Grey-area decisions: the rise of ‘all-green’ fraud

December 2026  |  FEATURE | FRAUD & CORRUPTION

Financier Worldwide Magazine

December 2026 Issue

Fraud has become an increasingly complex challenge for organisations. As businesses strengthen their security controls, deploy artificial intelligence (AI) and introduce more sophisticated authentication procedures, criminals are also adapting their methods to exploit vulnerabilities that traditional detection systems may struggle to identify.

One emerging concern is ‘all-green’ fraud, where fraudulent activity takes place despite apparently successful authentication and legitimate risk signals. Rather than circumventing security measures through obvious technical violations, fraudsters may exploit genuine customer accounts, convincing identities or seemingly ordinary transactions to conceal their intentions.

The implications extend beyond financial institutions (FIs), encompassing payment providers, online marketplaces and businesses operating across increasingly interconnected digital ecosystems. A merchant may satisfy onboarding requirements while concealing the true nature of its activities. An account may exhibit normal transaction patterns while participating in a wider fraud network. A customer may successfully authenticate a payment without recognising that they have been manipulated into making it.

For organisations, such scenarios expose a fundamental weakness in conventional fraud prevention: the assumption that activity which satisfies established controls is necessarily legitimate. As AI becomes more deeply embedded in risk management, the challenge is shifting toward understanding the context, intent and relationships behind apparently legitimate activity.

To navigate these grey areas successfully, companies must pay close attention to how they identify, assess and respond to fraud, while balancing stronger protection against the need to preserve customer experience and commercial opportunities.

Beyond traditional controls

Conventional fraud prevention frameworks have historically relied on identifying anomalies, such as unusual transaction values, unfamiliar devices, suspicious locations, repeated authentication failures and discrepancies in identity information. These indicators can trigger additional scrutiny. While they remain important, they are far less effective when the activity itself appears entirely legitimate.

As all-green fraud continues to evolve as a key challenge for FIs, organisations are finding it increasingly difficult to identify scams involving customers who successfully authenticate transactions but are ultimately being manipulated by criminals. In such cases, there is a key distinction between verifying an identity and establishing the legitimacy of an action. A customer may use their usual device, enter the correct credentials and authorise a payment through the appropriate channels, yet still transfer funds to a criminal.

Authorised push payment (APP) fraud is one area of considerable concern. According to UK Finance’s ‘Annual Fraud Report 2026’, APP fraud losses reached £576.4m in 2025, an increase of 19 percent year on year. The number of recorded cases also rose by 7 percent to 248,070. These figures illustrate a wider challenge for FIs: criminals are increasingly targeting the people and processes surrounding a transaction rather than relying solely on technical vulnerabilities.

It is important to acknowledge, however, that all-green fraud is not a single, formally defined fraud category. Rather, it describes a broader detection problem that can arise across several fraud typologies, including social engineering, synthetic identity fraud, merchant fraud and the misuse of legitimate accounts.

For businesses, it is no longer simply a question of whether an activity has passed established controls. The more important question is whether those controls have adequately assessed the risks involved.

The challenge of hidden intent

Perhaps the most difficult aspect of all-green fraud relates to determining and classifying intent. An individual, merchant or business may provide authentic documentation, satisfy onboarding requirements and demonstrate legitimate activity, yet still engage in misconduct.

This challenge is particularly evident during account opening, when FIs often have limited information about an applicant’s intended activities and cannot predict future behaviour.

Synthetic identities create further layers of complexity. Criminals can construct convincing identities that satisfy verification requirements by combining fabricated and genuine information. Generative AI is making this problem more acute by facilitating the creation of realistic documentation, imagery and supporting material.

Such fraudulent activity is not always apparent at the outset. Malicious actors may allow accounts to operate normally for extended periods before using them to facilitate scams, receive illicit funds or move money between connected entities.

Merchant onboarding presents similar difficulties. A business may have a genuine legal identity, a functioning website and apparently legitimate products. Yet these indicators of authenticity may be used to conceal prohibited activities, misrepresent transactions or process payments on behalf of another entity. According to Visa, transaction laundering, identity theft and unfunded chargebacks are among the risks payment acquirers must consider when screening prospective merchants.

These risks can be particularly difficult to identify through isolated checks. Individual transactions may appear unremarkable while the broader pattern of activity raises questions about a merchant’s business model, ownership structure or relationships with other entities.

At the same time, unusual commercial arrangements are not inherently fraudulent. Legitimate businesses may employ complex corporate structures, process transactions across multiple jurisdictions or experience sudden changes in activity. Risk and compliance teams must therefore distinguish genuine commercial complexity from deliberate attempts to conceal misconduct. This requires a more comprehensive assessment of beneficial ownership, business relationships, transaction patterns and behavioural changes over time.

Equally important is recognising that onboarding should not be treated as a one-off exercise. The risk profile of a customer or merchant can evolve throughout the relationship, making ongoing assessment essential.

Understanding the wider context

Identifying fraud that appears legitimate in isolation requires organisations to consider the wider relationships between individual activities.

While a single transaction may fall within normal parameters, a series of transactions across multiple accounts may reveal an underlying pattern. Similarly, several apparently independent merchants may share ownership information, devices, infrastructure or payment destinations.

Network analysis can help establish these connections, allowing investigators to identify clusters of activity that might otherwise remain fragmented across different systems. An account receiving payments from multiple unrelated customers may initially appear to operate normally. However, connections to other accounts exhibiting similar patterns could indicate involvement in a wider money mule network.

Contextual intelligence can also reveal inconsistencies between a business’s stated activities and its actual behaviour. Changes in transaction volumes, customer demographics, geographic exposure or payment destinations may warrant further investigation, particularly when they occur alongside other risk indicators.

Much of this information may be siloed across separate departments, platforms or organisations. Fraud teams, anti-money laundering specialists, cyber security personnel and customer-facing employees may each possess information relevant to an investigation. Without effective coordination, the overall picture can remain obscured and malicious actors may go undetected.

Organisations should therefore seek to integrate relevant risk information across the customer lifecycle while establishing clear responsibilities for assessing and escalating suspicious activity.

AI and the detection dilemma

Given the volumes of data involved, AI is becoming an increasingly important part of the fight against all-green fraud. Machine learning systems can analyse substantial quantities of transactional, behavioural and network data, identifying patterns and relationships that conventional rules-based systems and human investigators may overlook.

Behavioural analytics can examine how an individual interacts with a digital platform, including transaction preparation and patterns of activity. This information may provide indicators of manipulation or account compromise even when authentication has been successful. Similarly, graph analytics can highlight relationships between accounts, merchants and other entities, supporting investigations into coordinated fraud.

As with all AI applications, however, effectiveness depends heavily on the quality and relevance of the available data. Models trained primarily on historical fraud patterns may struggle to recognise emerging techniques. Incomplete datasets, inconsistent reporting and poor integration between systems can further undermine accuracy.

There is also a risk that organisations become overly reliant on automated risk scores. A favourable score may encourage investigators to regard activity as legitimate, even where relevant contextual information is missing or has not been incorporated into the model.

Ultimately, AI should support rather than replace effective risk assessment. Organisations must establish clear governance arrangements covering model development, validation, performance monitoring and accountability. Particular attention should be given to false negatives, where fraudulent activity is incorrectly classified as legitimate, alongside false positives that unnecessarily disrupt genuine customers.

According to the UK’s Financial Conduct Authority, effective AI implementation requires consideration of the wider system, including data, processes, oversight and governance, rather than focusing exclusively on model performance. Human judgement therefore remains essential, particularly where decisions involve ambiguity, incomplete information or potentially significant consequences. Experienced investigators can assess commercial explanations, consider information unavailable to automated systems and determine whether further enquiries are warranted.

The key challenge lies in combining technological capabilities with meaningful human oversight, ensuring that automation improves decision quality rather than simply accelerating decisions.

Balancing fraud prevention and legitimate activity

Greater scrutiny inevitably creates challenges for organisations seeking to maintain efficient operations and a positive customer experience. Overly restrictive controls can delay payments, disrupt legitimate businesses and discourage customers from using digital services. Unnecessary account restrictions can also create reputational risks and increase operational costs.

Conversely, insufficient intervention presents significant risks. Customers can be exposed to harm and organisations may suffer substantial financial and reputational losses.

This tension is particularly pronounced in grey-area cases, where evidence may be incomplete and conventional risk indicators provide little justification for immediate action. Organisations should adopt a proportionate approach that reflects the nature and severity of the potential risk.

A more incremental response is often appropriate. Rather than treating every unusual activity as grounds for rejection, organisations can introduce graduated interventions such as enhanced monitoring, additional verification, targeted customer engagement and specialist review. Contacting a customer through a trusted channel may be appropriate where behavioural indicators suggest potential manipulation.

For merchants, enhanced due diligence may involve reviewing business activities, requesting supporting information or examining connections with other entities. Supported by clear escalation procedures and decision-making criteria, these measures help ensure employees understand when additional scrutiny is warranted and how concerns should be documented.

It is equally important for organisations to establish mechanisms for reviewing disputed decisions and identifying instances where legitimate customers or businesses have been incorrectly classified as high risk. The objective should not be to eliminate uncertainty entirely but to ensure that risk-based decisions are consistent, defensible and proportionate.

Strengthening governance and accountability

Addressing all-green fraud requires organisations to adopt a broader approach to the reassessment of their fraud strategies. Boards and senior management must understand where existing controls may provide incomplete assurance, particularly where successful authentication or favourable automated risk scores are treated as definitive indicators of legitimacy.

Organisations should also examine whether their internal structures enable relevant intelligence to be shared effectively. Fragmented and siloed systems can delay investigations and prevent emerging patterns from being recognised.

Looking beyond the green signals

As fraud techniques continue to evolve and technology develops at pace, the distinction between legitimate and fraudulent activity will become increasingly difficult to establish through conventional controls alone. All-green fraud illustrates how successful authentication, apparently consistent documentation and normal transaction patterns are not always sufficient to detect fraudulent activity.

Beyond strengthening individual security measures, organisations must place greater emphasis on context, behavioural analysis and the relationships between customers, merchants, accounts and transactions.

While AI can provide valuable support by helping businesses identify subtle indicators and investigate complex patterns at scale, it is not a panacea. Its effectiveness depends on reliable information, effective governance and experienced human judgement.

By moving beyond isolated checks and developing a more comprehensive understanding of risk, businesses can improve their ability to identify threats while protecting legitimate activity. In an environment where fraud increasingly appears ordinary, understanding what lies behind an apparently legitimate transaction will become an essential component of effective fraud prevention.

© Financier Worldwide

BY

Richard Summerfield