This article is written by ETR Digital
Like every corporate treasurer, one of my main jobs is to make sure there’s enough liquidity to keep the business running smoothly, support growth, and give us the financial flexibility we need. But that’s often easier said than done!
Lately, I’ve been exploring some different tools for unlocking working capital. And with everything going on in the digital trade space, I figured I’d take a look at Digital Negotiable Instruments (DNIs). Because they can help speed up payments and receivables processes, unlocking significant amounts of liquidity and improving cash flow management.
Good question! And I only discovered them through a session at an industry conference, so you’re not alone in never having come across them. In a nutshell, DNIs are essentially the digital version of old-school paper trade instruments like bills of exchange and promissory notes. But instead of dealing with stacks of paperwork and slow manual processes, everything’s securely handled on a digital platform. So, it’s nothing totally new and risky, it’s an innovative way to approach an age-old challenge.
In practice, DNIs mean faster, clearer transactions – and much less waiting around. Where paper-based instruments might take days or even weeks to settle, DNIs can be wrapped up in a fraction of the time. That’s a big win when you’re trying to unlock liquidity.
Before using DNIs, we were facing a pretty common issue: too much working capital was tied up in slow trade cycles. We had payables and receivables stuck in the system, delaying access to cash we could’ve been using elsewhere in the business. I’m sure that feels like a familiar frustration.
Then we decided to start using DNIs (it wasn’t a tough switch). And the difference was like night and day.
Take our cross-border transactions. They used to be really paper-intensive, taking weeks to finalise payments and get funds moving. But with DNIs, we’ve cut down the processing time dramatically. That’s helped us speed up cash inflows and bring down our DSO.
We also brought DNIs into our supplier financing programme. Again, easy to do. And it meant that suppliers got paid faster, we improved our own cash conversion cycle, and we could even extend our own payment terms without damaging supplier relationships. A win-win (yes, it might sound a bit clichéd but it’s true).
Those few switches have made all the difference already. And it was easy to know which working capital ‘levers’ to pull because I used the ICC’s free Cash Conversion Cycle Calculator – and it showed me exactly where our cash was getting stuck.
So, I encourage you to check it out https://ic4dti.org/getting-started-ccc/ It’s super easy to use, anything you plug in is private, and it will give you a clear picture of where your working capital could be improved. For us, unsurprisingly, a lot of the inefficiencies were from outdated trade processes. But digitalising them has made a huge difference almost immediately.
If, like me, you’re a corporate treasurer looking to make your cash work harder, I can’t recommend DNIs enough. They’ve helped us streamline operations, reduce delays, and unlock liquidity that was previously gathering dust in slow-moving processes. They’re essentially just a better version of what was there before. Like upgrading from a landline phone (or carrier pigeon in some instances!) to the latest smartphone.
And if you’re not sure where to start, give the ICC’s Cash Conversion Cycle Calculator a try. It’s a quick way to spot opportunities to improve your cash flow – and helps put you in control.
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 [HERE] or fill out the form below.
This article is a contribution from our partner, Embat
As finance temas evolve from manual, file-based operations to real-time data-driven environments, one technology is quietly reshaping the backbone of financial connectivity: APIs (Application Programming Interfaces). For years, finance teams have been forced to rely on SWIFT files, SFTP servers, and nightly batch updates. Today, APIs are enabling instant access to cash positions, real-time payment execution, and seamless data flows between systems. The result? Faster decision-making, better control, and radically improved operational efficiency.
But what does it actually mean to “connect your TMS to your ERP and your banks via API”? In this article, we’ll break down the API building blocks of a modern treasury stack, explain how to integrate them, and share a real-world example of what this looks like in action.
APIs are software interfaces that allow different systems to “talk” to each other in real time. Think of them as the pipes that connect your treasury plumbing: your ERP (where transactions originate), your TMS (where liquidity and risk are managed), and your banks (where cash is held and moved). Instead of uploading files or sending emails, APIs enable direct, instant communication between these systems.
In treasury, this means:
APIs reduce manual errors, enhance visibility, and provide agility in responding to market changes — all of which are critical in today’s volatile financial environment.
To understand API integration, let’s break the treasury tech stack into three parts:
This is your accounting system — SAP, Oracle, Microsoft Dynamics, Netsuite , etc. — where invoices, payroll, and vendor payments originate.
This is the control tower for treasury. It manages cash flow forecasting, FX risk, intercompany loans, and bank account visibility.
These are your cash custodians. Banks offer APIs for balance reporting, payment execution, transaction notifications, and FX services.
APIs connect these three layers, enabling a seamless data exchange between operational finance (ERP), strategic decision-making (TMS), and execution (banks).
A typical API integration follows this flow:
Your ERP sends payment proposals, forecasted cash flows, and invoice data to your TMS via API. This helps the TMS consolidate positions and project liquidity across time horizons.
The TMS sends validated and approved payments, FX deals, or sweeping instructions to your bank via its corporate API. Banks respond instantly with confirmations, reference numbers, or error messages.
Your TMS receives real-time balance and transaction data from each bank account via API. This data can also be passed to the ERP for reconciliation and reporting purposes.
Together, this closed loop allows treasurers to manage cash, risk, and payments from a single interface — without ever exporting a file.
When integrating APIs in treasury, data protection and access control are essential. Key areas to focus on:
In short, treat APIs like opening a digital vault: only the right people and systems should ever get the key.
Implementing API integrations doesn’t have to be a chaotic and complex project. Follow these principles for a smooth rollout:
A European technology company using Embat’s TMS integrated its ERP (Netsuite) and two main banking partners via API. The project enabled the following:
The result: 90% fewer reconciliation errors, cash visibility by 9:00am every day, and real-time FX rate validation before settlement. What used to take hours and emails now happens automatically, every day.
APIs are no longer a buzzword — they are the foundation of a modern treasury infrastructure. By connecting your TMS, ERP, and banks through APIs, you enable faster, smarter, and more secure treasury operations. Whether you’re a multinational or a fast-scaling startup, building an API-first treasury stack is the next logical step toward efficiency, agility, and control.
Now is the time to assess your systems, talk to your banks, and start building the infrastructure of tomorrow — today.
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
Your cash flow forecast is lying to you. Not on purpose, but it’s wrong—probably by a lot. Spreadsheets lull you into a false sense of control, but the numbers lie. They miss shifts in spending, overlook delayed payments, and crumble the moment reality deviates from last month’s assumptions. By the time the cracks show, you’re already scrambling—plugging holes, chasing down cash, and making desperate decisions.
Hubert Rappold works as a Senior Treasury Expert at Nomentia, bringing his over 20 years of expertise to serving Nomentia’s customers’ treasury needs and challenges in payments, cash visibility, and forecasting.
Johannes Pöschl is a Senior Data Scientist at Nomentia, specializing in predictive analytics and AI-driven cash flow forecasting solutions.
A cash flow forecast that’s even slightly off can send your business into a tailspin. As Hubert Rappold puts it: “Small deviations in cash flow forecasts can lead to big financial risks.” Underestimate expenses, and you’re suddenly short on cash when payroll hits. Overestimate revenue, and you’re making spending decisions based on money that isn’t actually there. “Basing your decision on a forecast that is substantially off; you might end up making decisions based on inaccurate data—like borrowing unnecessarily or mismanaging liquidity.” The further your forecast drifts from reality, the riskier every financial move becomes. And in a world where margins are tight, customers pay late and surprises lurk around every corner; imprecise forecasting isn’t just an inconvenience but a liability.
At its core, cash flow forecasting is just this: predicting how much money will move in and out of your business over a given period. It’s not about spreadsheets or fancy formulas but knowing, with as much certainty as possible, whether you’ll have enough cash to cover what’s coming.
Most businesses, big and small, still rely on outdated tools and methods that weren’t built for today’s financial landscape. Here’s why they fail:
Let’s be clear—treasurers and finance managers are some of the most skilled problem-solvers in any business. They know how to fine-tune their forecasts, clean up messy data, and make Excel do what they need. But time is limited, and expectations keep rising. The demand for instant, up-to-date cash visibility means they can only do so much, no matter how skilled they are.
“Excel-based forecasting worked perfectly fine when business environments were more predictable,” explains Johannes Pöschl. “But today, cash flows are influenced by a web of factors—seasonality, economic shifts, even supplier behavior. Static spreadsheets just can’t keep up with that complexity.”
And here’s the real danger: many forecasts depend entirely on the person who built them. If that expert is unavailable—whether they leave, take a vacation, or simply get swamped—no one else knows exactly how their formulas and models work. That’s a terrifying prospect for any company relying on accurate cash flow predictions.
This is where AI-driven automation changes the game. Instead of a fragile, human-dependent system, businesses get a dynamic, always-updating forecast that adjusts in real time. It doesn’t replace the finance team—it gives them superpowers. Let’s take a look at:
For many, forecasting cash flow, is a reactive scramble. No wonder, when traditional forecasting relies on static models and best guesses. Implementing AI into your forecasting you can go beyond static formulas and outdated assumptions. Not just automate your forecasting, but make it smarter, spot hidden patterns and continuously refines projections.
“AI forecasting can lead to more objective forecasts, leaving behind the impact of regional optimism biases in forecasting,” says Johannes. “They can also incorporate resource prices and estimate their effects on supplier prices that traditional models or treasurers might miss.”
As Hubert adds: “AI can automatically classify transactions from bank statements, showing finance teams exactly where cash flow discrepancies are coming from—late customer payments, unexpected supplier costs, or seasonal trends. That kind of insight is invaluable.”
Here’s how it stacks up against traditional methods:
| Traditional forecasting methods | Challenges for forecast accuracy | AI in forecasting: Key techniques for accuracy improvement |
| Manual data entry & spreadsheets | Prone to human error, delays, and data inconsistencies | Automated connectivity & forecast reconciliation: integrating real-time bank & ERP data to eliminate errors and update forecasts dynamically. |
| Rule-based forecasting (fixed models) | Rigid assumptions fail to capture real-world volatility | Machine learning & pattern recognition: AI-powered forecasting learns from past errors and adapts dynamically to new trends. |
| Historical trend extrapolation | Fails to account for sudden economic shifts, external shocks | Multi-variable analysis: AI-driven forecasting incorporates economic indicators, market trends, and business-specific variables. |
| Limited scenario planning | Forecasts become unreliable in times of uncertainty | Risk simulations & stress tests: AI forecasting can run multiple scenarios to assess financial resilience under various conditions. |
| Static payment terms-based Cash flow predictions | Overlooks customer-specific behavior, which leads to inaccurate receivables forecasts | Dynamic payment behavior predictions: AI forecasts can analyze past payment trends to predict late payments with higher accuracy. |
| Isolated departmental forecasting | Fragmented cash flow data across finance, treasury, and operations | Automated connectivity: Forecasts integrate multiple data sources for a holistic, real-time view of liquidity. |
| Lack of external market consideration | Ignores macroeconomic trends, FX rates, inflation, and geopolitical risks | Sentiment & market trend analysis: AI-supported forecasts process can incorporate market sentiment indices, GDP forecasts, interest rates and other data affecting the business environment to refine forecasts. |
| Reactive forecast adjustments | Adjustments are made after discrepancies occur, not proactively | Self-learning algorithms: continuous refinement of forecasts based on real-time variance analysis. |
| Delayed cash flow reconciliation | Forecasts deviate from reality due to mismatches in receivables/payables data. | Automated invoice matching & forecast reconciliation: reconciling forecasts against actual bank transactions in real-time. |
“Take something as simple as public holidays,” says Johannes. “They affect cash flows differently depending on the industry and country, and AI can model these effects automatically. Over time, the system refines itself, making forecasts even more accurate.”
Hubert gives another example: “AI can analyze past customer payment behaviors to refine expected due dates. But even simple logic—like applying Days Sales Outstanding (DSO) metrics—can significantly improve accuracy, especially for short-term forecasts.”
Let’s consider:
Every morning, the Treasurer of a globally operating business opened the same monster Excel file—a tangled web of formulas, manual inputs, and linked sheets that somehow held the key to the company’s cash flow. Keeping it updated was a full-time job. Data trickled in from subsidiaries across time zones, bank accounts were scattered across multiple institutions, and assumptions had to be constantly tweaked. Forecasting was supposed to provide clarity, but instead, it felt like a high-stakes guessing game.
The demands from leadership kept growing: More accuracy. More real-time visibility. More risk mitigation. But with what? The Treasurer had already pushed Excel to its limits, building an intricate system that only they truly understood. When the CFO needed answers, they delivered—but not without late nights, countless emails chasing missing numbers, and a nagging fear that one small mistake could throw everything off.
Then came the talk about AI-powered forecasting.
It sounded promising—automation, real-time data analysis, better predictions. But there was also an unspoken worry: What if this replaces me? What if all my expertise, my hard-earned knowledge, gets sidelined by software?
And yet, the bigger fear wasn’t AI. It was this. This endless cycle of manual work, desperate fixes, and hoping that when leadership asked for insights, the numbers weren’t off—because if they were, it would be their name on the line. Worse still, if they ever stepped away, who else would even know how to keep this monster running?
That’s when the real question hit: What’s the bigger risk—adopting AI or continuing like this? Spending hours babysitting spreadsheets?
The reality hit. So they ran the numbers. Even a small boost in forecast accuracy would cut emergency borrowing and save more than enough to justify the investment. The business case was clear. It was time to kill the spreadsheet before the spreadsheet killed them.
“A lot of treasurers worry that AI will replace them,” Johannes notes. “But in reality, AI is just a tool—it provides forecasts, but treasurers still bring the expertise to validate and interpret them. The most successful teams use AI to eliminate tedious manual work, freeing themselves to focus on strategy.”
Hubert agrees: “If you set up AI-driven forecasting right, you don’t just improve accuracy—you make life easier. Treasurers get instant feedback, can compare past forecasts to actuals, and refine their approach over time. The goal isn’t to replace them; it’s to give them better tools.”
You wouldn’t steer a company based on gut feelings alone—so why accept guesswork in cash flow forecasting? AI isn’t a magic fix, but it’s the difference between informed decisions and financial blind spots. The real risk isn’t AI—it’s sticking to spreadsheets while the world moves forward.
Treasury Mastermind 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 [HERE] or fill out the form below to get more information.
From Treasury Masterminds
Verification of Payee (VoP) was designed to address a specific problem: money being sent to the wrong account. Whether through fraud, human error, or manipulated vendor data, misdirected payments cost businesses—and banks—billions every year. VoP was meant to close that gap by checking the payee name against the account details before any funds move.
In theory, it’s a straightforward control. In practice, the picture is considerably more complicated. After years of rollout, adoption remains patchy. Corporates are applying VoP at different stages of the payment lifecycle — and some have skipped it altogether. The question is no longer whether VoP is a good idea. The more useful question is: where does it actually add value, and where does it fall short?
The most underappreciated dimension of VoP is not whether you do it, but when. Most organisations that have implemented it apply the check at the point of payment — the moment before a transaction is submitted. That feels logical. It’s the last gate before money leaves.
The problem is that by that point, a significant amount of work has already been done. The vendor has been onboarded. A purchase order has been raised. An invoice has been approved. The payment has been queued. If a VoP check at the payment stage returns a mismatch, the team isn’t just looking at a flagged transaction — they’re looking at a compromised process that may need to be unpicked entirely.
This creates a dilemma that treasury and finance operations teams know well: hold the payment and absorb the internal friction, or release it and accept the risk. Neither option is satisfactory. And yet it is the predictable consequence of applying a verification control too late in the process.
The alternative — verifying at onboarding, before a vendor is activated in the system — addresses this at the root. Mismatches surface before any transactional relationship is established. The organisation has time and leverage to investigate without payment deadlines creating pressure to override controls.
Even organisations that have implemented VoP at onboarding are often exposed by a different vulnerability: changes to existing vendor records. Business email compromise (BEC) fraud, one of the most financially damaging forms of corporate fraud, frequently works not by creating fake vendors but by hijacking real ones.
The attack is straightforward. A fraudster impersonates a known supplier, contacts accounts payable, and requests a bank account update. The vendor exists in the system. They have a payment history. The change request looks plausible. If the organisation’s process involves verifying account changes against the VoP database at the time of the request — and acting on the result — the fraud can be caught. If that step is absent or bypassed, the next payment to that supplier goes to a fraudulent account.
This is an area where many VoP implementations have a genuine gap. The check was set up for new payees. The assumption was that existing, validated vendors are safe. That assumption does not hold when vendor master data can be changed, and changes are not automatically re-verified.
The opt-out rate for VoP among corporates is higher than most public commentary acknowledges. The reason is rarely ignorance of the risk. It is usually a calculation of operational cost.
False positives are the central issue. VoP matching is imperfect. Name variations, trading names that differ from legal names, and data quality gaps in bank records all generate mismatches on legitimate payments. Each false positive requires investigation. In a high-volume payment environment, the cumulative cost of those investigations — in time, in staff, in delayed payments — can quickly outweigh the perceived fraud prevention benefit.
The integration burden compounds this. For organisations running payments across multiple banks, ERPs, and jurisdictions, building a VoP check into every relevant workflow is a non-trivial technical project. When the business case is unclear and the operational disruption is tangible, deferral becomes the rational short-term choice.
This is not an argument against VoP. It is an argument for implementing it in a way that reduces operational friction — which means earlier in the process, with better data quality, and with clear protocols for handling exceptions.
VoP is a useful control. It is not a complete fraud prevention framework. Organisations that treat it as the latter will have gaps.
A VoP check confirms that a name and account number are linked in the bank’s records at that moment in time. It does not confirm that the person requesting the payment is authorised to do so. It does not confirm that the invoice is legitimate. It does not confirm that the account has not been compromised since the last payment. It does not replace the controls around who can modify vendor master data, or who can approve payments above certain thresholds.
The organisations seeing the best results from VoP are those that have positioned it as one layer in a broader control stack: onboarding validation, change management protocols, payment approval workflows, and ongoing monitoring working together rather than treating a single check as sufficient.
From a banking perspective, VoP sits at the intersection of compliance, fraud prevention, and customer service — not always a comfortable place. Banks are required to provide the infrastructure for VoP checks and respond to queries accurately. But the quality of the data underpinning those responses varies, and banks are not always in a position to explain why a mismatch has occurred or help a corporate resolve it quickly.
There is also a regulatory dimension that is still evolving. The rules around what banks must check, what liability they carry when a VoP check passes a fraudulent payment, and how cross-border transactions are handled are not yet fully settled. This creates uncertainty for corporates trying to build policy around VoP outcomes — particularly around what a ‘match’ actually means in terms of assurance.
VoP is not going away. Regulatory pressure in Europe and beyond is pushing towards broader mandatory adoption, and the underlying logic — verify before you pay — is sound. But the current gap between policy intent and operational reality is significant enough that many organisations are either not using it, using it poorly, or using it at the wrong point in their processes.
Closing that gap requires a clearer-eyed view of where VoP actually adds value (upstream, at onboarding and on changes), where its limitations lie (anything downstream of a compromised data change, or outside its matching logic), and what needs to sit alongside it to build a genuinely robust payee verification process.
A 45-minute panel session hosted by Treasury Masterminds in partnership with SisID. Three practitioners — from banking, corporate treasury, and fintech — take a practical look at how VoP is being used today, where it falls down, and what needs to complement it.
Speakers:
Moderated by Patrick Kunz, Founder of Treasury Masterminds.
Free to attend. Click below to register and submit questions ahead of time
