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Why modern treasury cannot rely on disconnected systems

By Alexander von Schirmeister,
CEO at Nomentia

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.

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Why modern treasury cannot rely on disconnected systems

You should replace your TMS with AI… If you really mean it

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

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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.

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replacing your TMS with AI

The hidden cost of manual treasury operations

This article is written by Nomentia

Why are manual treasury processes expensive?

Manual treasury processes become expensive because they require recurring effort to collect balances, prepare payment files, update forecasts, check approvals, and reconcile data. Even when each task seems manageable, the combined impact can reduce efficiency, slow down decision-making, and increase operational risk.

Treasury teams are used to making imperfect systems work.

A spreadsheet here. A bank portal there. A local ERP export from one entity, a payment file from another, and a cash forecast that still depends on email updates from the business. None of these workarounds may look dramatic on their own. In many organisations, they are even seen as normal.

The problem is that “normal” can become expensive.

Manual treasury operations rarely create one large, visible cost line. Instead, they create a pattern of hidden costs: time spent collecting data, delays in decision-making, duplicated effort, payment exceptions, outdated forecasts, missed visibility, and control gaps that only become urgent when something goes wrong.

That is why treasury automation ROI should not only be discussed as a technology question. It is also an operating model question. How much time does treasury spend managing the process instead of managing cash, liquidity, payments, and risk?

Why manual treasury work is difficult to measure

The cost of manual work is often underestimated because it is distributed across people, entities, systems, and routines.

A treasury analyst may spend hours preparing a daily cash position. A regional finance team may manually upload payment files. Another person may validate bank data, check approvals, update forecasts, or investigate why one bank statement does not match the expected format.

Each task may be manageable. Combined, they create a significant operational burden.

This is also why many teams struggle to build a cash forecasting business case. The value of better forecasting is not limited to “faster reporting”. It is the value of better decisions: knowing earlier where liquidity is needed, reducing dependency on outdated data, improving confidence in funding decisions, and giving leadership a clearer view of what may happen next.

External research points in the same direction. PwC’s 2025 Global Treasury Survey notes that treasury teams are under pressure to improve cash visibility, cost efficiency, and risk management, while leading organisations increasingly adopt real-time liquidity tools, AI-enhanced forecasting, and centralised payment models. HSBC also highlights that cash flow forecasting has remained a key treasury priority, reflecting the need for precise and timely forecasts in a volatile environment.

In other words, manual treasury processes are not only inefficient. They can slow down the organisation’s ability to respond.

The cost of fragmented cash visibility

Cash visibility is one of the clearest examples of hidden treasury cost.

When balances are collected manually across banks, accounts, currencies, and entities, treasury may technically have the data, but not necessarily in time to act on it. The team may know yesterday’s position, but not today’s. It may have a consolidated view, but only after several people have updated files, checked bank portals, and reconciled different formats.

That delay matters.

Without timely visibility, companies may keep too much cash idle in one place while borrowing elsewhere. They may struggle to identify trapped cash. They may make liquidity decisions based on incomplete information. They may also spend valuable time explaining numbers instead of improving them.

Nomentia positions its Smart Treasury Suite around visibility, control, and predictability across payments, cash, liquidity, and risk, integrating with ERPs, banks, and other systems. For companies operating across multiple banks and entities, that integration layer is not just technical infrastructure. It is the foundation for turning fragmented data into usable treasury insight.

The cost of manual payments

Payments are another area where manual processes can appear cheaper than they really are.

At first glance, uploading files through bank portals or managing payments across local workflows may seem acceptable. The team knows the process. The banks are connected somehow. Payments are executed. Work continues.

But payment operations carry a high cost when they depend on scattered portals, inconsistent approvals, manual file handling, and local exceptions.

The hidden costs include time spent preparing and checking payment files, resolving format issues, validating approvals, tracking payment statuses, and answering questions from subsidiaries, AP teams, banks, and auditors. More importantly, weak payment control can increase exposure to duplicate payments, missed cut-offs, fraud attempts, and compliance issues.

This is where payment automation benefits become easier to explain. Automation is not only about faster payment execution. It is about standardising the process, improving traceability, reducing manual intervention, and making payment control easier to prove.

The cost of unreliable forecasting

Forecasting is often where manual treasury processes become most visible to leadership.

The CFO does not necessarily see how many files were collected, how many emails were sent, or how many adjustments treasury made before the forecast was ready. But the CFO does see when the forecast is late, when confidence is low, or when the numbers change without a clear explanation.

A manual cash forecast can still be useful. Many experienced treasury teams are excellent at working around incomplete data. But as the business grows, expands into new markets, adds banks, or inherits systems through acquisitions, the limits become harder to ignore.

Forecasting depends on data quality, timing, ownership, and repeatability. If treasury spends too much time gathering inputs, it has less time to analyse drivers, challenge assumptions, and model scenarios. A forecast that takes days to prepare may already be outdated when it reaches decision-makers.

This is why the business case for treasury automation should include both time savings and decision quality. Faster data collection is valuable. But the larger value often comes from giving treasury more time to interpret what the numbers mean.

The cost of controls that rely on people remembering the process

Manual controls are often built around expertise. The team knows which approvals are needed, which files need checking, which bank deadlines matter, and which exceptions require escalation.

That works until complexity increases.

As more entities, banks, users, and payment types are added, control becomes harder to manage consistently. Processes may differ across countries. Approval rules sit outside the system. Audit trails may require manual reconstruction. Exceptions depend on individual knowledge rather than embedded workflows.

In a stable environment, this may go unnoticed. During growth, restructuring, audit, staff changes, or periods of financial pressure, it becomes a risk.

The Nomentia Treasury Trends Report 2026 describes treasury teams facing pressure to deliver real-time insights, stronger controls, and more strategic input, often while dealing with fragmented systems and limited IT support. The report is based on 384 treasury and finance leaders across the Nordics, DACH, Benelux, and the UK.

That is the reality many treasury teams recognise: expectations are rising faster than operational capacity.

How to think about treasury automation ROI

A strong treasury automation ROI discussion should not begin with software features. It should begin with operational impact.

  • Where is treasury losing time today?
  • Which manual tasks are repeated every day, week, or month?
  • Where do payment processes create avoidable risk?
  • How much effort goes into collecting and validating data?
  • Which decisions are delayed because cash visibility or forecasts are not ready?

From there, TMS cost savings become easier to frame. The value may come from fewer manual hours, lower operational risk, more efficient payment execution, improved cash visibility, reduced dependency on spreadsheets, or stronger audit readiness.

The most useful business case is not a generic promise that automation saves money. It is a structured estimate of where the organisation currently loses time and where better treasury processes could create measurable improvement.

Also Read

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.

Register here
The hidden cost of manual treasury operations