What Mattered and What Was Noise
Plenty happened in bank technology this year, and most of it was noise. Rather than cataloging all of it, I want to name the two shifts that actually changed how I think, the ones that still felt real once the conference hype wore off. They are not the flashiest stories of the year. They are simply the two that mattered.
The Governance Gap for AI That Acts
I have spent enough time building with AI agents on my own projects to be genuinely impressed by them. I have also spent enough time around banking to know that the harder I imagine putting one to work inside a bank, the faster I hit a wall, and the wall is not technical. It is governance. The moment software stops suggesting and starts acting, the frameworks a bank relies on to stay safe begin to strain.
From Answering to Doing
The thing that finally made AI agents click for me was a small distinction. A chatbot can tell you how to reverse a fee. An agent can go reverse it. One explains the steps. The other takes them. Once I started playing with that on my own projects, the shift stopped feeling incremental and started feeling like a different category of tool.
What the Middleware Collapse Revealed
For a few years, Banking as a Service looked like the rare arrangement where everyone won. Fintechs got to offer bank accounts without a charter, small banks got fee income and a foot in the digital door, and the people using the apps got slick products. I wanted that arrangement to work. Years ago, when I was building a payments product, a partnership like this was exactly what I was looking for and couldn't get. So when the model started breaking in the most visible way possible, I paid close attention, this time from inside a bank rather than outside trying to get in.
Modernizing Operations Without Losing the Relationship
For a while now, operations has struck me as the place where technology could do the most good inside a community bank. Not the headline projects. The day to day work of serving clients and moving loans through the pipeline. The easy assumption is that more automation is the goal in itself. I see it differently. The point of operations technology is to make the bank faster, more responsive, and easier to work with, for clients and for the people serving them. That is a different target than automation for its own sake.
Why Communication Is the Hard Part
Most of the conversation about data strategy at a community bank ends up in the wrong place. People want to talk about tools, vendors, dashboards, the latest architecture. The harder problem is sitting one floor up. The people making decisions do not need more data. They need a small set of things they can act on, and they need it consistently.
Mostly a Vendor Management Problem
The hardest part of using AI in banking is not the technology. It is figuring out where AI usage fits inside the vendor management discipline that banks already practice.
What I've Learned Outside of Work, and What I'm Thinking About
A year ago, ChatGPT showed up and changed the conversation. I've spent a lot of personal time since then digging into what these tools can actually do. My assumption going in was that everything would change overnight. What I've learned is more bounded, and more interesting, than that.
Rules, Models, and the Tradeoffs Between Them
The more I learn about fraud, the more I realize how much of banking is shaped by the people trying to exploit it. Fraud isn't a side problem. It influences how accounts are opened, how transactions are monitored, how clients are verified, and how much friction everyone tolerates along the way.
How the Three Layers Work
Banking as a Service has been a buzzword for a few years now, but the mechanics of how it actually works are often misunderstood. People use "BaaS" and "embedded banking" interchangeably. They are not the same thing, and the distinction matters.
