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 happening. They reveal where criminals are innovating, how victims get manipulated at scale, and which blind spots still exist across the financial ecosystem.
In 2025, those scams are more fluid, automated, and interconnected than ever. Understanding financial scams is about tracing the signals of how modern fraud actually operates. This article rounds up five of the top scams in 2025 and unpacks what they tell us about fraud’s evolution.
In the UK alone, fraudsters managed to scam people out of £629.3 million in the first half of 2025. Globally, fraud figures continue to trend in the wrong direction, and 2025 was no exception. Here are the top types of scams that add up to these enormous losses.
The defining feature of this year’s impersonation scams was improved AI-generated credibility. Fraudsters no longer need to rely on tone, timing, or linguistic tricks. With off-the-shelf AI tools, they can now look and sound like anyone, from a CEO to a compliance officer, even inside a live video call.
The technology stack behind these scams has matured dramatically. Generative adversarial networks (GANs) and diffusion models now create hyper-realistic facial movements and speech in real time with minimal latency. Open-source voice-cloning libraries can generate convincing speech from less than 10 seconds of sample audio. Combined with deepfake-as-a-service platforms and anonymised hosting, attackers can now scale impersonation across time zones and campaigns.
According to UNESCO, 37% of fraud experts have encountered voice deepfakes, and 29% have encountered video deepfakes. One report found $200 million lost to deepfake scams in Q1 of 2025 alone. In one glaring example, scammers in Brazil made millions of dollars using deepfakes of supermodel Gisele Bündchen on Instagram to trick many people into making small payments that they then laundered.
As a result, deepfake incidents have moved from isolated corporate wire-fraud attempts to a full-scale industrial model targeting employees, business partners, etc. For fraud and financial crime teams, the risk lies in how well these scams exploit the timing and authority structures of legitimate business workflows. The fraudster calls during a live deal closing, requests an urgent supplier payment, or joins a pre-scheduled meeting under a known calendar invite.
Detection now depends on behavioural coherence (does the requester’s behaviour align with prior patterns?) and cross-channel correlation (does the metadata of this call, device, or IP match historical baselines?).
Once seen as low-level phishing, fake job scams have evolved into one of the most effective mule recruitment methods of 2025. Fraudsters onboard unwitting mules into the financial crime ecosystem as account operators, test-transfer handlers, or payment intermediaries.
Fake recruiters create convincing company websites, clone job postings from real firms, and use AI-generated correspondence to walk candidates through onboarding. Many even issue offer letters, training tasks, and test payments to establish legitimacy.
In the US, the FTC received 19 percent more reports of fake online job scams in the first half of 2025 compared to the previous year. A recent Guardian investigation found Kenyans losing out on sums of over £1500 under the lure of securing non-existent work overseas. Employment scams cost Australians $4.2 million in January 2025 alone.
Detection is difficult because the initial transactions appear clean, with genuine KYC’d accounts, modest transfers, and consistent behavioural signals. The red flags lie deeper in shared infrastructure (device fingerprints, hosting environments, or timing patterns) that link clusters of accounts.
Among 2025’s most damaging frauds, few illustrate the fusion of psychological manipulation and industrial-scale laundering better than pig-butchering scams. Originally coined from the Chinese “sha zhu pan,” the term describes how fraudsters basically “fatten up” victims through months of trust-building before slaughtering them financially.
First comes emotional grooming. The scammer builds trust through weeks or months of seemingly genuine interaction on dating apps, social media, or messaging platforms. After establishing rapport, the conversation pivots toward a shared financial opportunity, which is usually a cryptocurrency, trading, or investment scheme.
In the UK, romance scams increased by 20 percent in the first quarter of 2025. Showing how severe pig butchering scams can be, US media reported that a Bay Area resident lost close to $1 million in a pig butchering scam in 2025.
Large fraud rings now run call-centre-style operations with specialised roles: hunters (who initiate relationships), farmers (who maintain contact), and cashers (who launder proceeds through layered mule accounts). Gen AI tools have supercharged this model by creating fluent multilingual chat scripts, automating responses across time zones, and generating deepfake photos or video clips to personalise the deception.
Pre-fraud signal intelligence helps discover the emergence of connected mule networks, observe rapid sequential onboarding of “investment” accounts, and monitor cross-channel anomalies that indicate shared infrastructure here.
The rise of instant payment platforms has transformed commerce. Systems such as Pix (Brazil), PromptPay (Thailand), UPI (India), Bizum (Spain) and Faster Payments (UK) now move money across accounts in seconds. This speed is great for convenience, but fraudsters exploit it too.
In the simplest form, a scammer basically replaces or intercepts a legitimate payment request. This could be a restaurant menu QR, a supplier invoice, or a delivery link. Then, they redirect funds to a mule account. In more targeted campaigns, attackers impersonate merchants or service providers through spoofed payment portals or Pay-by-Link pages that look indistinguishable from genuine ones.
The number of fraud cases on Brazil’s Pix already exceeded 28 million by September 2025. One interesting example in the UK linked a QR code used in a car parking scam to a global fraud network run from Dubai.
Because victims authorise the transfers themselves, the payments appear legitimate within traditional anti-fraud filters. For financial institutions, the structural challenge is that instant payments collapse the window for intervention. This is when having pre-fraud signal visibility will make all the difference: because real-time is already too late.
Social platforms have become one of the most powerful engines of financial fraud in 2025. On Facebook, Instagram, and TikTok, scam activity has surged. In fact, one report shows financial scams on Facebook increased by 340% in Q2 2025.
Scams on social media are fuelled by a blend of ad-targeting precision, viral design, and AI-generated content. A user sees a polished video ad or “sponsored post” promoting a government grant, a new investment app, or a celebrity-endorsed product. The visuals are often created using deepfake or AI-generated video. The link leads to a professionally designed landing page that mimics major media outlets or financial institutions.
From there, users are asked to share personal information, join a “VIP investment group,” or make an initial deposit through a redirected payment page. Once payments or deposits are made, funds flow through layered accounts of mule networks. Showing how bad these scams can get, one couple in Orange County, USA, lost their life savings this year after a scam that started on the Facebook Marketplace.
What makes this ecosystem particularly dangerous is its legitimacy by association. The ads often run through the platforms’ own advertising infrastructure, meaning victims engage with them inside feeds where they scroll past friends’ posts and legitimate brands. Fraudsters exploit the trust embedded in that context.
Across every major scam type in 2025, one constant is the use of money mule accounts. Every fraudulent payment, no matter how sophisticated the social engineering or AI behind it, ultimately converges on a receiving account designed to move funds faster than investigators can trace them.
Traditional controls focus on events like a suspicious transaction, a phishing alert, or a new payment request. But modern mule activity is rarely visible at the event level. It manifests as patterns that you can observe before a fraudulent transaction happens.
Acoru’s approach is built for this new reality. Its platform continuously classifies accounts using pre-fraud signal intelligence. These are signals that emerge long before a suspicious transaction is flagged. Instead of depending on static blacklists or post-event investigations, Acoru builds a living view of account risk that updates as activity unfolds in real time.
Acoru helps financial institutions see the network behind the node. The platform identifies the accounts most likely to become part of a mule chain before money ever moves. In a year defined by faster payments, smarter scams, and increasingly blurred human-machine deception, a shift from reaction to prediction is what stops fraud at its true source.
Want to learn more about what Acoru does? Talk to one of our experts.