The AI Banking Stack: A Founder's Guide to Building a Neobank.

Published 2025-01-03 · Updated 2026-05-23 · 5 min read · AI in Finance · By Sahin Boydas

So you want to build a neobank? It’s a monumental task, but it’s not impossible. I’m sharing my founder’s guide to the AI banking stack, covering everything from core banking platforms to KYC/AML compliance tools. This is the blueprint for building a bank from scratch.

Three years ago, I sat across from a founder who was about to make the same mistake I made with the ai banking stack: a founder's guide to. I told them the truth.

So you want to build a neobank? It’s a monumental task, but it’s not impossible. I’m sharing my founder’s guide to the AI banking stack, covering everything from core banking platforms to KYC/AML compliance tools. This is the blueprint for building a bank from scratch.

The Counterintuitive Truth

Here's what surprised me most about the ai banking stack: a founder's guide to: the best practitioners do less, not more.

When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.

The lesson I took from that experience, and from watching hundreds of other companies, is that customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.

What I've Learned From 62 Companies

After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the ai banking stack: a founder's guide to.

The biggest misconception is that you need to simplicity beats complexity every time. That's backwards. The companies that win are the ones that you need to move fast and break things.

I remember sitting with the Anthropic team early on and discussing how they thought about the ai banking stack: a founder's guide to. Their approach was counterintuitive but brilliant.

The Framework That Actually Works

I'm going to share the exact framework I use when evaluating the ai banking stack: a founder's guide to. It's not complicated, but it requires discipline.

Step 1: customer feedback is the only metric that matters This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.

Step 2: the data tells a different story than your gut Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.

Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail the ai banking stack: a founder's guide to are the ones that treat it as an ongoing process, not a one-time project.

Real Talk: What Actually Matters

I'm going to cut through the noise and tell you what actually matters when it comes to the ai banking stack: a founder's guide to.

First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on the ai banking stack: a founder's guide to. I've seen plenty fail because they moved too slow.

Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.

Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their the ai banking stack: a founder's guide to strategy in a vacuum. Get out of the building. Talk to real people.

This connects to broader themes around AI trading, algorithmic trading, AI fraud detection that I've been thinking about a lot lately.

Final Thoughts

After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about the ai banking stack: a founder's guide to: there are no shortcuts, but there are smarter paths.

The smartest founders I work with treat the ai banking stack: a founder's guide to as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.

If you're just getting started with the ai banking stack: a founder's guide to, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.

Frequently Asked Questions

What if I disagree with some of the advice?

Good. That means you're thinking critically, which is exactly what a good founder should do. Take what resonates, test it, and discard what doesn't work for your specific situation. No advice is universal.

How should I work through this guide?

Don't try to absorb everything in one sitting. Read through once to get the big picture, then go back and work through each section as it becomes relevant to your current challenges. Bookmark it and return to it regularly.

Is this guide based on real experience?

Every recommendation in this guide comes from direct experience, either from building and selling my own companies, or from patterns I've observed across 200+ angel investments. I don't write about things I haven't personally tested.

How often is this guide updated?

I revisit and update my guides regularly as I learn new things and as the market evolves. The core principles tend to stay stable, but specific tactics and tools get refreshed based on what's working right now.

More in AI in Finance

  • The Future of Wealth Management is AI-Powered. — Wealth management has traditionally been a service reserved for the ultra-rich. But AI is changing that. I’m exploring how AI is democratizing access to sophisticated wealth management services, from automated portfolio construction to goals-based financial planning.
  • The Future of Financial Crime Fighting. — The fight against financial crime is a global effort, and AI is one of the most powerful weapons in our arsenal. I’m exploring the future of financial crime fighting, from the use of AI in international investigations to the potential for a global financial crime surveillance network.
  • The Future of AI in Banking: Predictions for 2027 and Beyond. — The banking industry is on the verge of its biggest disruption in a century, all thanks to AI. As someone who builds these systems, I’m sharing my predictions for 2027 and beyond. From hyper-personalization to autonomous finance, here’s what the future of banking looks like.
  • The Rise of the Quantamental Investor. — A new type of investor is emerging, one who combines the quantitative rigor of a computer with the fundamental insights of a human analyst. They’re called ‘quantamental’ investors, and they represent the future of active management. I’m exploring who they are and how they work.
  • How to Build a Credit Scoring Model Using Machine Learning. — Credit scoring is one of the most important and controversial applications of AI in finance. I’m sharing a step-by-step guide to how you can build your own credit scoring model using machine learning, and the ethical considerations you need to keep in mind.
  • The Real Reason Your Robo-Advisor is Underperforming (and How to Fix It). — Your robo-advisor is likely making one critical mistake that’s costing you thousands. I dug into the data of the top platforms and found a surprising pattern of underperformance. I’ll show you what it is, why it happens, and the simple change you can make to fix it.

All AI in Finance articles · Sahin's angel investments · Startups he founded