Biometric Update's 2026 Deepfake Fraud Detection Market Report, published July 31, covers the fraud threat landscape of deepfake fraud globally and three-year market forecasts through 2028.

Deepfake fraud attempts across fintech and digital platforms will exceed 334 million annually by 2028, up from 145 million today, while combined voice and face deepfake detection checks are projected to grow from 6 billion in 2026 to over 12.2 billion by 2028. 

These are global figures, but the threat described is already active in African markets. INTERPOL's 2026 African Cyberthreat Assessment Report, released recently, confirmed that AI-generated synthetic identities are already being used to bypass biometric verification systems across the continent. Deepfake-enabled fraud has become an organised and scalable operation rather than an isolated event. 

These are the findings from the report that matter most for African fintechs and what each one means for your fraud prevention strategy.

Deepfake Detection Is Now a Baseline Requirement

The report's central argument is that deepfake detection is no longer a specialist anti-spoofing capability. It has become a foundational layer of modern identity infrastructure, required across onboarding, authentication, and transaction authorisation.

For African digital platforms, this means:

• The baseline has shifted: Deepfake detection is no longer a premium add-on. It is now a standard component of identity verification across financial services, government, and telecoms. Platforms that have not started are already behind. 
• A liveness check alone is no longer sufficient: Liveness detection addresses presentation attacks, but deepfakes can also be deployed through injection attacks that bypass the camera entirely. A platform running only liveness detection has a structural gap that is being actively exploited.
• Regulators are moving toward stronger biometric standards: Across African markets, regulators including the CBN are already requiring stronger KYC and continuous authentication. Deepfake detection is a direct response to the fraud patterns those requirements are designed to address. 

More Than Half of Fraud Attempts Now Involve a Deepfake Attack

One of the most important findings in the report: more than 50% of fraud flagged by identity verification platforms had an AI-generated element attached to it. This includes fake faces and voices used to impersonate real people, as well as manipulated identity documents where photos have been swapped or blended to pass verification checks. 

What this means for African fintechs:

• Fraud is no longer primarily about stolen credentials: The attack chain starts with a stolen identity, adds a deepfake selfie or document, and injects it into an onboarding workflow to create an account. This is the dominant pattern in identity fraud in 2026.
• Document verification and liveness need to work together:  A deepfake ID document can pass a document check. A deepfake selfie can pass a basic liveness check. Deepfake detection for both humans and their identity documents is now a must-have.

A Liveness Check Alone Is Not Built to Catch Injection Attacks

The report draws a clear line between presentation attacks and injection attacks. Presentation attack detection checks whether a real person is present at the camera. Injection attack detection checks whether the media feed itself has been tampered with. These are different threats requiring different detection methods.

For African fintechs running liveness checks today:

• Liveness detection alone misses injection attacks: An attacker using a virtual camera to inject deepfake footage into a verification pipeline bypasses the physical camera check entirely. The reported 9x year-on-year surge in injection attacks makes this the fastest-growing attack vector in identity verification.
• The scale is already measurable: Between 10 and 15% of authentication liveness checks are on deepfake-generated images, according to supplier data cited in the report. A platform processing thousands of verifications daily without injection attack detection is statistically clearing a meaningful volume of deepfake attempts.
• Detection needs to happen at the source: IAD verifies the media feed is coming from a real device. Deepfake detection catches fake content. Both are needed, and neither replaces the other.

Onboarding Is Not the Only Attack Surface

A significant shift the report documents is the movement of deepfake attacks from onboarding to authentication. Account takeover via voice deepfake in call centers, account recovery fraud, and post-login session manipulation are all documented as growing attack vectors.

For compliance and risk teams in African markets:

• A clean onboarding does not mean a safe account: ATO fraud increasingly involves deepfakes that fool voice-based customer service verification and facial recognition systems designed to protect account recovery. A synthetic identity that passes onboarding may activate weeks later through a different channel.
• The monitoring gap sits after onboarding: The report recommends platforms take a risk-based approach to biometric authentication that includes device fingerprinting, verified identity signals, and behavioural analytics alongside deepfake detection. The detection layer at onboarding and the monitoring layer after it need to be connected.
• Account recovery carry the highest post-onboarding risk: Fraudsters use cloned voices to pass identity verification calls, reset credentials, and redirect funds. African platforms relying on voice-based authentication without synthetic voice detection are carrying exposure the report explicitly flags.

A Single Check Does Not Close the Gap

Companies get it wrong when they treat deepfakes as a narrow liveness-check problem rather than a trust gap. The recommended system combines identity verification, liveness detection, deepfake detection, and continuous risk monitoring in a connected workflow. 

What changes for African platforms:

• Isolated tools leave gaps: A tool that only flags suspicious media still leaves your team to decide what to do next. Detection needs to be connected to your full fraud stack so the right action happens automatically, blocking the transaction, updating the risk score, or triggering a step-up check.
• A layered approach matches the threat:  Fraud rings do not use one attack type at a time. They combine fake identities, manipulated documents, and injection attacks in a single attempt. One check catches one part of that. A layered approach catches the combination.
• African-specific models work best:  A system built for high-bandwidth environments and narrow datasets will miss attack patterns common in African markets. It will struggle with the device quality and connectivity conditions your users are actually working with.

What This Means for How You Build

With deepfake fraud attempts doubling every two years, for you to stay ahead, you need more than one check in place. The market has moved, and platforms relying on a single verification layer are already behind. 

The gap the report describes is already an emerging threat as synthetic identities are already bypassing biometric systems on African platforms.

 Here is what to prioritise:

• Audit your liveness check: If your liveness check only detects presentation attacks, it has a gap that is being actively exploited. Confirm whether your current setup includes injection attack detection or whether that layer needs to be added.
• Connect your onboarding verification to post-onboarding monitoring: A synthetic identity that passes onboarding and then sits dormant will not be caught by a verification check. Continuous behavioural monitoring is what catches the activation pattern.
• Screen continuously not just at signup: The report's findings on account takeover and call center fraud make clear that ongoing detection across the customer lifecycle is no longer optional.
• Evaluate your verification stack: A model that performs well in a controlled environment may not deliver the same results across the range of devices and network conditions your users are actually working with.

How Dojah Helps You Stay Ahead of Deepfake Fraud in 2026 

The Biometric Update 2026 Deepfake Report is clear: effective fraud detection requires layered controls that cover onboarding, post-onboarding behaviour, and ongoing screening in one connected system. Dojah is the identity and trust infrastructure African platforms can use to stay ahead of deepfake fraud threats.

• At onboarding: Dojah's EasyOnboard and Liveness Check work together to confirm a real person is present and that the identity being submitted is genuine. EasyOnboard layers government database verification, face match, and configurable fraud rules in one flow, while Liveness Check confirms the biometric being captured is from a live person and not a deepfake or injected feed.
• After onboarding and across the lifecycle: Profiled Risk tracks behavioural signals, transaction patterns, and risk drift continuously. A synthetic identity that passes onboarding cleanly may only show its true pattern weeks later. Profiled Risk catches that activation before the damage is done.
• For ongoing AML screening: Dojah's AML Watchlist runs sanctions, PEP, and adverse media screening in one workflow. As deepfake-enabled fraud increasingly crosses borders and involves high-risk actors, ongoing screening is the layer that catches what onboarding cannot.

If you're ready to strengthen your fraud defences against deepfake threats, explore Dojah's Liveness Check or sign up on Dojah today to get started.