The moment it became real
I still remember the first time I saw Snapchat and TikTok demonstrating face-swap technology for beauty filters and gimmicky fun.
Tech-savvy individuals quickly adopted this filter tech to transform their selfies into a flawless, animated version, complete with smooth skin and animated expressions, in seconds. What seemed like harmless entertainment was a glimpse into a powerful tool, one that now powers deepfake fraud and enables AI-driven scams across the world.
Today, fraudsters can snap a stranger’s LinkedIn headshot, run it through generative AI, and out comes a live, blinking video good enough to fool most Know Your Customer (KYC) platforms. What used to take days of Photoshop wizardry now took less than the time it took my coffee to cool. This increase in biometric fraud has changed digital onboarding security into a major weak point for many organizations.
A 2025 industry fraud report by Verrif noted that global fraud attempts have grown by 21% year-over-year, with deepfakes driving 1 in every 20 ID verification failures. Deepfakes aren’t just a novelty; they are a direct threat to business credibility.
All over the world, AI-driven fraud is escalating, posing a severe threat to the financial sector. For instance, in Kenya, journalist Japhet Ndubi lost his phone in July 2024, only to discover that fraudsters had used his biometrics to withdraw money and secure a loan, which took months to repay.
In Ghana, Joshua Kumah fell victim to a fake text message, losing control of his mobile banking account and SIM card, resulting in financial losses and the need to start anew. Just recently in Hong Kong, a finance worker transferred $39 million, thinking they were on a call with their CFO and colleagues. Turns out they were talking to deepfakes impostors.
These cases and many others highlight how AI tools enable fraudsters to exploit digital systems with alarming ease, and how AI-powered fraud detection tools must evolve quickly to protect financial institutions across the globe.
Picture a mid-level civil servant in Abuja. A fraud ring identifies and resolves a phone number to the mid-level civil servant, scrapes high-resolution photos from Facebook, and submits a SIM-swap request while he sleeps. The cloned SIM captures one-time passwords (OTPs); an AI-generated face defeats the “blink-and-smile” liveness test; a stolen Bank Verification Number (BVN) pulled via USSD completes the profile. By dawn, instant-loan apps are drained, and new credit lines are opened. This chain requires no elite hacking skills, just commodity AI tools and freely available loopholes.
This scenario mirrors real-world cases like Japhet Ndubi’s in Kenya, where fraudsters used stolen biometric data to perpetrate financial crimes. Such incidents highlight the vulnerability of biometric authentication when combined with tactics like SIM swapping, which saw a 1,055% surge in the UK in 2024, with similar trends in South Africa and Kenya.
At his desk in California, 82-year-old Steve Beauchamp watches a video of Elon Musk announcing a new investment opportunity. The voice is calm, the smile familiar — the world’s richest man himself promising lucrative returns. Convinced, Beauchamp wires $690,000 of his retirement savings over several weeks. The money vanishes.
In August 2024, The New York Times dubbed deepfake “Musk” the Internet’s biggest scammer. Victims like Beauchamp, and others such as Heidi Swan who lost $10,000 through a Facebook ad, describe the videos as indistinguishable from reality: “Looked just like Elon Musk, sounded just like Elon Musk.”
At a private-bank desk in Lagos, a familiar client voice requests, “Good morning, I’d like to move fifty-thousand dollars to my London account.” Except it’s not the client—it’s a real-time voice clone built from a podcast snippet. The banker runs a routine voiceprint check, which gives a green light. The funds are transferred, unrecoverable. Even Sam Altman has called reliance on voiceprints “crazy,” as AI has rendered them obsolete.
Voice cloning’s sophistication makes traditional voiceprint authentication ineffective, yet many financial institutions continue to rely on these outdated methods, unaware of their vulnerability to AI-driven attacks.
A CFO on holiday in Zanzibar opens an urgent email referencing last week’s board minutes. The syntax, tone, and even the CEO’s favorite catchphrase are spot-on, thanks to a large-language model. She wires supplier payments to a Kenyan account, unaware it’s fraudulent. INTERPOL now lists AI-crafted business-email compromise (BEC) among Africa’s fastest-growing cyber threats.
BEC attacks leverage AI to create highly personalized, convincing emails, increasing their success rate. The use of large-language models enables fraudsters to mimic executives’ communication styles, exploiting trust within organizations.
BEC attacks leverage AI to create highly personalized, convincing emails, increasing their success rate. The use of large-language models enables fraudsters to mimic executives’ communication styles, exploiting trust within organizations.
Related: AI and Frauds: How to Protect Yourself from Deepfake Video
Traditional security measures are increasingly ineffective against AI-driven fraud:
Left unchecked we will haul customers back to branch queues and notarised photocopies, reversing a decade of digital progress.
To counter AI-driven fraud, financial institutions must adopt advanced, multi-faceted strategies:
Related: How to Detect Deepfakes and Synthetic Identities.
Overcoming Implementation Challenges in AI Fraud Prevention
Implementing these fraud detection solutions in Africa faces challenges, including data scarcity, inconsistent and incompatible data, a shortage of AI specialists, and production-ready and training AI models from and for Africa is still out of reach for so many reasons. Initiatives like the African Data Collaborative, involving 15 East African banks, and synthetic datasets from companies like DataSynth address data issues. Cloud-based AI services and educational programs, such as those by the African Institute for Mathematical Sciences (AIMS), can mitigate infrastructure and expertise gaps and strengthen AI fraud resistance.
A Twelve-Month Fuse: Urgency in Combating AI Fraud
Fraud detection tools that once sat with nation-state hackers now fit in a browser window. In less than two years they will be mainstream, even for low-skill scammers. Cifas pegs Africa-wide fraud losses at roughly 10 billion dollars a year and rising; every month of delay compounds the bill. Globally, fraud loss is estimated to be $5.4 trillion, $185 in the UK, and a 9.9% increase in the cost of fraud for U.S financial firms.
At Youverify, we are integrating silos, anchoring liveness in hardware, and developing continuous AI monitoring. The industry must match this pace to prevent financial losses and preserve trust in digital financial services, which are critical for financial inclusion.
The wake-up call is ringing. We still have time to answer… just not much!