Building AI agents for banking is hard. Real hard.
Not only because AI agents are difficult to build.
Not only because banking workflows are complex.
Not only because agents work with sensitive customer data.
But because these agents have to solve real business problems
They may need to reduce onboarding time, speed up dispute resolution, or investigate fraud alerts faster.
And they have to do all of this within regulatory constraints.
That is the real challenge.
Here’s what frustrated me
I couldn't find a single site showing how to build AI agents that solve real business problems.
Some sites explained AI agents.
Some explained banking concepts.
Some explained cloud architecture or data workflows.
But none of them showed how these pieces connected.
Instead, you’re left jumping between vendor blogs, AI papers, architecture diagrams, and regulatory guidance.
Then you still have to figure out how it applies to real workflows like KYC, AML, fraud, onboarding, disputes, payments, or insurance claims.
So I rebuilt PMExaminer to connect these pieces and show how AI agents can be built for banking under regulatory constraints.
Here’s what PMExaminer actually does
It teaches the principles of building AI agents for banking workflows.
You’ll learn how to choose the right workflow to start with, what the agent should do, where human review belongs, and what needs to be captured for audit.
You’ll also learn how to connect the agent to a real business outcome.
Each idea is broken down into small, concrete steps, showing one part of how these agents get built.
This helps you make better decisions when building AI agents inside regulated financial environments.
A bit about me.
I’m Manu, founder of PMExaminer.
I’ve built products at Fortune 500 financial institutions — TD Bank and Manulife.
At TD Bank, I helped build a mobile app for high-net-worth clients from scratch. The app reduced churn and improved revenue.
Now I’m applying that product lens to AI agents in banking.
