This article is written by Nomentia
Cash pooling is a popular solution for balance netting to provide better access and visibility to the group’s liquidity position through a real-time, cross-border, and multi-currency structure. It can be an integral part of a group’s cash management strategy, together with target balancing to have the ability to mobilize cash across the entire group.
Cash pooling is a cash management method for optimizing cash balances within a group. There are two different main approaches to pooling: physical cash pooling and notional cash pooling. In this article, we focus on physical pooling.
To put it simply, in physical pooling, the HQ (parent/holding) company works as a hub for collecting the excess cash from entities and distributing the cash to entities that are short on cash. Obviously, there are many different aspects that corporations should take into consideration when pooling cash, like taxes and legal obligations, which are left out of this article. In many organizations, the treasury function is in charge of cash pooling processes and agreements.
Many banks provide different pooling options, but we will focus on the process where cash pooling is managed within the group. The benefits of having control of the cash pooling in-house are many. Having cash pooling functionality in the Treasury Management System (TMS) will create independence from banks, making the system bank-agnostic while covering any currency.
In the current economic environment, we have recently witnessed a considerable change in interest rates and currency fluctuations. This is one of the reasons why corporations are constantly searching for solutions to manage cross-currency and multibank cash pooling options. Additionally, corporations are interested in calculating the pooling need with intraday information instead of end-of-day balances.
What is then needed for the technical setup of the cash pool? Obviously, some change management and internal communication are necessary before starting to create the technical setup for a new cash pool. Setting up cash pooling should be easy; you should be able to choose the desired balance per bank account. Furthermore, you should be able to define tolerances for the desired balance as well as payment types for the execution and the HQ’s counter account. Particularly when starting with cash pooling, the corporate treasury should have the option to start cash pooling manually, and then when the process is fully in control, they should be able to automate it. There are always different Treasury policies and practices in different corporations. Therefore, executing the actual sweeps or tops should be possible with Straight Through Processing or by using as many approvals as needed. Identifying balance transactions in bank statements should be an easy task.
Above, we discussed cash pooling and the practical setup for it. Many of you might have noticed that the “intercompany loan” between HQ and entities was not discussed in detail. In this chapter, we focus on handling that part efficiently.
Imagine a situation where excess cash is swept from an entity bank account to an HQ bank account. This creates an intercompany loan. There are multiple ways to book the loan, and next, we will focus on utilizing an in-house bank for that purpose.
An in-house bank typically contains one or more accounts, which we call member accounts, per participating entity. Every transaction that hits these accounts will be mirrored to the HQ mirror account. Typically, interest is calculated for the accounts, and in advanced systems, withholding tax is included when applicable. When combining in-house cash pooling and in-house banking, the obvious result would be to allocate the sweep and top transactions to an entity’s in-house bank account. This would mean that any cash pooling transactions could be found on a member account and the Treasury could automatically calculate interest for the intercompany loan as desired. One might ask: How do I allocate the pooling transaction to the entity’s member account? The answer is twofold: firstly, the TMS needs to make sure pooling payments have some individual information that the bank is reporting back and secondly, a sophisticated in-house bank system should have a dynamic way of identifying pooling transactions from potentially thousands of transactions.
There are regions where even the smallest corporations have several bank accounts in several banks operating in just one country. The basic idea here is to have one or two main banks and the others will just provide a vehicle for collecting customer payments. In such a case, it might be interesting to sweep the extra cash from the collecting accounts to an optimized bank account owned by the same entity. This method is similar to cash pooling, but in this case, the counter account is not an HQ account but a different account owned by the same company. The arrangement, of course, also works for cross-border and cross-currencies, as no intercompany balance is created.
In this blog, we have discussed how in-house cash pooling and even target balancing for a company can be done. Some corporations might even consider combining bank-offered cash pooling and in-house cash pooling in a hybrid structure. In such an arrangement, bank accounts belonging to the same cash pool are balanced by that bank and top accounts are managed by the TMS. Perhaps in the future, we will discuss how target balancing could be used in bilateral in-house bank settlement clearing or how intra-day cash forecasting could be used in physical cash pooling.
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This article is written by our partner, Cobase
For decades, large international banks have positioned themselves as gateways to the global financial system. Their pitch is straightforward: one partner, global reach, consistent service. For multinational corporates, the appeal is obvious – simplify banking by consolidating relationships.
But beneath the branding, the idea of a truly “global” bank starts to unravel.
The limitation is not ambition or scale. It is jurisdiction.
Banks do not operate across borders in the way technology companies or logistics networks do. They expand into countries, but once there, they become subject to local rules – rules that define, often in granular detail, what services they can provide, how they provide them, and to whom.
What emerges is less a single institution and more a network of locally regulated entities, loosely stitched together under a common name.
That distinction matters.
A corporate working with a global bank across Europe, Asia, and the Americas might expect a consistent experience. Instead, they encounter variation at almost every layer. A payment setup that works seamlessly in the Netherlands may require adjustments in the United States. A liquidity structure available in London may not be permitted in Mumbai. Even something as routine as onboarding can turn into a multi-country exercise, with separate documentation, timelines, and approval processes for each jurisdiction.
In some cases, the gaps are subtle. A bank may offer ISO 20022 payment formats globally, but local implementations differ. Files accepted in one country may fail in another, not because the standard changed, but because interpretation did. Error handling, cut-off times, and processing logic follow local conventions, not global ones.
In other cases, the limitations are more explicit.
Take liquidity management. In theory, a multinational corporate should be able to centralise cash across accounts worldwide, optimising funding and reducing idle balances. In practice, that depends heavily on where the cash sits. European markets allow relatively sophisticated pooling structures, including notional pooling across entities. Move into markets like China or India, and those structures quickly encounter restrictions. Capital controls, regulatory approvals, and tax considerations can prevent funds from being moved freely or at all.
The result is a familiar problem for treasury teams: cash that exists, but cannot be used.
Payments tell a similar story. While a global bank may offer local payment capabilities in dozens of countries, it does not always control the full chain. In markets where it lacks direct access to domestic clearing systems, it relies on local correspondent banks. For the corporate client, this dependency is largely invisible until something goes wrong. Delays, additional fees, and reconciliation issues emerge, often without clear transparency into where in the chain the problem occurred.
In certain regions, even data becomes fragmented. Regulatory regimes increasingly require financial data to be stored and processed locally. For global banks, this means that account information, transaction data, and reporting cannot always be fully centralised. A corporate attempting to build a real-time, global view of its cash position may find that some pieces simply cannot be integrated in the same way as others.
And then there are the markets where global banks are only partially present or absent altogether.
In parts of Africa, Southeast Asia, and Latin America, even the largest international banks rely on partnerships with domestic institutions. In these cases, the “global” relationship effectively stops at the border, and the corporate is pulled back into the very fragmentation it was trying to avoid.
None of this is accidental. It reflects the underlying structure of the financial system.
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Banking is, at its core, a nationally regulated industry. Governments retain control over their financial systems for reasons that go beyond efficiency: monetary policy, financial stability, capital controls, and oversight. These priorities impose boundaries that even the largest banks cannot cross.
The consequence is a persistent gap between how corporates operate and how banks are structured. Corporates expand internationally and expect their infrastructure to scale with them. Banks expand internationally but remain constrained locally.
This is why even the most sophisticated multinationals rarely rely on a single banking partner. They build networks—combining global banks for reach, regional banks for depth, and local banks for access. Integration becomes their responsibility.
And that is where a different type of solution has started to emerge.
Rather than trying to replace banks or force uniformity where it cannot exist, platforms like Cobase sit above this fragmented landscape and act as an integration layer. They connect to multiple banks—global and local, across channels such as SWIFT, EBICS, APIs, and host-to-host, and standardise how corporates interact with them.
In that model, the complexity of dealing with multiple banking entities does not disappear, but it is absorbed. Payment formats are converted automatically to meet bank-specific requirements. Differences in file structures, validation rules, and communication protocols are handled centrally. Data coming back from banks—balances, transactions, statuses is normalised into a consistent format.
The effect is not that a corporate suddenly has a “global bank.”
It is that it gains a single, controlled interface across many banks.
This distinction is subtle, but important. The fragmentation remains at the infrastructure level where it is dictated by regulation and market structure, but it is no longer fully exposed at the operational level.
In that sense, the role of integration shifts. It moves away from trying to find the one bank that can do everything, toward building a layer that can manage many banks as if they were one.
The “global bank,” then, is less a reality than an abstraction.
What corporates increasingly build instead is their own version of it—on top of the system as it actually exists.
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 our partner, FIS
Receivables finance (RF) has long been a cornerstone of corporate finance, enabling businesses to convert outstanding invoices into immediate liquidity. By selling their receivables to a funder, companies may access cost-effective funding and reduce risk, while investors may gain exposure to short-duration, diversified assets at a competitive return.
However, as recent events surrounding First Brands Group illustrate, this structure remains vulnerable to risks beyond credit, such as fraud. These vulnerabilities highlight the need for greater scrutiny and the adoption of new technologies.
Fraud in RF transactions typically manifests through misrepresentation of receivables quality, double pledging of assets and fabrication of invoices. These risks arise because RF relies heavily on the integrity of the originator’s reporting and servicing processes. If invoices are falsified or pledged to multiple financiers, the asset base becomes compromised, exposing investors and lenders to significant losses.
The First Brands case underscores these vulnerabilities. The U.S. auto parts supplier, which filed for Chapter 11 reorganization in September 2025, allegedly engaged in widespread financial misconduct, including doctoring invoices and double-counting receivables to secure billions of dollars in financing.
Several market features of RF contribute to fraud risk:
These factors can make RF particularly vulnerable when governance fails or liquidity pressures incentivize aggressive accounting.
The First Brands saga has accelerated calls for digital transformation in RF oversight. Advanced technology and reporting platforms can reduce fraud risk through:
The collapse of First Brands is a cautionary tale for all stakeholders in the RF ecosystem. While RF remains a powerful liquidity tool, its resilience depends on effective governance and technological safeguards. Platforms that deliver real-time transparency, automated controls and immutable records are no longer optional: They are essential to maintaining trust and confidence in this asset.
In essence, resilient frameworks are often built on digital efficiency and the irreplaceable insight of experienced practitioners.
As institutional investors continue to seek exposure to trade finance assets and corporates aim to unlock working capital, the combination of advanced technology and human expertise within complex RF structures will help to shape the market’s future.
While digitalization may stand as the frontline defense against fraud, it’s equally vital to maintain effective human oversight by having seasoned professionals conduct independent reviews and engage in regular dialog with originators and funders.
This interplay between technological innovation and expert judgment helps ensure that not only are anomalies flagged automatically, but also the nuances of complex transactions are properly understood and addressed. In essence, resilient frameworks are often built on digital efficiency and the irreplaceable insight of experienced practitioners.
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
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?
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.
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.
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.
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.
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.
A strong treasury automation ROI discussion should not begin with software features. It should begin with operational impact.
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.
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.
