Fraud is evolving rapidly thanks to generative AI

Fraudsters now have access to technologies that were once the preserve of experts. It takes just a few clicks to generate:

• doctored images of products “supposedly” broken,
• forged supporting documents,
• and well-crafted synthetic identities

As TF1 explains, private sellers are now facing disputes based on AI-manipulated photos, with automated systems sometimes turning against the honest victim themselves.

One user interviewed recounted losing €180 due to an AI-generated image showing a supposedly broken vinyl record — an item that, by its very nature, does not break in that way. The platform’s automated system, however, ruled in favour of the fraudster, illustrating the vulnerability of traditional checks.

This phenomenon is part of a global trend: fraud involving doctored images is skyrocketing, with a 15% increase in falsified images in claims since the start of 2025.

Three major trends observed by Oneytrust

At Oneytrust, our experts have identified three major shifts:

1. More sophisticated fraud

Generative AI can produce highly realistic images, some of which are impossible to distinguish with the naked eye. Synthetic identities or invoices recreated pixel by pixel can fool traditional verification systems.

2. The industrialisation of attempts

What was once manual work is now automated. Fraudsters are now launching vast, coordinated campaigns, drastically increasing the volume of attempts detected on e-commerce sites.
This industrialisation is confirmed by the TF1 report, which warns of waves of scams potentially affecting millions of people.

3. New weak signals

Traditional methods are no longer sufficient. When evidence is generated by AI, behaviour, metadata and contextual inconsistencies become the new areas of analysis.

 AI: an essential tool for countering these new attacks

With fraudsters now using AI, anti-fraud solutions must evolve. That is why Oneytrust has integrated advanced artificial intelligence models at the heart of its technologies.

In particular, our systems enable us to:

Detect behavioural and contextual inconsistencies

Frequency of returns, unusual histories, multiple addresses or devices: the patterns speak for themselves.
Our solution cross-references these signals to identify abuse well before it becomes visibly repetitive.

Adapt in real time

Fraud methods change every week. Our models continuously recalibrate to keep pace with these changes and block emerging attacks.

Identify organised networks

By analysing data structure and correlating identity details, devices and network history, Oneytrust exposes organised criminal groups operating under different identities or accounts.

Tailor detection to your returns processes

Oneytrust adapts precisely to each retailer’s returns policies and internal workflows, incorporating their rules, thresholds and operational specifics.

The real challenge: maintaining trust

In this context, the issue is no longer simply about blocking fraud.
It is about protecting the relationship of trust between retailers and their customers.

Excessive suspicion can damage the shopping experience. Being too permissive can encourage abuse and undermine business models. The balance is therefore delicate — and AI plays a crucial role in accurately distinguishing between:

• honest shoppers,
• opportunistic fraudsters,
• and organised criminal networks.

Oneytrust is committed to maintaining this balance by combining cutting-edge technology with human expertise.

Fraud and AI: a huge challenge… but far from insurmountable

Artificial intelligence has profoundly transformed the landscape of reimbursement fraud. Whilst it provides fraudsters with new tools, it further enhances companies’ ability to detect and stop them.

With ongoing investment, increasingly sophisticated models and greater collaboration across the entire ecosystem, this battle is not only possible, but winnable.

Trust is one of the cornerstones of online commerce. Together, let’s continue to protect it.

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1. The new broad definition of an ‘AI system’

The text defines an AI system very broadly as a ‘machine-based system, operating with varying degrees of autonomy, capable of adapting after deployment and which, for explicit or implicit purposes, deduces from data how to generate results (predictions, content, recommendations, decisions) that influence physical or virtual environments’.

This broad technological scope is complex to implement operationally, but the market agrees that it encompasses sophisticated approaches such as machine learning (ML), hybrid expert rule + machine learning approaches, and advanced optimisation.

With the proliferation of AI use by fraudsters, trusted anti-fraud companies such as Oneytrust will need to further develop their range of AI solutions to power their scoring, behavioural alerting and anomaly analysis engines in order to keep up with the times and identify increasingly sophisticated fraud patterns.

2. Risks: where does fraud detection fit in?

The AI Act classifies certain uses as ‘high risk’ (critical biometrics, employment, education, access to essential services such as credit scoring, health insurance pricing, etc.). These systems are authorised but must be accompanied by a very sophisticated risk management system and an assessment of their compliance before being placed on the market.

One notable exception is that AI used to detect financial fraud is not automatically considered ‘high risk’. This reduces the direct regulatory burden, but does not remove the expectations of transparency, data quality and human oversight that regulated institutions contractually pass on to their subsidiaries and suppliers (such as Oneytrust).

3. What are the key dates to remember?

The main requirements of the AI Act will come into force in stages. Here are the key dates to remember:

• 2 February 2025: entry into force of the ‘unacceptable risk’ prohibitions + AI literacy requirement (awareness/training for staff involved in AI).
• 2 August 2025: obligations for general-purpose AI models (GPAI) begin to apply (generative AI).
• 2 February 2026: European deadline for certain implementing acts (post-market surveillance plans).
• 2 August 2026: obligations for high-risk AI systems.
• August 2027: general application of all provisions of the regulation.

4. Why prepare even if you are not ‘high risk’?

Fraudsters are becoming industrialised with AI. Deepfakes, fake documents, automated attack scripts, automated use of synthetic identities: fraud is scaling up. Those involved in the fight against fraud must adapt quickly to these new trends.

Although fraud prevention is not explicitly considered high risk, it is essential to ensure that solutions respect the fundamental rights and privacy of customers and end users, whether in accordance with the GDPR or the AI Act. In addition, the expectations of regulated partners (mainly in the banking sector) are increasingly high, as they are subject to sector-specific requirements that demand traceability, good documentation and heightened vigilance regarding the quality of the data used to satisfy their supervisors.

5. The Oneytrust response

Oneytrust provides identity verification and fraud detection solutions for e-merchants, fintechs and banks, relying on AI solutions coupled with our 25 years of human expertise in fraud prevention to detect synthetic identities, transactional anomalies and risk signals in real time. We are members of the BPCE Group, which drives us to high standards of compliance and model governance.¹

As with the GDPR, we did not wait to cultivate our expertise on the AI Act and have been raising awareness and training our staff on these new compliance issues for several years. Contact us if you would like to learn more about Oneytrust’s vision for AI compliance and risk management!

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That something is weak signals.

What is a weak signal in digital identity?

A weak signal, as its name suggests, is not a red alert. It is a detail. A micro-anomaly. An almost invisible (or rather ignored) inconsistency taken in isolation, but which takes on its full meaning when correlated with other elements.

For example:

• a known device… but whose technical fingerprint has changed slightly;
• a connection from a usual city… at a completely atypical time;
• correct keystrokes… but with an unusual rhythm;
• a smooth user journey… but traveled at a speed too fast to be human.

None of these elements alone is sufficient to justify blocking an event. But together, they tell a different story from what the declarative data would suggest.

Why strong signals are no longer enough

Historically, security relied on binary elements: correct password, validated SMS code, recognized device.

The problem? These proofs can be stolen, intercepted, or simulated.

Malware, phishing, massive data leaks, and automation tools have turned credentials into mere raw material for fraudsters. Even strong authentication is no longer foolproof against real-time proxy attacks or remote takeover.

The result: a user can tick all the boxes for legitimacy while still being a fraudster. This is precisely where weak signals become decisive.

Weak signals reveal behavior, not a declared identity

A declared identity can be falsified. Behavior is much more difficult to fake.

Weak signals allow us to answer not the question “Is this information correct?” but “Does this behavior resemble that of a legitimate human being in this specific context?”

This leads us to a dynamic interpretation of digital identity, based on the consistency of habits, the continuity of interactions, and the logic of sequences of actions.

From isolated events to behavioral history

The true power of weak signals lies not in a snapshot, but in duration.

A strange connection may be insignificant. Two anomalies close together begin to raise questions. A series of micro-deviations paints a picture of fraud.

It is by connecting these dots that we move from one-off checks to continuous behavioral monitoring. Digital identity ceases to be a state and becomes an evolving probability.

The challenge: detecting without degrading the experience

Exploiting weak signals does not mean increasing friction.

The goal is to adapt the level of vigilance: request additional verification only when the risk increases, silently monitor suspicious behavior, and trigger enhanced controls on sensitive actions.

In other words: make security proportional to the actual risk.

When email addresses become a weak signal

Email addresses are often perceived as simple contact identifiers. However, they contain a wealth of information that is underestimated when analyzed from a semantic and behavioral perspective.

Let’s take two examples: marie.dupont@yahoo.fr and ma.rie_du.pont1567@gmail.com.

Both addresses are technically valid. Both can pass standard checks. But they don’t tell the same story.

Semantic analysis involves observing the structure, logic, and consistency of an email address in relation to the context declared by the user.

Discreet but telling clues may emerge: domain name, username composition, presence of promotional keywords, consistent or inconsistent series of numbers, unusual complexity, or recurrence of similar patterns observed in previous fraud cases.

Taken in isolation, none of these elements proves fraud. But they may indicate that an address was created quickly, en masse, or for purely transactional rather than relational purposes.

A real digital identity often leaves traces of continuity. Conversely, in many fraud scenarios, the email address is a disposable tool designed to pass a technical check, not to reflect a lasting identity.

Semantic analysis therefore allows us to answer a subtle question: does this address resemble a human point of contact or a functional artifact created for a specific scenario?

Once again, the value comes from correlation: unusually structured email, rapid account creation, device recently seen on other accounts, automated browsing. It is the aggregation of these weak signals that reveals a credible risk.

From raw data to contextual intelligence

Weak signals already exist in your systems: technical logs, browsing data, device metadata, action sequences.

The difference lies not in the quantity of data, but in the ability to correlate it in real time, contextualize it according to the journey, learn from past patterns, and detect subtle deviations.

Digital identity is no longer a file, it is a flow

For years, we treated identity as a file: static information to be verified once and for all.

Weak signals are forcing us to change our paradigm. Identity is becoming a continuous stream of behaviors, which are confirmed or degraded through interactions.

Conclusion

Major frauds rarely hide behind big anomalies. They are concealed in the details that we don’t look at.

Learning to read weak signals means accepting that the truth is no longer found in a single piece of evidence, but in the accumulation of micro-clues. In a world where identities can be fabricated, stolen, or rented, this subtle reading becomes one of the pillars of digital trust.

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• Easy to hijack: A stolen cookie can be enough to steal someone’s online identity.
• Rich in sensitive data: Usernames, sessions, preferences… all this information can be exploited for targeted attacks.
• Invisible to the user: Most internet users are unaware of the scope of the information stored, which facilitates fraud.

The impact on your brand

• Advertising fraud: Falsified cookies artificially inflate impressions and clicks, distorting your KPIs and increasing your costs.
• Session hijacking: Hackers can access customer accounts, generating fraudulent transactions.
• Reputational damage: A vulnerability exploited via cookies can damage your image and your GDPR compliance.

Examples of techniques used by fraudsters

• Session sniffing: Interception of cookies on unsecured websites.
• XSS injection: Malicious scripts to steal cookies.
• CSRF: Fraudulent actions performed without the user’s knowledge.
• Predictable credentials: Exploitation of weak algorithms to guess sessions.

How to protect your digital ecosystem?

• Proactive monitoring: Detecting anomalies in cookie-related behaviour.
• Advanced anti-fraud solutions: Identifying falsified or cloned cookies.
• Awareness and transparency: Informing your users and building trust.

Conclusion
Cookies are valuable tools for improving user experience and optimising marketing strategies. However, their exploitation by fraudsters poses a major risk: session theft, identity theft, advertising fraud, etc. These threats can impact customer confidence, distort your metrics and damage your image.

To remain competitive and protect your digital ecosystem, it is essential to take a proactive approach: secure your customer journeys, integrate anti-fraud solutions, and inform your users. Protecting cookies means protecting your business and your reputation.

Contact us to secure your customer journeys and build trust in your services.

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Unlike traditional identity theft, where the fraudster steals an entire real identity, a synthetic identity is a hybrid combination. It combines authentic data (in the United States, this is the social security number, while elsewhere it is usually the national identity document, date of birth and postal address) with invented information (usually contact details). This combination creates a seemingly coherent profile, especially when only the so-called authentic data is verified/analysed (as in a banking KYC process, for example).

As a result, traditional systems, which are often calibrated to validate an identity by digitising traditional practices (‘your papers, please’), miss the mark.

Why institutions are vulnerable ?
Fraudsters exploit the trust placed in partially accurate data. In the United States, with a valid official ID or minimal credit history, synthetic identities pass the initial verification stages. The fraudster can then ‘mature’ their identity: open a bank account, take out small loans, make regular payments… until they obtain a solid credit rating. This patience pays off: once credibility is established, the fraudster takes out a larger loan and then disappears. This is known as ‘bust-out fraud’.

The cost is considerable: across the Atlantic, synthetic identity fraud already accounts for several billion dollars in losses each year, according to estimates by major firms. In Europe, the figures are beginning to follow the same trend, particularly with the rise of 100% digital processes.

The limitations of traditional approaches
Traditional controls – document verification, historical scoring, ad hoc analysis of events or transactions – struggle to detect these hybrid profiles. Weak signals (email username, telephone number attributes, presence of ‘digital footprints’) can only be detected through rare expertise. Furthermore, current regulations focus almost exclusively on KYC compliance. However, control and fraud prevention are two distinct things. The former encompasses all the characteristics/steps that allow access to a service… but because all of this is exposed, it also makes it possible to understand and therefore circumvent the measures.

Towards a new approach to detection
Faced with this threat, companies must change their paradigm. The future lies in solutions capable of:

• Going beyond ‘declarative’ data by being able to add additional information.
• Cross-reference dynamic signals (browsing behaviour, machine fingerprint, location) with static identity attributes.
• Detect relational inconsistencies between different identities: shared telephone numbers, recycled addresses, accounts linked to the same device.
• Use artificial intelligence to analyse massive volumes of data/information in real time and identify patterns invisible to the human eye.

These approaches transform detection: moving from simple documentary/contextual validation to a holistic and behavioural view of the user.

A strategic challenge for digital players
Synthetic identities are not a marginal phenomenon: they directly threaten profitability, regulatory compliance and customer trust. For banks, e-merchants and insurers, investing in advanced prevention technologies is no longer an option: it is a condition for survival in the digital economy.

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Adopted in 2016 and effective since 25 May 2018, the GDPR has profoundly transformed the way companies collect, process and secure personal data.

Its objectives:

• Strengthen the rights of European citizens.
• To make companies more accountable in their use of data.
• To harmonise rules within the European Union.

For the anti-fraud sector, the impact is significant: the detection of anomalies and suspicious behaviour relies on the analysis of many types of personal data (identity, payment methods, transaction history, etc.). The GDPR therefore requires a balance between security, performance and respect for individual freedoms.

2. A legacy of innovation and security with our historic CNIL authorisation

Long before the GDPR came into force, Oneytrust had already adopted this approach to compliance.

In fact, we benefited from a historic agreement (FIA-NET in 2005 and Oney Tech in 2013) with the CNIL, authorising the pooling of our customers’ data, mainly in the e-commerce sector, subject to guarantees of privacy. Oneytrust has thus become the only French player to benefit from such extensive experience in data sharing, spanning more than 20 years.

This authorisation, developed in collaboration with the supervisory authority, has made it possible to:

• To significantly streamline the journey time for customers already known in our database,
• To identify anomalies and fraud trends more effectively by relying on velocity and using our fraud experts to make decisions,
• To protect both consumers and merchants,
• while ensuring a high level of personal data security.

This expertise was even cited by the CNIL in its 2021 White Paper, proof of the added value and pioneering nature of our approach.

3. A stronger internal GDPR culture

The implementation of the GDPR marked a new stage: beyond technical measures, we focused on fostering an internal culture of compliance.

At Oneytrust, every employee plays a role in data protection, thanks in particular to:

• mandatory annual training,
• regular awareness campaigns and monthly newsletters,
• the systematic involvement of our Data Protection Officer (DPO) and compliance team in our projects,
• the implementation of a data protection impact assessment on all our projects that have a structural impact on personal data,
• a continuous improvement process to adapt our practices to regulatory changes.

This collective effort means that compliance is not only perceived as a legal and regulatory issue by our teams, but more as a shared value in our daily work, by design, and acts as a differentiating factor for Oneytrust in a landscape of anti-fraud players, many of whom are based outside Europe.

4. Enhanced security thanks to our membership of a major banking group

Our membership of the BPCE group, France’s second-largest banking group, imposes particularly high standards in terms of security and compliance. Banking institutions are subject not only to horizontal regulations but also to numerous sector-specific regulations due to the critical nature of their activities.

This is reflected in particular by Oneytrust’s active participation in the Oney group’s DPO community, where we share best practices and regulatory monitoring, the implementation and execution of ongoing internal controls (quarterly, half-yearly, annual) that require the commitment of all our teams, and the integration of banking standards into our contractual processes (GDPR annexes, security clauses). All this is coupled with advanced technical measures: regular security tests, data flow security, etc.

Oneytrust’s commitments reinforce the robustness of our systems and the trust placed in us by our customers, placing compliance by design at the heart of our decision-making processes and projects.

5. Data protection: a key lever in the fight against fraud

Trust is the cornerstone of the fight against fraud. Consumers and businesses alike must be assured that their data is protected, even in the most sensitive contexts.

At Oneytrust, we consider compliance with the GDPR to be a strategic weapon. We see it as an opportunity: an opportunity to legitimise our role as a trusted intermediary, to ensure that the fight against fraud is conducted in a manner that respects individual freedoms and privacy, while also preparing for the future.

Indeed, with the proliferation of artificial intelligence applications, new challenges are emerging. In order to counter the increasingly widespread use of AI by fraudsters, it has become essential for those involved in the fight against fraud to increase their use of AI. This allows us to remain adaptable and effective in the face of these new fraud trends.

However, AI makes extensive use of data, particularly personal data, and requires continuous development of protection and compliance measures. This is why the CNIL (the French supervisory authority responsible for enforcing the GDPR) has already published several series of recommendations on the proper handling of personal data when using AI. These are new challenges that need to be fully taken into account by those involved in the fight against fraud!

In conclusion, for more than 25 years, Oneytrust has placed data protection at the heart of its anti-fraud measures. The GDPR should not be seen as a constraint, but rather as an opportunity to strengthen trust, protect our customers and guarantee ever more effective and responsible solutions.

In a context marked by the rise of artificial intelligence, this requirement has never been more essential. It guides our daily commitment: combining security, performance and respect for fundamental rights. Please do not hesitate to contact us if you have any questions about the security of our fraud prevention processes and solutions, including the protection of personal data!

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It all starts with fake advertisements on a reputable marketplace, offering an educational game (story box) called MERLIN at a slightly discounted price compared to the competition. The buyer, enticed, places an order. But behind this transaction lies a well-oiled mechanism: the fraudster uses the customer’s details and a stolen bank card to purchase the product from a legitimate retailer – one of our partners specialising in childcare products.

The result: the customer receives their product, convinced they have got a good deal. But the retailer faces a bank dispute and will never be paid.

Rapid detection, immediate response

Thanks to a sudden spike in sales of this product, our models detected an anomaly: our analysts were immediately alerted. Expert rules were put in place for our teams in less than four hours! Less than 12 hours after detection, the network was neutralised. This efficiency is based on the synergy between our Data department and our Investigations unit.

• Investigators analysed weak signals to reconstruct the modus operandi.
• Data analysts transformed these insights into enhanced security rules capable of blocking future attempts and monitoring network developments.

In-depth investigation: knowing your enemy

The story does not end there. Our experts continued their investigation to gain an in-depth understanding of how the network operated:

• Four ‘professional’ shops were identified on the marketplace, all registered under different company names – identity theft.
• Active since March 2024, they generated hundreds of orders, exclusively for educational games.
• The fraud network manipulated the platform’s algorithms to push his products to the top of search results, notably by offering lower prices than others.

The result: a few hundred pounds in damages, but thousands of pounds in fraud prevented. Enhanced security rules, seasoned investigators, and fraudulent shops closed. A healthier marketplace.

The return of the Merlin network

In February 2025, the network returned, more cunning than ever. This time, it targeted two other partners via a new marketplace, with different products: small electrical appliances. The cat-and-mouse game lasted until May 2025, with the opening of fake shops, changes in products and targeted retailers.

This new round required:

• Constant monitoring,
• Regular updates to security rules,
• Ongoing training for our investigators,
• And above all, close collaboration with our merchant partners, who are fully committed to this fight.

How can you protect yourself against triangular fraud?

Here are the key levers for anticipating and blocking this type of fraud:

1. User awareness: Inform buyers and sellers about warning signs: prices that are too low, suspicious contacts, unusual payment requests.
2. Identity verification: Strengthen controls on platforms: supporting documents, cross-checks, etc.
3. Transaction monitoring: Implement analysis tools to detect abnormal behaviour and weak signals.

Triangular fraud is a growing threat. But with the right tools, constant vigilance and active collaboration between all e-commerce players, it can be effectively contained.

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a) Security and protection of citizens

The primary purpose of digital identity is protection. Users whose identities are stolen suffer dramatic consequences: loans taken out in their name, administrative disputes, damaged online reputations. Having a robust digital identity based on reliable technical and behavioural signals helps limit these risks.

b) Trust and fluidity of exchanges

For a company, accepting a new customer involves a risk. Digital identity provides the necessary elements to establish a trust score: is this a real, consistent person who has been active for a long time? This trust then facilitates exchanges, reduces friction (simplified KYC, smooth user journey) and improves the customer experience.

c) Sovereignty and independence

Today, proving one’s identity online often involves major private players (connection via Google, Apple or Facebook). However, delegating this strategic function to a few companies poses a problem of sovereignty. European states are seeking to regain control in order to avoid excessive dependence and guarantee data protection.

d) Inclusion and equal access

Digital identity must not become a factor of exclusion. People who are digitally excluded (the elderly, rural areas, vulnerable populations) must have access to simple and accessible solutions. Equity requires an inclusive design of these tools.

2. Future prospects

By 2030, digital identity could undergo three major developments:

Increased standardisation: adoption of international standards enabling seamless recognition between countries and services.

Biometric integration: enhanced association with biometric fingerprints (voice, face, fingerprint) to further secure access.

Enhanced user control: emergence of solutions where users decide which elements of their identity to share, depending on the context (e.g. proving they are of legal age without revealing their date of birth).

In a nutshell…

Digital identity is no longer a technical curiosity. It has become the beating heart of digital societies. It protects, streamlines and empowers, but also raises questions about surveillance and sovereignty.

By understanding it, decision-makers and citizens have an essential lever for navigating a world where the virtual and the real are inseparable. Ultimately, digital identity is the identity card of the digital age: intangible but decisive, invisible but unavoidable.

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1 . Defining digital identity:

Unlike civil identity, which is established and certified by the state, digital identity takes many forms. It is based on four main dimensions:

• Declarative data: surname, first name, email address, telephone number, address. This information is provided voluntarily, but can be misused/“imitated”.
• Technical fingerprints: each connected device generates a unique signature (IP address, configuration, sensors, time zones, cookies). These signals, invisible to the naked eye, become valuable clues for recognising a user.
• Behaviour: typing speed, connection times, location, frequency of service use. These elements are more difficult to falsify and constitute a behavioural signature that is almost biometric.
• Social and economic interactions: history of contact, payment and browsing data use. This data builds a coherent narrative… or, conversely, reveals inconsistencies typical of fraudulent events.

2. An identity in flux

Unlike a national identity card, which is fixed at the time of issue, digital identity is dynamic. Every connection, every payment, every interaction enriches this digital portrait. This dynamic provides robustness – it is difficult to simulate a consistent digital life over time – but also raises ethical questions:

How much data should be collected without infringing on privacy?

How can we ensure that users retain control over their digital identity?

What safeguards should be put in place to prevent widespread surveillance?

The GDPR in Europe and the concept of ‘self-sovereign identity’ attempt to provide answers. But the balance between security, fluidity and respect for individual freedoms remains fragile.

3. Why has this concept become so important?

Two major trends explain its growing importance. .

• Widespread digitalisation: smartphones have become our universal access point. From public services to healthcare, everything is now digital. Initiatives such as France Identité and the European eIDAS 2.0 regulation aim to create digital identities that are recognised at national and supranational level.
• A boom in 100% web-based crime: identity fraud is skyrocketing. The concept of ‘synthetic identity’ illustrates this trend. Fraudsters assemble pieces of real and invented data to create fake profiles capable of fooling traditional controls.

In this context, the ability to assess the credibility of a digital identity is becoming strategic for banks, insurers, retailers and also for governments.

In a nutshell…

Digital identity is constantly evolving: shaped by our usage, regulated by legislation, scrutinised for its security and commercial value. But this dynamic is not without its challenges. In the second part, we will address the multiple issues it raises – security, trust, sovereignty, inclusion – and the prospects that could redefine our relationship with identity by 2030.

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Triangular fraud is a complex and ingenious method of fraud that combines elements of real and synthetic identities to create a network of fraudulent transactions. This type of fraud relies on the interaction between three parties: the fraudster, the legitimate seller and the unwitting consumer, in order to divert resources discreetly and effectively.

How triangular fraud works:

The triangular fraud process can generally be broken down into four steps:

1. Creation of multiple identities: fraudsters begin by creating synthetic identities by combining real and fictitious information from real customer information received during purchases on their website. This allows them to generate credible buyer profiles that can interact with sellers without arousing suspicion.
2. Ordering products: using these identities, fraudsters place orders with legitimate sellers. Payment is often made with stolen or misused credit cards.
3. Reshipping products: the purchased products are shipped to intermediate addresses or directly to the ‘real customer’ depending on the method used. At this stage, fraudsters may also sell the stolen products to real buyers via online platforms at a reduced price.
4. Cash-out: Fraudsters cash out payments from genuine buyers, while the original seller remains unpaid or faces a chargeback.

Why is triangular fraud effective?

Triangular fraud is particularly effective because of its networked approach, which allows fraudsters to separate the elements of the transaction. The initial seller believes they are interacting with a legitimate buyer, while the final buyer thinks they are getting a product at a bargain price. This separation makes detection difficult, as individual transactions may appear legitimate because the information used is real, long-standing and sometimes even shows healthy activity. Traditional security rules are unable to detect these weak signals of fraud.

The MERLIN network

At Oneytrust, we are constantly detecting new fraudulent schemes. One of the most telling recent cases is that of a network we have named ‘Merlin’ because it used products from this brand to carry out its attacks. Detected in June 2024, this network perfectly illustrates the complexity and evolution of today’s threats.

It all started with an alert about unusual purchases: a sharp increase in orders for Merlin educational games, worth less than £100, from a partner specialising in childcare products. This sudden spike immediately caught our attention.

Our experts quickly spotted similarities between the orders, suggesting the existence of an organised network. However, certain elements were puzzling: the digital identities appeared reliable, and some profiles even belonged to real customers – but with a different email address. This was a clear sign of identity theft, which is rare for such small amounts.

To remove any doubt, we contacted the customers concerned. All confirmed their identity… but denied having made these purchases. The fraud was now certain. A thorough discussion with one of them revealed the key: he had indeed purchased the product, but on an online marketplace, not from our partner. The evidence provided enabled us to identify the fraudulent shop and understand the modus operandi: a triangular fraud.

Once the pattern had been established, our analysts listed the common signals associated with suspicious orders and created specific security rules. Thanks to these targeted actions, reinforced by direct checks with customers, we were able to block transactions linked to this network. The result: in less than 12 hours, the ‘Merlin’ network was neutralised.

This case shows how essential the responsiveness and expertise of our teams are in stopping new fraud networks as soon as they emerge. And the story of ‘Merlin’ is only just beginning… we will tell you more very soon.

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