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.
Three AI-Fraud Scenarios Every banker should know
- Heist in the small hours
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.
- Deepfake “Elon Musk”: The Internet’s Biggest Scammer
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.
Except it was never Elon Musk. It was a deepfake.
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.”
- A Banker’s Nightmare Call
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.
- Email from “The Boss”
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.
Related: AI and Frauds: How to Protect Yourself from Deepfake Video
Why Traditional Security Measures are Failing Against AI Fraud
Traditional security measures are increasingly ineffective against AI-driven fraud:
- One-trick liveness tests, such as the blink-and-smile test, are easily bypassed with tools that can create deepfakes that pass these tests. This is akin to entrusting security to an untrained guard.
- Voiceprints, once reliable, are now vulnerable to real-time cloning with off-the-shelf kits. Audit logs may record deepfakes instead of genuine interactions.
- SMS OTPs, a common fallback, are compromised by SIM swapping, with cases surging by 1,055% in the UK in 2024 according to cifas and rising in South Africa and Kenya, rendering this channel insecure.
Left unchecked we will haul customers back to branch queues and notarised photocopies, reversing a decade of digital progress.
AI Fraud Solutions: Rebuilding Trust in the Digital Age
To counter AI-driven fraud, financial institutions must adopt advanced, multi-faceted strategies:
- Zero-trust data strategy: Integrating cybersecurity, compliance, and transaction-monitoring logs eliminates blind spots that AI could exploit, ensuring no part of the system is automatically trusted.
- Continuous, multi-modal proof of life: Combining passive face analytics, depth sensors, device attestation, and behavioural biometrics ensures robust authentication. For instance, a deepfake video may pass facial recognition but fail a behavioural check analyzing typing patterns.
- Federated intelligence: Sharing anomaly signals across units internally and, potentially, across institutions without exchanging raw customer data enables collective learning from continent-wide fraud patterns, thereby enhancing detection capabilities.
- Red-teaming with deepfake kits: Periodic testing with the latest deepfake tools ensures defenses remain robust. If internal testers can breach systems, so can external fraudsters.
- Explainable AI for analysts: Clear explanations of AI alerts empower analysts to identify new fraud patterns, addressing the “unknown unknowns.”
- Agile regulation: Africa today does not have a benchmark for remote onboarding and minimum threshold for liveness to combat this epidemic, as a result, Regulatory sandboxes for testing new liveness technologies, mandatory disclosure of AI-generated evidence, and fast-tracked standards keep oversight relevant and required
- Cryptographic provenance: Also in the future, as the technology matures, C2PA should be adopted to watermark selfies and bind them to the capturing device preventing replay attacks, making fraudulent attempts detectable.
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!
