AI Agents in Payments & Open Finance: The Execution Layer of Finance

AI agents are quickly becoming the new interface to financial services.

But the real story in payments is not conversational AI. It’s execution.

In the next wave of financial infrastructure, agents won’t just recommend actions — they will initiate payments, optimise routing, enforce policy, and create an evidence trail.

That transition only becomes possible at scale because of Open Finance.

Open Finance is not only about data sharing. It is about permissioned access — the missing layer that transforms agents from “smart assistants” into delegated operators inside regulated financial systems.

Quick definitions

What are AI agents in payments?AI agents in payments are software entities that can interpret intent (e.g., “pay supplier invoices”), evaluate constraints (risk/policy/liquidity), select the best payment rail, execute the transaction, and record an audit trail.

How does Open Finance enable AI agents?Open Finance provides consent-driven, regulated access to bank accounts and payment initiation. This turns AI agents from advisory tools into execution systems with explicit permission scopes.

What is the difference between agentic payments and automation?Automation follows fixed rules. Agentic payments combine rules with data, context, and dynamic decisioning — including retries, fallback rails, exception handling, and auditability.

AI agents are moving from “front-end assistants” to “back-end operators”.But in payments, execution is regulated — which means autonomy only works when it’s governed.

This is where Open Finance becomes a catalyst: it adds consent, permissions, and initiation rails that allow agents to act safely.

Why this matters now

Why AI agents in payments are suddenly real

Payments has always been operationally complex:

• multiple rails (SWIFT, instant payments, A2A, cards, wallets, stablecoins)

• scheme and corridor-specific rules

• fraud monitoring and compliance constraints

• reconciliation and exception handling

So far, AI adoption in payments has been largely observational:

• analytics

• categorisation

• fraud scoring

• operational dashboards

Agents shift the centre of gravity from observation to action.

This is why the agent trend matters:the product advantage moves to the orchestration layer — the system that decides how a payment should happen, not merely that it happened.

Open Finance is the bridge from “assistant” to “operator”

Without Open Finance connectivity, an AI agent can be limited:

• it can answer

• it can suggest

• it can draft instructions

But it cannot act.

Open Finance introduces the rails required for safe execution:

• Consent: who authorised the agent, for what scope, for how long?

• Access: verified account and transaction data

• Initiation: the ability to trigger bank payments (A2A / pay-by-bank)

• Governance: logging, traceability, dispute handling

The real architecture: “Consent → Decision → Execution → Evidence”

The modern payment stack for agents looks like this:

Step 1: Intent

The user expresses intent:

• “Pay my suppliers today”

• “Optimise FX for this corridor”

• “Keep a minimum balance buffer”

• “Avoid rails with high failure rates”

Step 2: Consent + mandate scope

The agent checks what it is allowed to do:

• account scope

• transaction limits

• time validity

• merchant whitelists / blacklists

• approval requirements

Step 3: Data intelligence (ISO 20022 becomes the fuel)

Agents are only as good as the data they can interpret.

ISO 20022 structured signals make a difference because they allow:

• better transaction classification

• enrichment of remittance and purpose codes

• improved compliance decisioning

• consistent reconciliation across banks

This is where the industry transitions from message formats to data intelligence.

Step 4: Policy + risk decisioning

Before executing, the agent must answer:

• Is this allowed?

• Is this typical behaviour?

• Are there anomaly signals?

• Does it breach internal risk thresholds?

Step 5: Rail selection + routing orchestration

The agent chooses execution pathways:

• A2A / pay-by-bank (Open Finance rails)

• SWIFT/ISO corridors

• local instant rails

• tokenized settlement where available (stablecoin rails)

Step 6: Evidence + audit trail

In regulated environments, execution without evidence is unacceptable.

So the agent must produce:

• reason codes

• policy checks performed

• data used in decisioning

• timestamps

• approvals / exemptions

This evidence trail is what makes AI acceptable to compliance and audit teams.

What “agentic payments” really means in production

Agentic payments isn’t a chatbot making transfers.

It is a controlled execution engine that can:

• route dynamically

• retry with alternatives

• handle exceptions

• trigger human approvals when required

• produce an audit-ready trail

This is how payments become “governed autonomous operations.”

The competitive advantage is not the agent — it’s the control plane

Many companies can build an agent UI.

The durable moat forms underneath, in what we call the Payments Decisioning Control Plane:

• rules and policy engines

• ISO 20022 enrichment and quality scoring

• dynamic routing and fallback orchestration

• fraud and anomaly correlation

• audit logs and explainability

Because, the winners won’t be those with the smartest agent.They’ll be those who own the governed infrastructure that agents run on.

About PaymentLabs

PaymentLabs builds structured data intelligence and orchestration capabilities for modern payments — spanning ISO 20022 operations, Open Finance initiation and policy-driven routing across rails.