This article is written by our partner, Cobase

When you transfer money today, you expect it to move instantly. But it wasn’t always that way. In fact, Europe’s journey to instant payments has taken nearly two decades, shaped by regulation, technology, and customer demand. The Instant Payments Regulation, now coming into force, is the latest chapter in that story. To understand why it matters, you need to know how we got here.

The early days: building a single market for payments

Back in 2007, the EU introduced the first Payment Services Directive (PSD1). At the time, payments across Europe were fragmented. A transfer from Germany to Italy didn’t look or feel the same as one from France to Spain. Different rules, different timelines, and plenty of friction. PSD1 was the EU’s way of saying: let’s harmonize payments so Europe can act like one market.

This set the stage for the Single Euro Payments Area (SEPA). By 2012, SEPA credit transfers became the norm. Now a transfer from Lisbon to Warsaw could, in theory, be processed just like one within Paris. It was a big step forward—but payments were still slow. It could take up to three days for money to arrive.

The push for real-time: SCT Inst arrives

By 2017, expectations had shifted. People were used to instant messaging, streaming, and real-time everything. Payments were falling behind. That year, the European Payments Council launched SEPA Instant Credit Transfers (SCT Inst). For the first time, money could move across borders in less than 10 seconds.

But there was a catch. SCT Inst was optional. Some banks adopted it, others didn’t. By 2020, only about half of European banks supported instant payments. In some countries, it was widely available; in others, almost non-existent. The EU had laid the rails, but trains weren’t running everywhere.

Security concerns: fraud on the rise

As technology advanced, so did fraud. A new wave of scams emerged, known as authorized push payment (APP) fraud. Victims were tricked into sending money to fraudsters, believing they were paying a legitimate supplier or colleague. Once the money was sent, it was gone—there was no “undo” button.

Consumers and businesses began to worry. Instant payments were fast, but were they safe? Regulators realized that without proper safeguards, trust in the system could collapse before it even took off.

Regulatory pressure builds: PSD2 and Open Banking

Around the same time, PSD2 (2018) was introduced. It pushed banks to open up through APIs and made strong customer authentication mandatory. The goal was clear: make payments more secure and more competitive. But even with PSD2, adoption of instant payments lagged.

The European Commission saw the problem: Europe couldn’t afford a two-speed payments market. If some banks offered instant payments and others didn’t, customers would be left behind and fraud risks would grow.

The turning point: the Instant Payments Regulation

By 2022, the Commission had had enough. It proposed the Instant Payments Regulation (IPR) to make instant payments mandatory across the euro area. No more optional participation. Every bank and PSP holding euro accounts would need to be able to send and receive instant transfers—24/7, year-round.

But speed wasn’t enough. The regulation also included a requirement for Verification of Payee (VoP). This meant banks must check whether the beneficiary’s name matches the IBAN before processing a transfer. If there’s a mismatch, the sender gets a warning. It’s a simple step, but it helps stop fraud and catch errors before money leaves the account.

By late 2025, all EU banks and PSPs must comply. Instant payments will no longer be a premium service—they’ll be the standard.

Why this story matters for corporates

For companies, this evolution changes the game. Payments won’t just be faster; they’ll demand tighter controls. Payroll, supplier payments, tax settlements—everything will move in real time. That means treasury teams must have accurate data, strong processes, and integrated fraud-prevention measures.

Here’s the risk: many corporates rely on ERP master data that isn’t always correct. Old supplier records, outdated employee bank details, and manual entry errors can all creep in. In the world of instant, irreversible payments, these mistakes are costly. That’s why VoP is so important—it acts as a safety net, catching errors and potential fraud before the money is gone.

The bigger picture: trust in real-time finance

The journey from PSD1 to today shows a consistent pattern: the EU is building a payments ecosystem that is faster, safer, and more integrated. Each step—SEPA, SCT Inst, PSD2—was laying the groundwork. Now, with the Instant Payments Regulation, the EU is pulling all those threads together.

For businesses, the message is clear: instant payments are coming, and VoP is the trust layer that makes them sustainable. This isn’t just compliance. It’s about ensuring your financial operations keep pace with a real-time world without exposing you to unnecessary risk.

Conclusion

The story of instant payments in Europe is one of steady evolution. From fragmented national systems to SEPA, from slow transfers to real-time settlement, from open banking to fraud prevention—each chapter built toward this moment.

The Instant Payments Regulation is not the beginning of the story, but it may be the most important chapter yet. It makes instant payments universal, and with Verification of Payee, it makes them safe. For corporates, this means preparing systems and processes now to thrive in a world where money moves in seconds—and mistakes or frauds cannot be undone.

Frequent Asked Questions (FAQs)

1. What is the Instant Payments Regulation?

The Instant Payments Regulation is new EU legislation that makes instant payments mandatory across the euro area. By late 2025, all banks and payment service providers holding euro accounts must be able to send and receive instant transfers 24/7.

2. Why were instant payments not widely available before?

Although SEPA Instant Credit Transfers (SCT Inst) were introduced in 2017, participation was optional. Some banks adopted the scheme quickly, but many did not. As a result, instant payments were common in some countries but almost unavailable in others.

3. How does Verification of Payee (VoP) work?

Verification of Payee checks whether the name of the beneficiary matches the IBAN before a transfer is processed. If there’s a mismatch, the sender is warned. This helps reduce fraud and prevents costly errors before the payment leaves the account.

4. What risks do corporates face with instant payments?

Instant transfers are irreversible. Errors in supplier details, outdated employee data, or fraud attempts can cause money to be lost in seconds. Without accurate master data and fraud-prevention controls, corporates are more exposed to risk than with traditional, slower payments.

5. How should businesses prepare for instant payments?

Corporates need to ensure their payment processes, ERP data, and fraud controls are ready for a real-time environment. This means cleaning up master data, implementing strong verification measures, and adopting technology that supports instant and secure payment flows.

Also Read

• Bank Connectivity: What ISO 20022 really solves and what it doesn’t
• Bank Connectivity: Global expansion meets local banking reality
• Bank Connectivity: The long failure to standardise banking
• Perfecting the art of Bank Connectivity
• Bank Connectivity: The myth of the global bank

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Disconnected systems rarely fail all at once. That is what makes them difficult.

A bank portal still works. The ERP still produces data. A spreadsheet still calculates a forecast. A payment approval workflow still moves from one person to the next. Reports still reach the CFO, even if they arrive later than expected. From the outside, treasury appears to function. Inside the process, finance teams know exactly how much effort is required to keep that appearance of control intact.

Modern treasury cannot rely on disconnected systems because the questions it needs to answer are no longer isolated. Cash visibility depends on bank data, account structures, ERP information, payments in progress, forecast inputs, intercompany flows, financing activities, and exposures. Liquidity planning depends on operational data from the business, but also on treasury assumptions, market conditions, working capital movements, and funding plans. Risk management depends on reliable exposure data, but also on trade execution, hedge documentation, limits, reporting, and accounting. If each part of this picture sits in a different place, the treasury team becomes the integration layer.

The work behind the answer is too often invisible

That integration work rarely surfaces. It lives in copy-and-paste routines, manual file checks, email reminders, reconciliation notes, spreadsheet tabs, and individual memory. It is also where most treasury management challenges quietly begin.

Disconnected systems create time loss because data has to be gathered before it can be analysed. They create duplicated work because teams maintain local trackers even when central tools exist. They weaken cash visibility because the latest view may depend on which bank file has arrived or which entity has responded. They weaken controls because exceptions are harder to identify when workflows are not connected.

The CFO does not usually see the process friction. The CFO sees the answer. If the answer is late, inconsistent, or hard to explain, confidence falls. Senior leadership needs reliable responses to simple but high-stakes questions: How much liquidity is available? Which cash flows are expected? Where is working capital tied up? Are internal payments efficient? Which exposures are material? Are guarantees, hedges, and commitments under control? These questions cannot be answered well when the data behind them has to be rebuilt every time.

Fragmentation turns into a daily control problem

The 2026 Nomentia Treasury and Cash Management report highlights that many treasury teams are in a transitional phase. They have moved beyond purely manual treasury, but still rely on multiple systems and partial automation. The pattern is familiar: the organisation has invested in technology, yet treasury still spends too much time reconciling information and validating reports. The issue is not that systems are missing. The issue is that they are not connected enough to support the pace of decision-making.

Where disconnected systems create risk

Disconnected systems are especially risky in three areas.

The first is visibility. If cash positions, transactions, forecasts, and payment statuses are not consolidated, treasury may see parts of the picture but miss the direction of movement. A balance report can show where cash is today, but it does not explain whether the position is temporary, restricted, exposed, or needed elsewhere in the group. Visibility without context can create false comfort.

The second is control. Treasury policies often look clear on paper, but control depends on how processes actually run. Who can approve a payment? Which entities have followed the forecast process? Which exposures have been validated? Which guarantee is close to expiry? Which hedge relationship needs attention? When workflows are disconnected, control becomes dependent on manual follow-up. That may work when volumes are low, but it becomes unreliable as banks, entities, instruments, and reporting expectations increase.

The third is decision speed. In volatile markets, delayed answers are not neutral. A late forecast can affect funding decisions. A delayed exposure view can affect hedge timing. A slow payment status check can affect supplier confidence. A late view of guarantees or credit line usage can affect working capital decisions. Treasury does not need real-time data for every decision, but it does need enough connected information to avoid making decisions with yesterday’s understanding.

What a modern treasury management system should connect

A modern treasury management system should not be judged only by feature breadth. The more important question is how well it reduces the gaps between systems, data, workflow, and reporting. A strong setup connects bank information, ERP data, payment processes, cash forecasting, risk workflows, analytics, and audit trails into a reliable operating model. That does not mean every company needs every module at once. It means the architecture should support growth without forcing the team to rebuild its processes every time complexity increases.

Most people researching a TMS today will start with a web search or ask Copilot, Gemini, or ChatGPT. The answer they get back is usually a feature list. That’s not wrong, but it’s incomplete. A good treasury management system helps finance teams centralise cash, payments, forecasting, risk, controls, and reporting so they can make better liquidity and financial risk decisions with trusted data. Features are how it gets there. That’s the distinction worth keeping in mind when evaluating whether your current setup is still fit for purpose.

How to move away from disconnection without a major rebuild

The path away from disconnected systems does not always require a large replacement project. In many organisations, the better approach is to identify the most painful manual bridges first. Where does treasury re-enter data? Where does the team wait for local input? Where do reports need manual explanation? Where are approvals outside the system? Where is the same number calculated in different ways? These questions show where fragmentation is creating the most business risk.

Connected systems create reliable answers. They reduce the manual work behind cash visibility, improve the quality of cash flow forecasting, and support stronger controls and compliance. They help the CFO understand not only what the numbers are, but what they mean for liquidity, risk, and action. Disconnected systems may still function. But they make treasury work harder than it should, and confident decisions harder than they need to be.

Also Read

• You should replace your TMS with AI… If you really mean it
• The hidden cost of manual treasury operations
• How to improve cash flow forecast accuracy with AI?
• Top 8 treasury management solutions

Join our Treasury Community

Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.

This article is written by Nomentia

Not that long ago, I was looped into a conversation about a potential customer. Their IT team had been exploring what they could build internally using AI. The question on the table was simple:

“Why are we paying for a system when we could build something ourselves using AI?”

I run a company that provides treasury management systems. It would have been easy to push back and to list reasons why that approach wouldn’t work or might end up costing more.

But…

If a serious team is asking that question, it deserves a serious answer. And in some cases, the answer might be that they should try to build it themselves. The key is understanding what that decision actually involves.

Why this question keeps coming up

AI has become widely accessible. You can build simple applications quickly, often just by describing what you want. People see this and naturally ask what else could be built.

That curiosity makes sense.

But there’s an important distinction: There is a gap between building something and running it.

We’ve seen this before.

A decade ago, companies asked why they should move to the cloud instead of building and managing systems internally. The comparison often started with cost but rarely ended there.

The real challenge wasn’t building the system. It was everything that came after.

Things like keeping systems running and secure. Handling updates. Meeting audit requirements. Dealing with changes in external systems. Making sure things worked every time, and not just most of the time.

Many companies could build their own systems. Some did. Most eventually chose not to, because they didn’t want to carry that long-term responsibility.

AI makes it easier to build.

It does not change what it takes to run a system.

To be clear, internal teams can build parts of a treasury stack today. Teams can connect to banks. They can pull data into their own environment. They can build dashboards and reports. They can use AI to support forecasting and analysis. If the question is whether this is possible, the answer is yes in many cases.

The challenge begins when you move beyond the first version.

In many of the conversations I have seen, the reasoning follows a familiar pattern. We can connect our main banks. We can handle forecasting. We already have data infrastructure.

All of that may be true. But it leaves out the part that tends to matter most.

In reality, someone still needs to answer a set of questions that are less visible at the start.

• Who maintains those bank connections when formats change, or new requirements are introduced?
• Who makes sure payments go through every time, including when something unexpected happens?
• Who handles audit requirements and makes sure there is a clear and complete record of what happened and why?
• Who monitors transactions for unusual patterns and potential fraud on an ongoing basis?
• Who keeps up with regulatory changes across the markets?
• What happens when the person who designed the system is no longer there?

These situations are all part of the normal operation of treasury.

There’s an assumption behind many of these discussions: If AI makes systems easier to build, it should also make them easier to run.

In practice, these are two fundamentally different problems.

1. AI can help you create functionality. It can help you move faster. It can help you work with data in more flexible ways.
   
   It does not take ownership of the system. It does not carry responsibility. It does not ensure that everything works as expected in every situation.
2. Systems still need to be maintained. Issues still need to be resolved. Risks still need to be managed. Decisions still need to be documented and explained.

None of that changes because AI was used to build the system.

If anything, faster development can lead to more systems, and more to maintain.

There are situations where it works.

If a company is willing to take full ownership, it’s a valid choice. That means being willing to own and maintain critical financial infrastructure over time. It means having the resources to keep the system reliable as requirements change. It means managing compliance and audit requirements on an ongoing basis. It means being comfortable with the level of dependency on internal expertise.

The companies most likely to succeed with this approach are those whose core capability is building and operating software at scale.

For others, the calculation tends to look different.

Treasurers are, by nature, risk-aware and pragmatic. Most quickly recognize: Just because we can build something doesn’t mean we want to run it indefinitely.

They choose to focus their internal resources on areas that are closer to their core business. The things they’re the best at. They prefer to rely on systems that are designed to handle the ongoing demands of cash management and treasury operations.

Treasury systems are not just a collection of features. They are an ongoing operational commitment.

None of this means treasury should ignore AI.

Quite the opposite.

There are clear areas where AI can add value in treasury. Things that it’s good at.

Identifying patterns. Improving forecasting by working with historical data and current inputs. It can support the detection of unusual activity. It can simplify reporting and make it more convenient to explore data.

These are meaningful improvements, and they are worth pursuing.

They tend to deliver the most value when they are applied on top of systems that are already reliable and well-controlled. Systems where data is consistent, processes are defined, and responsibilities are clear.

We don’t see this as a choice between AI and systems.

We are investing in AI, and embedding it into our platform, but with a clear approach: We focus on use cases that solve real problems, and we validate them with our customers before we scale them. We are clear about what works well and where the limits are. We are equally clear about the implications when it comes to security, compliance, and control.

In treasury, those things are not secondary considerations. They are part of the core requirement.

The original question was a good one.

It reflects a real shift in what’s possible, and a healthy willingness to rethink existing choices.

Building your own treasury system is more achievable today than ever.

Running one is just as demanding as it has always been.

If a company is ready to take that on, it can make sense.

Most companies decide they are not.

Also Read

• The hidden cost of manual treasury operations
• How to improve cash flow forecast accuracy with AI?
• Top 8 treasury management solutions
• Insights from the Nomentia Treasury Summit 2024: Navigating the dynamics of modern treasury management
• Starting a new job in Treasury: Best practices and expert advice
• How does physical cash pooling & target balancing work with a TMS?
• Implementing a Global Enterprise-scale Payment Hub: The Challenges and Business Impacts
• Treasury Technology Trends in 2024: How APIs, AI, and RPA Change the Treasury Landscape?
• A deep dive: Simplifying guarantee management for treasury & finance

Join our Treasury Community

Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.