How AI and ML are used in payment fraud detection (16 use cases)

This article is written by Nomentia

Artificial Intelligence (AI) and Machine Learning (ML) are two technologies that have been widely discussed in recent years. They offer a range of tools that are gradually being integrated into our personal and professional lives. These technologies are particularly useful in situations where there are many time-consuming and manual tasks. Or where there is a large amount of data to analyze. One such application is payment fraud detection. Before diving into the use of AI and ML in fraud detection, it is important to address them separately.

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What is AI exactly?

AI systems are designed to integrate vast amounts of data with intelligent algorithms. They mimic or simulate human-like actions and decision-making processes. AI can be utilized for various techniques like problem-solving, natural language processing, image recognition, reasoning, learning, and more. The technology functions in a way that collects data, processes it, and selects an algorithm. It trains a specific model, modifies the model’s parameters, and evaluates the results. This can then potentially be deployed in the real world. As it operates, AI typically uses feedback loops from its users to improve over time.

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What is machine learning exactly?

Machine learning is a form of AI that teaches a system to think in similar ways to humans, as it learns and improves based on previous experiences. Machine learning algorithms typically need little supervision because they are learning by themselves.

We usually distinguish between three techniques of machine learning: 

Supervised learning

Supervised ML algorithms are taught to make predictions or decisions primarily based on previous data patterns. Here, the ML learns based on already labeled data. So, input data needs to be categorized beforehand. So that the algorithm will use it as a benchmark to come to conclusions when analyzing new data.

Unsupervised learning

In unsupervised ML, there is no need for labeled or categorized data. Instead, the algorithm tries to find patterns or relationships in the data without explicit guidance. Typically, the goal of unsupervised ML is to explore data and find clusters or relationships.

Reinforcement learning

With reinforcement learning, you train the algorithms to make sequential decisions. In an environment with a reward or penalty feedback mechanism that it learns from to improve its subsequent decisions. Usually, no labeled data is available. The algorithm needs to learn from its own experiences and the feedback it receives.

The difference between AI and machine learning

AI refers to the simulation of human intelligence in machines that are programmed to mimic human-like actions and decision-making processes. AI encompasses as a wide range of techniques, including problem-solving, natural language processing, image recognition, and expert systems, among others. It aims to create systems that can perform tasks that typically require human intelligence. Tasks such as reasoning, learning, perception, and problem-solving.

Machine Learning is a subset of AI that focuses on developing algorithms. This enables computers to learn from and make predictions or decisions based on data. ML algorithms allow machines to improve their performance on a task through experience without being explicitly programmed for that task. It does so by using statistical techniques to enable computers to learn patterns and relationships from data and make decisions or predictions.

Why are these topics relevant to payment fraud?

Recent research by PwC showed that 51% of organizations have experienced fraud in the past two years. A 20-year high compared to previous survey results. Fraud mainly impacted organizations in terms of financial losses. Respondents highlighted that many of them will require new, advanced technologies to tackle the issue. Some respondents also mentioned that fraud had more considerable consequences. Like operational disruptions or damage to the brand or customer loyalty. Ultimately, AI has the potential to assist businesses in maintaining a secure payment environment. Thus safeguarding a company’s customers, revenue, and reputation.

RESEARCH BY PWC SHOWED THAT 51% OF ORGANIZATIONS HAVE EXPERIENCED FRAUD IN THE PAST TWO YEARS — A 20-YEAR HIGH COMPARED TO PREVIOUS SURVEY RESULTS

PWC’S GLOBAL ECONOMIC CRIME AND FRAUD SURVEY 2022

With the rise in cybercrime and the evolving sophistication of financial threats, we’ve come to an era where humans cannot keep up with processing an abundance of data efficiently and securely. We can by no means compete with the speed and thoroughness of data interrogation that AI and ML can deliver today. As a result, we need to embrace and team up with such technologies. To support this view, a recent study by the Association of Certified Fraud Examiners revealed that 17% of organizations already leverage AI and ML to detect and prevent fraud, and 26% of organizations are actively planning to adopt fraud detection AI or ML in the next two years. On top of that, technology providers are now heavily investing in developing practical AI-driven solutions to tackle payment fraud.

How can AI and Machine Learning be used in fraud detection?

These days, ML and AI can help you with fraud detection in various ways. Let’s focus on some of the main ways how organizations currently leverage the technologies and what the future may bring:

  1. It helps with transactional efficiency

For example, AI and ML can streamline payment processes and enable faster risk identification in payables, receivables, and reporting. They can help manage exceptions or spot anomalies in large data sets based on previous patterns it has studied.

  1. Large payment data files can be fed to AI

AI can analyze large datasets much faster than human beings, and it provides good insights and points to pay attention to. Faster analysis will also help speed up decision-making. Especially with more data than ever and little time to analyze, it will become essential to save time while deriving insights by leveraging tools like AI and ML.

  1. Automate and streamline routine tasks

AI can help automate essential but manual tasks such as data entry, reconciling payments, or generating reports. Minimizing manual processes, in turn, reduces the room for errors and fraud.

  1. Reconciling payments

Reconciliation of payments is essentially comparing two data sets with each other and finding matches, which AI and ML are incredibly good at. Even when anomalies arise, you can train AI to handle them in pre-set ways.

  1. Identifying complex relationships

Machine learning tools are great at uncovering complex relationships from data humans may overlook. Hence, it can provide quick and valuable insights that are potentially strategically important or can impact cash flow.

  1. Screen payments against historical payment data

Any payment data can be screened based on benchmarking it with historical payment patterns to pinpoint out-of-the-ordinary payments faster.

  1. Risk scoring based on certain factors

In some cases, AI has been used to score payments by risk based on factors like locations, banks, sums, recipients, countries, previous behavior, and much more. This way, you can easily identify the payments with higher risk levels and make sure they’re safe.

  1. Adaptive learning

The advantage of the learning capabilities of AI is that it can, over time, identify risks increasingly well. It allows new types of risks to be spotted that you previously may not have caught.

  1. Suspicious account behavior

In a few cases, AI has been applied for account takeover detection. For example, if unusual payment behavior or account usage patterns occur, machine learning has the capability to recognize it and notify the right person or put a stop to it, limiting any further financial damage.

  1. Invoice fraud detection

When integrated effectively, machine learning can analyze incoming invoices and identify out-of-the-ordinary, duplicate, mismatched amounts, or other indicators of fraud. With a large number of incoming invoices, this can help payable departments save time.

  1. Cleaning master data 

Master data is one of the biggest assets of companies, and AI can help maintain it to be in good health. For example, it can organize it by categorization, create links or connections between values, and clean up master data into more usable formats, to name a few. Better structured master data provides better insights and makes it easier to spot fraud.

  1. Dealing with false positives

You can let AI check future payments against historical payment data and establish certain tolerance levels and sensitivity for false positive management. Since dealing with false positives can be a lot of work for accounting, treasury, or finance, it can help speed up the process.

  1. Trends analysis

Suppose AI is connected to the internet or apps in real-time; it can deduce trends, market data, signals, and other external factors, providing insight into potential disruptions or risks at an early stage. There are various use cases for this. For example, it can help predict fraudulent attacks based on cybercrime trends, and it could even assist with considering the impact of fraud on cash flow in forecasts and help develop what-if scenario analyses.

  1. AI as middleware

AI is evolving rapidly, becoming more integrated with many apps and systems. In the future, it could be leveraged as a step-in-between systems for data checks, restructuring, reformatting, and other purposes to help speed up payment processes and minimize payment fraud.

  1. Sanctions screening

AI is incredibly good at comparing data sets, which is exactly how sanctions screening works. Some payment technologies already offer sanctions screening, but AI has the potential to do so as well. This can be particularly helpful for teams that do not yet have a payment hub in place.

  1. Giving instructions

Perhaps in the future, payment hubs and TMS technologies will offer instructional functionalities like you’re already used to with ChatGPT, Google Bard, or Microsoft Copilot. Imagine all the questions you could ask based on all the data. Why did X payment not process? What is my exposure? How much do I have available on Y account? What caused the discrepancy between our budget and forecast?

Important additional considerations when using AI or ML

While AI offers unprecedented opportunities for innovation and efficiency, it also raises various considerations that demand careful attention. Some of the things that we suggest you should at least consider:

  1. Do you really want to share sensitive data with AI that stores your data to strengthen their intelligence? Most companies and contracts with customers do not allow you to share sensitive data with third parties. With cyber-attacks on AI suppliers, there is always a risk that your data will be leaked in the event of a breach.
  2. Like with all data, the garbage in garbage out principle also counts for AI. If you provide AI with bad data, you will receive wrong interpretations, which can be very misleading when making decisions.
  3. The longer algorithms run, the more accurate they should become. So, if you keep flagging false positives, fraud, and other risks to AI, it will start identifying those better over time. So, try to be patient. Though AI can help ease the number of false positives, you can still expect quite some false positives at the start until algorithms have learnt to filter those out.
  4. If you become too dependent on AI and it does not catch a mistake, it can go unnoticed unless you remain cautious and always keep a human eye on things.
  5. Ensure that an accountable person in the team can be held responsible for checking data quality and the accuracy of AI-generated outcomes. There’s always a risk that AI is wrong and it is up to the people who use it to examine any analysis thoroughly.
  6. It is best to start small and slowly scale technologies like AI before rolling them out all at once. Even if it’s rolled out fully, for example, to all entities, it still means that entities must comply with their data input. Expect a lot of work cleaning up the data and getting buy-in from all stakeholders. All stakeholders must also clearly understand the benefits and use cases, so communication about such projects is vital.
  7. Sandbox environments can be a great way to test out the application of AI or ML. They provide a safe environment to play around with data without damaging any critical company data while still simulating the real impact that the technologies have.

Are AI and ML the future of payment fraud?

Companies, banks, and financial technologies will likely incorporate new ways to leverage AI and ML technologies for fraud detection in the future. The true use of AI and ML was limited but is expected to develop rapidly. Recently, Euro finance hosted a webinar where some of the biggest companies explained how they leverage AI in treasury, finance, and accounting. It is definitely worth a watch if you are interested in how AI will shape the future beyond payment fraud.

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By Alex Cannon

Across 111 European public tenders for treasury systems, training is named in 47% of notices. The highest-ranked treasury capability is FX and hedging, at 13%.

uropean public bodies have to write down what they want from a treasury system, in public, before anyone can bid.

It all gets published on TED, the European Union’s procurement journal.

I looked at 111 notices, published between May 2018 and October 2026.

Training appears in 47% of them.

After that, the highest-ranked treasury capability is FX and hedging, at 13%.

Why I am raising this

A product that a qualified professional cannot operate without a course has been designed badly.

This market has stopped treating that as a defect and started pricing it in instead.

Some training is unavoidable. A treasury system touches bank mandates, approval limits and payment files and no amount of good design removes the need to show a new user how a particular organization has set those up.

A degree in the domain, 40 hours of instruction and a consultant on call is not that. That is a product nobody can learn by using it.

The newer products in this category are not like this. You open one, click around for an afternoon and it explains itself.

So why are buyers still signing for dinosaur software and then signing again for somebody to explain it?

Why a buyer budgets for training at all

Nobody buying a treasury system is new to treasury.

The people who will use it know what a forward contract is, what a cash position is, which balances get swept at the end of the day.

The training in these notices is not teaching them their job. It is teaching them the product.

What they are paying to be taught is where things are, what the screens are called, which order the steps go in and what the system wants before it will let you do the obvious thing.

The European Central Bank bought its treasury management system in 2018 for €16,534,744. Since then it has signed two consultancy frameworks for that same system: €7m in 2020 and €11.7m in 2025.

That is €18.7m to be helped with a system it paid €16.5m for. The advice has outgrown the software, at an institution staffed with some of the most qualified treasury professionals in Europe.

A degree in the domain, a budget to learn the software and consultants on retainer for the parts that stay unclear.

Does nobody else find this strange?

Where training ranks

Share of 111
Training46.8%
Cloud or SaaS delivery21.6%
FX and hedging12.6%
ERP integration12.6%
Cash flow forecasting10.8%
Debt and investment10.8%
Reporting and dashboards10.8%
Multi-entity groups8.1%

Why it comes out as “training”

Nobody writes “this software is confusing” into a procurement document.

You can write 40 hours of training into a contract. You can name a number of sessions, a count of on-site days, a trainer’s qualifications. All of it is enforceable, it survives legal review and a bidder can put a price on it.

You cannot write “make it obvious” into a contract.

So the learning curve gets stated in the only currency procurement accepts, which is people and days. Training tops this list not because treasury teams want courses, but because it is the one line where a buyer can put a number on the distance between signing and competence.

What a buyer can take from it

Score time-to-competence. Half your peers write training into their requirements.

If “how long until my team runs this without you” is not a line on your scoring sheet, you are scoring a different purchase than the one you are making.

The demo is run by the person who wrote the training. Ask what the fifth week looks like for somebody who wasn’t in the room.

And read a few notices yourself.

Public bodies publish requirement sets at a level of detail corporate buyers keep private, written by people who had to think a whole project through before they were allowed to publish anything.

If you are a young vendor, most of these are not for you

Public tenders look like an open door. They mostly are not.

A public buyer has to justify the decision in writing, to people who were not in the room.

That incentive produces the oldest reflex in enterprise software: nobody ever got fired for buying IBM.

The notice encodes it, through reference requirements, revenue and insurance thresholds, years of comparable deployments and a scoring sheet that rewards the familiar answer.

Nearly half of the treasury software awards that reported a bid count drew a single bid, which is the sound of a decision made before publication.

Unless you hold something specific, an existing relationship with the buyer, a local presence the notice requires, a capability in the specification few others can meet, bidding is an expensive way to lose.

What is useful in these documents is not the opportunity. It is the requirement set, telling you what the incumbents are being paid to compensate for.

What clarity is worth

None of this is the consultants’ fault. If a system takes a month to explain, somebody is going to get paid to explain it.

That is real work and there is real demand for it.

What I keep coming back to is how calmly it gets signed off. Nobody at the ECB had to argue that the treasury system was hard to use. The money went through as consultancy, twice, five years apart and the word “confusing” never had to appear in a document.

So if you are building in this category, that is the number to look at.

You do not have to beat them on functionality. You have to be the system a treasurer can open on Monday and use by Wednesday with nobody sitting next to them.

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This article is a contribution from our content partner, PrimeTrade

I never would have thought, at the start of my emerging career as a trade banker, supply chain financier and potential “Master of the Universe” (per Tom Wolfe), that debit notes would be interesting, and that thirty years later I might be writing a blog about them.

But here we are!

It is quite a long post, but this is a topic that merits a detailed treatment, even if the subject matter is rather dry and somewhat technical.

Myths and realities

This post sets out five myths about invoices and debit notes.

These myths can lead to various confusions, complications and inefficiencies when it comes to supply chain finance.

The post aims to set out clearly how things normally work in the real world of general ledgers and corporate ERPs. It also digs into the implications for SCF and for the design of platforms that help suppliers to access supply chain finance.

There are generally three strategies used to deal with debit notes in supply chain finance – and these are covered in the final section.

With the right design and approach, debit note recovery can be automated and fully-optimised – no more spreadsheets, no more manual tracking, and no need to get suppliers to deduct debit notes from future invoices.

What is a debit note?

A debit note acts as the evidence in the buyer’s books for a reduction in its expenses.

So it is issued by the buyer’s accounts payable team to a supplier when the buyer seeks to pay less than it has been invoiced.

For supplier financing transactions (including SCF), a debit note can be an unwelcome event. If an invoice has been financed and even purchased by a financier, the financier typically relies on the invoice being paid in full.

Myth #1: invoices can be “approved” or not approved

What if I told you that “invoices always get approved”?

A valid invoice is one that is properly addressed, is from a recognised counterparty (ie: a supplier), has the right taxes on it shown in the right way, and which adds up correctly.

Valid invoices always get approved and there is a good reason.

This is a fundamental part of the way financial controls work. A valid invoice has been received representing a claim for payment from a supplier and it will be posted in full to the general ledger.

If this didn’t happen, we would get in a mess very quickly. Accounts payable teams cannot use their discretion and post valid invoices to the general ledger with whatever numbers on them they decide might be right.

Audits would be failed and financial controls would have broken down.

If the invoice from the supplier is for an amount that is too high (for whatever reason), a debit note is raised by the buyer so the supplier gets less money. The question is “what’s the debit note?” not how much of the invoice should be approved.

Some accounts payable teams will, alternatively, ask the supplier to raise a credit note and the invoice remains pending and not posted until the credit note is received. Here, for ease of explanation, we assume the debit note route is the one that the accounts payable team uses.

Myth #2: Payables are the same as invoices

In the ordinary course of business, accounts payable teams in corporate clients approve payments and not invoices.

A few key points:

  1. Valid invoices are posted to a supplier account in the general ledger at the full amount demanded by the supplier (see above). If debit notes are raised, they are also posted.
  2. The amount due to the supplier is the balance on the supplier account in the general ledger – equal to invoices posted less debit notes posted.
  3. When it comes to paying a supplier, it is the outstanding and due balance on the supplier account that is settled. The due balance is calculated by taking all the items posted to the supplier account that are due on or before the current date, summing them up and subtracting payments already made.
  4. The result is a payable amount – a figure that will be settled to the supplier, usually via a payment run. That figure may be made up of many individual transactions (invoices, debit notes and credit notes). The key approval step is at the time of the payment run.

This makes supply chain finance and supplier financing tricky – since bankers and lawyers like to think in terms of transactions rather than balances.

Bankers purchase invoices not balances.

Debit notes do not conveniently fit into this model.

Whilst the corporate client may be settling a balance that is due to a supplier (invoices less debit notes),

bankers want the invoices that they have purchased to be paid in full to them.

This is also one of the two main reasons why many corporate clients operate an all-or-nothing model with suppliers when it comes to supplier financing.

  • Reason 1: if all the supplier invoices are financed by the same bank, there’s no need to try and unpick a balance that has been settled and split it into individual invoices.
  • Reason 2: nothing to do with debit notes, so just mentioned here for completeness: most general ledgers cannot handle more than one account payee record per currency – so this one record gets changed from the supplier banking details to the financier banking details and then all the money due to the supplier simply goes to the financier. Again, this avoids the need to try and split the payment into the individual invoices involved.

These two reasons mean SCF programs can lose a lot of agility. There’s no easy way to move suppliers between financiers, and no easy way to have dynamic management of funding, switching suppliers between financiers whenever needed (even daily) to optimise the use of limits and mix in own treasury funds as a funding source.

Myth #3: debit notes are somehow “outside” SCF

I made a statement earlier about “valid invoices always being approved”.

The key question for the accounts payable team when they are deciding what the supplier might be owed is not how much of the invoice to approve.

The key question is: how big should the debit note be?

Yes, that’s right. The full amount of a valid invoice is always posted to the ledger but if there is a reason to pay less than the full amount, then a debit note needs to be raised.

The questions being answered by the accounts payable team reviewing purchase orders, delivery paperwork and the invoice (ie: the 3-way match) are (a) whether a debit note should be raised and (b) how much it should be for?

For example, the invoice face value is 100, but there is a shortage on the delivery of value 5. So that means the invoice of 100 is posted to the ledger, and a debit note of 5 is created and posted – and 95 is the balance on the supplier account, which is what will be settled in due course.

As stated above, the general ledger is an account-based (ie: balance-based) system.

The supplier account is credited with the full amount of the invoices and debited with debit notes to reach the correct amount that should be paid to the supplier when a payment run is operated.

So what is the bank buying when it provides supplier financing?

  • The amount due under the invoice (without set off, counterclaim etc), or
  • the amount payable in respect of the invoice at the time of purchase, which could include amounts that are already planned to be netted from the invoice payment.

Is it an “approved payable” (an invoice that is net of specified debit notes) or is it an “approved invoice” (due at face value without deduction)?

This is a key documentary point and one which might not always be clear in the paperwork used to support supply chain finance. On the one hand, the bank wants to buy invoices free and clear of any debit notes. On the other hand, the corporate client may well want the ability to net pre-existing debit notes (at the time of financing) against those invoices.

Is the IPU for the same amount as the invoice? What exactly is the claim that the bank is buying and relying on here – the invoice or the payable amount (at IPU creation-time) in relation to each invoice? If the supplier financing is documented on the basis that the bank is buying the IPU (and not the invoice) – query the accounting treatment and whether the arrangement might then become financial indebtedness in the books of the corporate obligor?

Myth #4: debit notes specifically relate to invoices

Debit notes “sort of” relate to invoices – but this is an artificial construct and not one that is required in the ordinary course of business.

Companies typically want to collect claims (debit notes) against suppliers immediately. Why wait?

Invoices are posted with a due date (in the future) that reflects when the amount claimed by the supplier should be paid.

Debit notes are often posted with an immedate due date.

That means debit notes are usually collected by set off against the next amount due to be paid to the supplier – ie: as soon as possible. There’s no waiting.

That would be the case even if the debit note might relate to a delivery where the invoice(s) for that delivery are not due to be paid for many days.

And debit notes may also arise that are wholly unconnected with individual deliveries – for example, where there are volume rebates or programme-wide discounts to be recovered.

As mentioned above, settlements clear the due portion of the balance outstanding on the supplier account.

The settlement is calculated by adding up all the transactions that are due and ignoring future-dated transactions. The debit note is always due immediately and is included as soon as possible in any payment that is made to the supplier. The invoice may be due in the future and has to wait.

The idea that this specific debit note goes with that specific invoice is typically a myth when it comes to the practical reality of payables and settlements.

Myth #5: we can ignore debit notes in SCF

Most platforms ignore debit notes. These are “someone else’s problem”.

That’s why many accounts payable teams don’t like supply chain finance and specifically prefer to limit the size and scope of programs to a handful of large suppliers.

And many SCF platforms ignore debit notes completely.

Corporate clients are expected to provide them with a nice and tidy file of “approved payables” which are guaranteed to be paid in full no matter what else may be going on.

This makes life easy for the platform and easy for the bankers and lawyers involved.

But this also makes life complicated for the corporate accounts payable team.

If clean “approved invoice” or “approved payable” files are to be provided to the SCF platform, this typically means that:

  • debit notes cannot be posted to the general ledger in the usual way
  • debit notes have to be tracked “off-system” with recovery of the debit notes handled away from the settlements and payments process.

This can mean that many clients end up failing to collect all the debit notes that they might claim, or end up having to track debit notes manually in excel and away from the general ledger. This all leads to extra work for everyone, compromising financial controls and adding costs and complexity.

Debit notes and SCF – are there solutions?

Yes – it requires a modern approach to the way that the supply chain finance platform is developed and operates.

There are three approaches:

  1. Manual: Corporate accounts payable teams handle the debit notes away from the SCF program with manual tracking and recovery. SCF program is then simple as it is fed with an “approved invoice” file and all invoices are simply settled at their face value. Debit notes are tracked in a spreadsheet and suppliers are required by procurement teams to deduct them manually from future invoices.
  2. Delay debit notes: The corporate accounts payable team runs two processes:
    • Debit notes are allocated to invoices before they are included in the file transmitted into the SCF platform; early payments to suppliers are then net of debit notes that were outstanding at that specific moment; and then
    • Those invoices that are in the SCF program are then ring-fenced from the further application of debit notes against them by the accounts payable team – so the SCF financier is not exposed to any netting and will be paid in full. This requires some careful management of posting processes and dates applied to debit notes in the ERP to ensure debit notes are not deducted from financed invoices. One way to do this is to post debit notes with future due dates to defer processing and delay their recovery, leading to earlier due invoices that are financed always being paid in full; this is a cost to the business though.

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This article is written by our partner, Cobase

Oracle NetSuite is a cloud-based ERP platform designed to bring core business processes together in one environment, spanning financial management, reporting and consolidation as well as areas such as billing, projects, CRM and operations. Its multi-entity and multi-currency capabilities make it particularly relevant for organisations whose financial complexity increases as they expand internationally.

Around the core ERP sits a broad ecosystem of implementation partners, technology providers and integrated applications. As companies scale, this ecosystem becomes increasingly important. The ERP may sit at the centre of the finance architecture, but it also needs to connect effectively with the wider financial landscape, including banks, payment providers and other specialist applications.

One of the partners operating in this ecosystem is Novutech, a European finance transformation and NetSuite specialist supporting growing and international organisations with ERP implementation, integration and optimisation.

We spoke with Marta Obrador, Director, Business Transformation Practice & UK Lead at Novutech, about the type of companies NetSuite is best suited to, what makes ERP transformations successful and why bank connectivity should be considered as part of the wider finance architecture.

What type of companies are the best fit for NetSuite, and what makes the platform stand out from other cloud ERP solutions?

“For me, the best fit is really a company in motion: a business that is scaling quickly, expanding internationally or simply dealing with increasing financial complexity.

As organisations add entities, markets and currencies, finance needs more structure without creating more fragmentation. That is where NetSuite is particularly strong. It gives companies a single cloud-based foundation for financial management, consolidation and broader operational processes, while still giving them room to grow.

What matters is that finance teams gain the structure and visibility they need without having to redesign the whole environment every time the business changes.”

As an implementation partner, where do you add the most value and what do successful ERP projects tend to get right from the start?

“Our value lies in connecting people, processes and technology. An ERP project is never only about implementing a system. You also need to think carefully about how people will use it and how the processes around it should work.

The strongest projects usually get a few fundamentals right early on: clear ownership, good-quality data, the right integrations and, importantly, clear priorities.

I also think focus is critical. Companies sometimes try to solve everything at once. The better approach is to focus on what really matters, make good use of best practices and build a realistic path to adoption. That creates a much stronger foundation for the transformation.”

How are customer expectations around finance and treasury changing, and what does that mean for the role of the ERP?

“Finance is moving away from simply recording what happened in the past. Teams are increasingly expected to provide insight, support decisions and help shape what happens next.

That changes the role of the ERP as well. It needs to become a trusted platform for decision-making, not just a place where transactions are recorded.

At the same time, the ERP cannot operate in isolation. If you want finance processes to become truly efficient, it also needs to connect well with banks, payment processes and the wider finance technology landscape. That is where the conversation increasingly moves from ERP alone towards a more connected finance architecture.”

Where does ERP-to-bank connectivity typically become challenging, particularly for companies operating across multiple banks and countries?

“The real challenge is not usually connecting to one bank. Complexity starts to build when a company has several banks, entities, countries, accounts and payment formats, but still wants one consistent process.

Without the right connectivity layer, that complexity often ends up with the finance team. People are moving between bank portals, uploading or downloading files, fixing exceptions and reconciling transactions manually.

As the organisation grows, that becomes increasingly difficult to scale. You may have implemented a strong ERP, but if the banking side remains fragmented, a lot of manual work still remains. Ideally, you want to abstract that complexity away from the finance team.”

What does a strong ERP-to-bank setup look like to you, and what should companies consider when designing it?

“For me, a strong setup creates one trusted flow from payment creation in the ERP through to bank execution and then back into reconciliation and reporting.

Automation is obviously important, but it should never come at the expense of control. You still need clear visibility, the right approvals and enough flexibility to support how the organisation operates.

Companies should also think beyond the situation they have today. If you know you are likely to add entities, countries, banks or accounts, the architecture needs to support that. It is much better to design connectivity as part of the overall finance architecture than to keep adding separate bank interfaces over time.”

Why do you like working with Cobase, and what does combining your ERP expertise with Cobase’s banking connectivity bring to customers?

“What we like about the combination is that it extends the value of NetSuite beyond the ledger and into the day-to-day banking and treasury processes.

NetSuite provides the financial core, while Cobase creates a consistent connection between that core and the banking landscape. For internationally growing companies, that is particularly relevant because the number of banks, accounts and entities can increase quite quickly.

Customers should not have to choose between having a strong ERP and having strong banking connectivity. By combining our NetSuite and transformation expertise with Cobase’s multi-bank connectivity, we can help create a finance architecture that is much more connected and scalable.”

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