AI transaction monitoring for agentic banking risk

4 snips

Mar 25, 2026

Tyllen Bicakcic, co-founder of Payment AI who advises banks on agentic banking and payments infrastructure. He explains what agentic banking means and why AI that can act on accounts raises intent and authorization questions. They explore real use cases, how transaction monitoring must evolve, trust and compliance challenges, and vendor controls for safer AI-driven payments.

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INSIGHT

Agentic Banking Means AI That Moves Money

Agentic banking is AI that moves beyond answering questions to perform real banking actions like making payments and paying vendors on behalf of customers.
Tyllen frames it as AI taking the same actions a banker or customer would in online banking, not just chat responses.

ANECDOTE

Saving For A Ticket Demonstrates Practical Agent Value

Tyllen shares a concrete use case: an AI helps a college student save for a Taylor Swift ticket by analyzing transactions and recommending automated transfers.
The agent can set up automatic savings, cut costs suggestions, and execute transfers across accounts on schedule.

ADVICE

Log Agent Chain Of Thought For Auditable Decisions

Design agentic systems to record the agent's chain of thought so auditors can see why a transaction occurred and review the agent's decision steps.
Tyllen argues auditability of AI reasoning gives more transparent evidence than human decisions.

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What is up fraud fighters, and welcome to Fraud Forward!

Today we’re talking about AI transaction monitoring and why it matters so much as agentic banking starts moving from concept to reality.

Because once AI agents in banking move beyond simple chat and into actions, payments, and customer instructions, the risk conversation changes fast.

This is no longer just about whether AI in banking feels helpful.

It is about whether institutions can actually monitor intent.

Trace authorization.

Support banking dispute resolution.

And reduce fraud risk without losing the trust they’ve worked so hard to build.

In this episode, I sat down with Tyllen Bicakcic to break down what agentic banking actually means, why it is gaining traction, and why AI transaction monitoring is going to become one of the most important control layers in this entire conversation.

What I really appreciated about this discussion is that we did not stay at the hype level.

We talked about real use cases.

Real banking friction.

Real fraud questions.

And the very real challenge of how banks can build safer banking with AI without creating new blind spots around governance, payment risk monitoring, and customer trust.

And the reality is this.

If AI agents are going to influence transactions, then transaction monitoring in banking has to evolve right alongside them.

What you'll hear in this episode

• What agentic banking actually means and how it differs from traditional AI in banking

• Why AI agents in banking create new questions around intent, authorization, and accountability

• How AI transaction monitoring can strengthen banking fraud prevention and AI fraud prevention

• Why trust infrastructure in banking matters as much as innovation

How banks can think about AI governance in banking without rebuilding everything from scratch

• Why banking compliance and AI must move together as agentic payments become more practical

• How banking dispute resolution changes when an AI agent initiates or influences a transaction

• Where real-time fraud monitoring, payment risk monitoring, and AI risk signals fit into the next phase of digital banking AI

You should listen to this episode if

• You lead fraud, risk, compliance, BSA, AML, or payments programs at a bank or credit union

• Your team is evaluating agentic banking or AI agents in banking

• You are thinking about how AI transaction monitoring should work in real financial services AI environments

• You want a better understanding of AI payment authorization and dispute handling in an AI-driven world

• You care about building safer banking with AI without giving up control, visibility, or trust

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