Every year, fraud costs organisations the equivalent of around 5 % of their turnover, according to the ACFE’s flagship Report to the Nations. And the invoice, in the literal sense, remains one of fraudsters’ favourite points of entry. According to the 2026 Fraud Barometer, supplier fraud alone accounts for nearly 45 % of the cases recorded within organisations.
Fake invoices, fictitious suppliers, inflated amounts, duplicate payments: for a business that processes hundreds or thousands of invoices a month, spotting these anomalies with the naked eye is quite simply impossible. The good news is that AI trained on your own data can learn to detect fraudulent invoices and flag them before payment. Let’s take a practical look at how this works.
Invoice fraud: a costly and underestimated risk
Document fraud is by no means a minor issue. PwC’s Global Economic Crime Survey estimates that a large proportion of organisations have experienced at least one instance of fraud over the past two years. Yet a single fraudulent invoice that is paid can represent a loss of several thousand, or even tens of thousands, of euros. Added to this are the time spent investigating, recovery costs that are rarely recouped, and the climate of mistrust that such cases create within the organisation.
The problem affects businesses of all sizes. Large corporations are targeted because of the scale of the sums involved. SMEs, on the other hand, are targeted because their internal controls are often less rigorous and their accounting teams smaller. In both cases, the finance department finds itself on the front line, with limited resources to combat increasingly well-organised fraudsters.
The main types of invoice fraud
Before we discuss detection, it is important to understand what a fraudulent invoice looks like. The most common schemes are as follows.
A ‘fake supplier’ scam involves creating or impersonating a supplier in order to issue invoices for goods or services that were never delivered. A particularly dangerous variant of this involves changing the IBAN: the fraudster poses as a legitimate supplier and, just before the payment deadline, asks for payment to be made into a new bank account.
Overcharging discreetly inflates the actual amounts, often by small sums that go unnoticed but which add up over time. Duplicate invoicing involves submitting the same invoice twice – sometimes in paper form and then digitally – in order to secure a double payment.
Finally, fictitious invoices claim payments for services that never took place. All these types of fraud have one thing in common: they are hidden amongst the volume of transactions and rely on the fact that no human has the time to check everything.
Why traditional checks are no longer enough
Most companies rely on fixed rules: spending limits, lists of authorised suppliers, and dual approval for amounts exceeding a certain threshold. These rules are useful, but they have two major weaknesses.
They are, first and foremost, rigid. A fraudster who knows your thresholds will simply stay just below them. A rule such as «amount exceeding 10,000 euros» will never detect a fake invoice issued for 9,800 euros.
They then fail to pick up on weak signals. A supplier’s address that has been slightly altered, an IBAN that changes at the last minute, unusual formatting, or an abnormal invoicing frequency: these are all clues that no simple rule can capture.
This is exactly where bespoke AI makes all the difference. Instead of following pre-defined rules, it learns to recognise what distinguishes a valid invoice from a suspicious one, based on your real-world cases.
The concept: an AI that learns from your own invoices
The approach is based on fine-tuning, which involves training an AI model specifically on your data. The process can be summarised in three steps.
First, the model is provided with a set of invoices known to be valid. It is then provided with a set of invoices known to be fraudulent or abnormal. The model then learns the subtle differences between the two, whether these involve inconsistencies in amounts, suspicious suppliers, formatting discrepancies, unusual bank details or duplicates.
Once trained, the model analyses each new invoice and assigns it a risk level. Suspicious invoices are automatically flagged to your accounts team, which can then focus its attention where it is really needed. Humans always retain the final say. AI does not replace your oversight; it makes it much more efficient by filtering out the volume of invoices and prioritising high-risk cases.
Above all, every time a case of fraud is confirmed or ruled out, the dataset is enriched. The model is constantly improving and adapting to fraudsters’ new techniques – something a system based on fixed rules will never be able to do on its own.
The stages of an AI-based fraud detection project
At iterates, a project of this kind follows a clear and step-by-step process.
The first step is to define the scope and audit the data. We assess the volume of your invoices, their format and, above all, the availability of examples that have already been labelled as legitimate or fraudulent. This is the stage known as ‘data readiness’.
Next comes data preparation and labelling. We organise and clean the data, ensuring its quality and representativeness, as a model is only as good as the data on which it is trained.
The third stage is training and evaluation. We fine-tune the model, then measure its performance on real-world cases – monitoring, in particular, the detection rate and the false alarm rate – before any deployment.
Integration into your workflow is the fourth step. The template connects to your accounting software, ERP system or payment tool, ensuring seamless control without the need for re-entry and without disrupting your existing processes.
Finally, continuous improvement brings the project to a close and ensures its long-term success. We are establishing a feedback loop and a system for monitoring performance over time, to ensure that your AI remains effective against fraud, which is constantly evolving.
This phased approach – which involves starting with a targeted scope, measuring the impact and then scaling up – minimises risks and ensures genuine adoption by your teams.
Governance and compliance: a must for financial data
Invoices contain sensitive data: suppliers, amounts and bank details. They must therefore be handled with the utmost care, and data governance is at the heart of our projects.
Compliance with the GDPR requires the anonymisation or pseudonymisation of data, granular management of access rights, encryption and the logging of processing activities. Data sovereignty is also guaranteed: your model and data can be hosted on-premises or in a European private cloud, without ever being used to train a third party’s AI. Finally, traceability enables you to document every decision made by the model and understand why an invoice was flagged – a key factor for auditability and for building trust amongst your teams.
This section draws on our expertise in technical and security audit and on architectures designed from the outset to ensure privacy.
Tangible benefits for your business
Beyond security, fraud detection AI delivers very tangible benefits. It saves your accounts team valuable time, as they no longer need to manually check every invoice and can instead focus on genuinely suspicious cases. It reduces the risk of unauthorised payments, and therefore direct losses. Finally, it provides greater visibility into your supplier cash flows and strengthens the robustness of your internal control processes – a significant advantage in the event of an audit.
Beyond invoices: other types of fraud that can be detected
The same principle applies to many other situations. For example, it can detect duplicate payments, check expense claims, identify unusual transactions or verify consistency between purchase orders, deliveries and invoices. Once a history of normal and abnormal cases has been established, a bespoke AI system can learn to distinguish between them.
This approach is particularly relevant for the [financial and insurance sector], but it applies to any business that handles a large volume of transactions.
Take action
Do you process a large volume of invoices and want to secure your payments using AI tailored to your business? We can help you transform your invoice history into a bespoke, compliant and scalable fraud detection model.
Discover our [AI fine-tuning service in Brussels], or arrange a consultation with our AI experts to assess the feasibility of your project.
See also: Fine-tuning, RAG or a generic model: which AI approach should your business choose?