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.

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.

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.

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.

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.

FedNow went live on July 20 with 35 certified institutions. After decades of batch-processed payments, the Federal Reserve now has its own instant payment rail. I've been waiting on something like this for a while, and I'm already turning over what it means for community banks.

I've been turning over how many banking processes start the same way. Someone needs to prove they are who they say they are. Account opening, wire transfers, loan applications, address changes. Every single one begins with identity.

GPT-4 was released in March 2023, and I've had time since then to spend with it on personal projects, comparing it against 3.5. The difference is not subtle.

I've been thinking about the SVB collapse from the startup side. The banking analysis has been thorough. Duration mismatch, concentrated deposits, digital bank run. But there's another story that hits closer to home. The story of the founders who spent that weekend not knowing if they could pay their teams.

A month out from the SVB collapse, the immediate panic has subsided but the lessons haven't. The biggest one for me is a distinction that sounds simple but changes how I think about risk. SVB was well-capitalized. It ran out of cash. Those are two very different problems.