INTERPOL's African Cyberthreat Assessment Report 2026, released on August 3, draws on survey data from 36 African countries and covers the full range of cybercrime activity on the continent. Most of the coverage has focused on the headline stat: AI now enables 55% of reported cybercrimes across Africa. That number matters, but the detail underneath it matters more for African fintechs and banks trying to understand what the threat landscape actually looks like today.

These are the five findings from the report that have the most direct implications for financial platforms operating in Africa and what each one means for your fraud prevention strategy.

1. AI Is Now the Default Tool for Cybercrime

The report describes cybercrime in Africa as having evolved from isolated incidents into an industrialised, borderless ecosystem, with AI automating every stage of an attack from reconnaissance and phishing to extortion and evasion. The 55% figure is the current state of reported cybercrime across 36 countries.

What this means for African fintechs:

• Attack speed has changed fundamentally: AI-automated attacks do not operate at human pace. By the time a fraudulent account is flagged manually, the same operation may have already succeeded elsewhere. Detection systems that depend on human review at the first line of defence are structurally behind.
• Social engineering is more convincing at scale:  AI-generated phishing messages, deepfake voice calls, and synthetic correspondence can now be produced at volume without the quality drop that used to make mass attacks detectable.
• Rule-based detection alone is no longer enough: Fixed rules catch known patterns. AI-automated fraud generates new ones faster than rules can be updated. Behavioural monitoring that adapts to emerging signals is what closes that gap.

2. Synthetic Identities Are Defeating Biometric Verification

This is the finding with the most direct implications for any platform running identity verification. AI-generated synthetic identities, combining real personal data with fabricated elements, have been used to bypass advanced biometric verification systems. These identities have been used to open bank accounts, secure mobile loans, and register SIM cards under false names.  

What this means for African fintechs:

• A liveness check alone is not enough without detection:  Synthetic identities increasingly enter verification flows through injected video feeds that bypass the camera entirely. A liveness system that only checks for blink and smile responses without detecting the source of the feed has a structural gap.
• The quality of the verification model matters: A biometric system trained on a narrow dataset will miss attack patterns it has not seen before. The diversity and recency of training data determine how well a model holds up against evolving synthetic identity techniques.
• Verification cannot stop at onboarding: A synthetic identity that passes onboarding may behave normally for weeks before activating. Post-onboarding behavioural monitoring is what catches the activation pattern.

3. Scam Centres Have Industrialised Fraud

72% of surveyed African countries reported the presence of scam centres, with the highest concentration in Southern and West Africa. These are not informal operations. They are organised, staffed facilities using AI, mobile money platforms, and social media to run fraud campaigns at industrial scale. 

What this means for African fintechs:

• Mobile money is a primary attack surface: The report explicitly names mobile money as one of the channels scam centres exploit. Platforms without fraud detection built specifically around mobile money transaction patterns are carrying exposure generic tools were not designed to catch.
• Coordinated fraud leaves patterns your monitoring should catch: Scam centre operations produce signals: velocity patterns, shared device clusters, similar onboarding sequences. Behavioural monitoring can surface these even when individual transactions look clean.
• The risk is not just to your users: A platform whose infrastructure facilitates scam centre activity, even unknowingly, carries reputational and regulatory exposure beyond the direct financial loss.

4. Africa Is Now a Launchpad for Global Cybercrime

The report notes that Africa-based threat actors are increasingly targeting victims in Europe and North America, using infrastructure spread across multiple jurisdictions. Business email compromise schemes originating in Central and West Africa are reaching corporate victims globally, with AI generating convincing correspondence that bypasses standard detection.

What this means for African fintechs:

• Cross-border AML exposure is real and growing: If your platform is used by actors running schemes targeting international victims, your AML programme needs to screen for cross-border risk signals, not just domestic transaction patterns.
• Correspondent banking relationships are at risk: International banks are paying attention to where fraud originates. African fintechs with weak AML controls risk being de-risked by correspondent partners who do not want the exposure.
• Regulatory scrutiny will follow the money:  As African cyber policy gets pulled into wider diplomatic conversations, the compliance bar for cross-border platforms will rise. Building ahead of that is cheaper than responding to it.

5. The Data Sharing Gap Is the Biggest Structural Vulnerability

The report identifies the absence of real-time data sharing between banks, telecoms, and law enforcement as a dangerous blind spot being actively exploited. Beyond the technological tools, it is also a structural problem.

What this means for African fintechs:

• Your fraud detection is only as good as the data it can see: If your monitoring only covers your own platform's transaction data, you are missing the cross-platform signals that reveal coordinated fraud rings operating across multiple institutions simultaneously.
• Closing this gap is a competitive advantage: Shared fraud intelligence produces detection quality that no single platform can achieve alone.
• This is where regulators will push next: The CBN's March 2026 Baseline Standards already mandate automated transaction monitoring. Inter-institutional data sharing requirements are the logical next step, and several markets are already moving toward it.

What This Means for Your Platform

The five findings above point to the same underlying challenge: fraud has industrialised, identity is the primary attack surface, and the gap most platforms carry is not at the point of verification but in what happens after onboarding. Here are the steps worth acting on:

• Audit your onboarding verification depth: If your liveness check does not detect injection attacks, synthetic identities that combine real data with fabricated elements can pass. The report documents this as an active pattern.
• Build monitoring that continues after onboarding: A synthetic identity or scam centre operation that passes onboarding may behave normally for weeks before activating. Monitoring that stops at account creation will not catch it.
• Screen continuously: PEP and sanctions lists update. A customer who was clean at onboarding may appear on a watchlist six months later. The report's cross-border findings make clear that ongoing screening is now a baseline expectation.
• Connect your identity and transaction data: The report specifically identifies the absence of real-time data sharing between banks, telecoms, and law enforcement as the biggest structural vulnerability. Platforms that connect identity verification to transaction monitoring close that gap internally even before inter-institutional solutions exist.

How Dojah Helps You Close the Gap

Every finding in this report traces back to the same infrastructure need: layered controls that cover onboarding, post-onboarding behaviour, and ongoing AML screening in one connected system. Dojah gives you that system, built specifically for African platforms.

• At onboarding: Dojah's EasyOnboard combines government database verification, liveness detection, face match, and configurable fraud rules in one flow, giving platforms the layered defence needed to detect fraud early.
• After onboarding and across the lifecycle: Profiled Risk tracks behavioural signals, transaction patterns, and risk drift continuously. It catches the coordinated activity and dormant accounts that only activate long after a clean onboarding. 
• For ongoing AML screening: Dojah's AML Watchlist runs sanctions, PEP, and adverse media screening in one workflow, supporting continuous monitoring so your screening does not stop at signup.

If you’re ready to build fraud defences against the emerging threats of 2026, sign up on Dojah and access anti-fraud solutions built for African platforms.