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Top 5 Scams in 2025
While reports and regulations help you understand fraud in theory, the scams unfolding in the real world often paint the clearest picture of what’s...
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Acoru : Jan 13, 2026, 11:13:18 AM
If you lead or support fraud prevention, it’s important to understand the trends shaping fraud in 2026. Attackers are gaining affordable access to increasingly sophisticated capabilities, payments are moving faster, and regulation is reshaping who carries responsibility when things go wrong. Together, these forces are changing how fraud works - and how it must be prevented.
This article explores the key fraud trends shaping 2026 and the structural changes influencing how fraud is executed and defended against. For fraud and risk teams, understanding these developments can help strengthen prevention strategies and prepare for the evolving fraud landscape.
Just as ransomware-as-a-service (RaaS) transformed ransomware from a niche cyber attack into a scalable business model, fraud-as-a-service (FaaS) is doing the same for scams, account abuse, and mule-enabled payments fraud. In 2026, FaaS continues to grow, enabling increasingly sophisticated and scalable fraud operations.
Toolkits for scam scripting, phishing infrastructure, mule recruitment, and account takeovers are already sold with onboarding guides, performance guarantees, and customer support. What continues to change is the scale and accessibility of these services, lowering the barrier to entry for fraudsters.
In FaaS, specialist operators build and maintain tooling, while affiliates handle execution. Success is measured by conversion rates, time-to-cash, and how quickly failed techniques can be swapped out. The result is more consistent fraud that’s repeatable, optimised, and increasingly resilient to takedowns.
Defenders against ransomware eventually learned that focusing solely on the payload (the ransomware itself) was insufficient. The same is now true for fraud. In 2026, effective fraud prevention depends on identifying shared enablers earlier in the lifecycle, think account preparation, behavioural priming, and mule recruitment signals that appear long before a payment is initiated.
As fraud adopts a service model, defence must move upstream to prioritise intelligence, early indicators, and cross-institution visibility over isolated transaction-level decisions.

The traditional image of a money mule as a knowingly complicit criminal is no longer sufficient. Money mules can play different roles in fraud operations, with participation ranging from deliberate criminal involvement to individuals manipulated through social engineering, financial pressure, or seemingly legitimate payment requests.
At one end are complicit mules: individuals recruited deliberately, paid for access to accounts, and often cycling through banks as part of organised operations. These actors remain a core enabler of large-scale fraud. But witting mules in younger age groups are becoming more common. Recent Barclays research found that 35% of Gen Z would consider moving money for someone they didn’t know, if they were offered a fee to do so.
Fraudsters are increasingly recruiting money mules through social platforms such as TikTok, Instagram, Telegram, and Discord. Recruitment messages are often embedded in comments, direct messages, or group posts that resemble legitimate side-hustles, crypto activity, or short-term “payment tasks.”
In 2026, fraud prevention will hinge on understanding mule risk as a lifecycle rather than an outcome. That means identifying behavioural signals, account changes, and social engineering indicators before transfers occur.

The democratization of powerful AI tools puts deepfake technology in the hands of many more would-be fraudsters. The growth in deepfakes has been significant. There was an increase from 500,000 online deepfakes in 2023 to about 8 million in 2025.
Voice cloning, short video impersonation, and AI-generated messages are increasingly used to simulate urgency, authority, or emotional closeness. A brief voice note that sounds like a manager, a family member, or a known contact provides fertile grounds for emotional manipulation.
Another trend shaping fraud in 2026 is how deepfakes are being deployed throughout the fraud lifecycle. Deepfakes will be more frequently used to bypass safeguards that were designed to raise the bar for attackers. Pre-recorded or semi-interactive video deepfakes can now be used to defeat basic liveness and video verification checks, particularly those that rely on scripted prompts or limited challenge-response. Fraudsters only need enough fidelity to clear automated or lightly supervised controls.
This also has direct implications for mule recruitment. Deepfakes allow recruiters to present themselves as legitimate employers, intermediaries, or authority figures without exposing real identities. A convincing video call or voice message can provide just enough reassurance for a prospective mule to proceed with a transfer.
Defending against ongoing deepfake-enabled fraud will require a shift in focus. Success will depend less on identifying fake content and more on recognising stress signals like urgency, deviation from normal decision-making patterns, and sudden changes in payment behaviour.

In 2026, financial services continue to absorb a disproportionate share of fraud activity. The sector offers the most reliable return on effort. No other industry combines high transaction velocity, instant liquidity, and complex regulatory obligations in quite the same way.
Within the sector, pressure is uneven. Crypto and digital asset platforms will continue to attract high levels of identity and document abuse, driven by fast onboarding, global reach, and limited recovery once assets move. One report found that 2025 saw a year-on-year rise of 38 percent in attempted fraud against crypto platforms. Lending platforms face persistent exploitation of synthetic identities and account farming. Traditional banks, sitting at the centre of payment flows, are impacted across all categories.

Instead, fraud defence is becoming increasingly layered, driven by a convergence of regulatory pressure, instant payments, and fraud techniques that leave little room for post-event recovery.
Regulatory liability is shifting upstream. APP reimbursement frameworks, expanding consumer protection obligations, and growing expectations that institutions apply “last-resort” brakes mean that allowing a payment to proceed is no longer a neutral act. In many cases, inaction now carries financial and regulatory consequences. Fraud teams are increasingly expected to intervene before harm occurs, not merely react after the fact.
At the same time, instant payments compress the fraud window to seconds. Once funds move, recovery is unlikely. Add deepfake-enabled social engineering and mule recruitment to the mix, and traditional transaction-only controls simply arrive too late. The fraud has already happened by the time the system flags it.
The result is a shift toward pre-fraud intervention with controls designed to detect risk during account preparation, behavioural change, and payment intent, rather than at execution alone. But this can’t come at the expense of customer experience. Blanket friction, delays, or unnecessary challenges introduce their own risks to customer attrition and operational overhead.
In 2026, effective fraud defence will focus on explainable, targeted intervention. That means layering behavioural intelligence, contextual risk signals, and account-level insights to apply friction only where it is justified. The objective is not to stop more payments indiscriminately, but to stop the right ones early, with clear rationale and minimal disruption.
The fraud landscape of 2026 will be shaped by imbalance. Attackers will continue to benefit from low-cost access to highly scalable tools, rapid iteration, and service-based criminal ecosystems that adapt faster than individual institutions can respond. Defenders, by contrast, operate under regulatory scrutiny, customer experience constraints, and shrinking reaction windows.
The organisations that fare best will be those that treat fraud prevention as a coordinated system that combines early risk detection, behavioural intelligence, explainable decisioning, and targeted intervention across the entire lifecycle. Layered defences with an increased emphasis on upstream, pre-fraud visibility will become the differentiator.
Payment fraud now forms across channels, relationships, and behaviours long before money moves. Acoru addresses this shift by enabling fraud teams to assess every relevant event across channels and accounts, not just those held within their own institution. By correlating signals early, risk can be scored, classified, and understood in context, rather than inferred after the damage is done.
As fraud continues to evolve, financial institutions need visibility into risk before fraudulent payments are initiated - not after losses occur.
See how Acoru helps financial institutions detect fraud before payments are processed. Book an Acoru demo.
Fraud-as-a-service (FaaS) is a criminal business model in which specialist operators build and sell scam toolkits, phishing infrastructure, mule-recruitment scripts, account-takeover kits, to affiliates who carry out the fraud, mirroring the ransomware-as-a-service model.
Recruiters use social platforms young people already spend time on TikTok, Instagram, Telegram, Discord, and frame mule activity as a side hustle or paid task. Barclays research found 35% of Gen Z would move money for a stranger for a fee, and most were unaware of the legal or financial consequences.
Deepfake-driven fraud attempts now account for roughly 6.5% of all fraud attempts globally, up from 0.1% in 2022, according to Sumsub, driven by voice-cloning and face-swap tools that no longer require specialist technical skill.
Because much of today's fraud is authorised by the genuine account holder, under manipulation, using a recognised device. Transaction-level and device-based controls were built to catch account takeover, not social engineering, which is why detection is moving upstream to account behaviour and pre-fraud signals.
Layered, explainable, account-level detection that flags risk during account preparation and behavioural change, not just at the moment of payment, while avoiding blanket friction that damages genuine customer experience.
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