decides what an AI agent is allowed to do? Who is responsible when it makes a mistake? How do you train it with data that reflects the institution’s values rather than the biases of the past?
The institutions that solve this first will gain an advantage that will be hard to catch. Those that keep playing with pilots will be left watching from the sidelines.
