Fraud Trends in Africa: 6 Key Insights from the 2025 Report
Gift Arku
Marketing Associate
Africa's digital transformation continues to accelerate, fueled by the rapid expansion of fintech and the growing adoption of digital payment platforms. However, this growth comes with increasing exposure to sophisticated fraudulent activities. Smile ID’s 2025 Digital Identity Fraud in Africa Report uncovers the evolving tactics of fraudsters across the continent, providing invaluable insights for businesses seeking to protect their platforms and customers.
This year's findings are based on an analysis of over 110 million identity verificationchecks conducted across various African industries throughout 2024. The data reveals both progress in combating fraud and alarming trends that businesses must address to stay secure. From the growing use of generative AI to craft hyper-realistic fake documents and deepfakes to insider-assisted fraud driving layered money laundering schemes, the report sheds light on the tactics shaping the fraud landscape in Africa.
Among the report's key takeaways: Biometric fraud reached an all-time high of 16% in a single quarter, and fraudsters increasingly target authentication points over registration processes. Additionally, regional fraud trends show East Africa overtaking Central Africa with the highest rates of fraud detected in 2024.
Each insight reveals a piece of the broader fraud puzzle, helping businesses understand where vulnerabilities lie and how to fortify their defences. In this blog, we’ll explore six critical findings from the report, offering a comprehensive overview of the state of fraud in Africa.
Key Insight 1 - A Shift in Biometric Fraud Tactics
Biometric fraud, which targets systems designed to verify unique physical traits, has evolved in complexity. Unlike document fraud, which manipulates physical or digital identity records, biometric fraud attempts to bypass authentication mechanisms that ensure the user's identity is genuine.
Fraudsters are now deploying more advanced techniques to deceive biometric systems. Digital face manipulation, replay attacks, and face-swapping technologies are increasingly common, often aided by the capabilities of generative AI. These technologies allow fraudsters to create hyper-realistic fake identities or manipulate existing biometric data with remarkable precision, making it harder for even advanced verification systems to detect fraud.
Historically, biometric fraud has relied on the following tactics:
No Face Match: Fraudsters would submit identity documents or images without matching biometric data, exploiting systems that lacked facial comparison capabilities.
Spoofing: Using static images, pre-recorded videos, or masks, fraudsters impersonated legitimate users to bypass basic biometric checks.
Duplication: Reusing the same biometric data across multiple accounts, fraudsters targeted systems unable to detect repeated submissions.
However, advancements in biometric verification have rendered many of these methods obsolete. For example, “Intentional Obscuration of Face,” a technique previously identified in our reports, has been eliminated due to recent updates to our SDKs, which prevent selfie captures when no face is detected. This has effectively closed a major vulnerability.
Despite these improvements, fraudsters have adapted by shifting their focus to bypassing liveness detection systems. As a result, fraud attempts categorised as "Liveness Fails" have increased. These include sophisticated techniques such as deepfake videos and 3D-rendered faces designed to fool biometric verification systems, illustrating the growing sophistication of biometric fraud.
Emerging Biometric Spoofing Techniques
With fraud tactics constantly evolving, we’ve seen the rise of new and more sophisticated techniques aimed at bypassing biometric systems. Here are some of the most notable methods identified from our analysis of biometric spoofing rejections:
Biometric Fraud Rate Hits a Quarterly All-Time High of 16%
Biometric fraud continues its upward trend, reaching a quarterly average of 16% in 2024—the highest level recorded in the past three years. This surpasses the previous peak of 13% in 2023, signalling that fraudsters are increasingly targeting biometric verification systems.
Biometric fraud remained the most prevalent form of fraud, though it showed fluctuations across the year. Biometric fraud climbed to 15% in Q1, peaked at 16% in Q2, and gradually declined to 14% in Q3, ultimately closing the year at 13% in Q4.
Meanwhile, document fraud experienced a significant uptick in the second half of the year, rising from 4% in both Q1 and Q2 to 7% in Q3 and 9% in Q4. This represents a 125% increase in document fraud, which points to a shift in fraudster tactics toward exploiting vulnerabilities in document-based KYC (Know Your Customer) systems, particularly as biometric systems improve.
By Q4, the gap between biometric and document fraud had significantly narrowed. While biometric fraud still outpaced document fraud, the ratio had decreased from 1.5 times higher in Q1 to just 1.4 times by Q4 (13% vs. 9%). This suggests that while biometric fraud remains dominant, document fraud is becoming a more significant threat.
Key Insight 2 - Fraud 4x Higher at Authentication Than Registration
In 2024, fraud attempts during user authentication were four times higher than during registration, underscoring a significant vulnerability in the authentication process. This pattern highlights a critical issue:
“Fraud is often not limited to initial account creation but escalates when users return to access platforms.”
Data from 2024 revealed that fraud rates during authentication consistently outpaced those at registration. For instance, in May, the fraud rate for authentication jobs was 30%, while registration fraud stood at just 11%. On average, fraud during authentication jobs was four times higher than at registration, signalling the need for more robust fraud prevention measures during the authentication stage.
The Rise of Authentication-Related Fraud
While onboarding (the registration phase) remains a key target for fraudsters, the larger concern is the rising threat during post-onboarding interactions, particularly account logins, high-value transactions, and password recovery. This trend reveals a growing prevalence of account takeovers, where fraudsters leverage stolen credentials or identity manipulation to gain unauthorised access.
Businesses must address these vulnerabilities by implementing advanced biometric authentication solutions, which not only secure the initial registration but also safeguard future interactions. By strengthening defences at the authentication stage, companies can better protect users from sophisticated fraud tactics, such as deepfake-based spoofing or identity theft.
To address these risks, businesses need robust biometric authentication solutions.
SmartSelfie™ Enrolment lets businesses onboard new users digitally while capturing high-quality images for future authentication. This is required when a user first signs up for a service.
SmartSelfie™ Authentication verifies the identity of an enrolled user in real-time. It compares a user’s selfie to their previously enrolled image to ensure the person attempting to access an account is the same individual.
SmartSelfie™ Compare combines key elements of enrolment and authentication into a single API call. It compares two images to confirm whether they belong to the same person. This is particularly useful when a business already has an image on file, such as access to a government database photo.
Smile ID’s proprietary facial recognition technology (SmartSelfie™) has a 99.8% accuracy rate on African faces of all skin tones, making it the superior way to verify African identities.
Key Insight 3 - SDKs Detect Twice as Much Fraud as Other Integration Methods Combined
Fraud detection through Smile ID’s Mobile SDKs accounted for 68% of all detected fraud cases in 2024, compared to just 32% from other integration methods such as APIs. This stark contrast underscores the superior effectiveness of SDKs in combating identity fraud, making them the gold standard for digital onboarding in Africa.
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Why SDKs Are the Gold Standard for Digital Onboarding in Africa
When it comes to integrating identity verification solutions, businesses often face a choice between SDKs and APIs. While APIs offer flexibility for custom development, they present significant challenges, particularly when addressing the unique needs of the African market. Mobile SDKs, on the other hand, provide a streamlined, secure solution that is perfectly suited to regional challenges.
Unlike APIs, which often require extensive custom development and management, SDKs embed biometric verification capabilities directly into an application’s codebase. This results in a more seamless integration, with the added benefit of stronger security across all user interactions. In Africa, where issues such as network instability, low-quality device cameras, and limited storage capacity are common, SDKs shine by addressing these hurdles.
SDKs are also superior in security. By controlling the image capture process, they ensure that only real-time media is captured directly from the device camera. This makes it impossible for fraudsters to use pre-recorded or manipulated images to bypass biometric verification. As identity fraud tactics become increasingly sophisticated, such as the rise of identity farming, this real-time capture and liveness detection provides an essential defence against evolving threats.
In fact, systems integrated through Smile ID’s SDKs detected more than double the fraud attempts than those using other integration methods. This impressive performance highlights not only the superior fraud detection capabilities of SDKs but also their increasing adoption by businesses seeking more secure and reliable identity verification solutions.
Smile ID SDKs - Tailored for the African Market
Some of the key ways we’ve optimised our SDKs to stand out for the African market include:
Easier Integration with Liveness Stack
Simplifies the implementation of liveness detection, ensuring secure and reliable biometric data capture.
Optimised Image Sizes
Captures high-quality images that are small in size, enhancing storage and performance efficiency.
Offline Capabilities with Automatic Retries
Ensures consistent functionality, even in poor network conditions, by supporting offline use and retry mechanisms.
Multi-Platform Support
Available on, Android, iOS, React Native, and Flutter for maximum flexibility and compatibility.
Lightweight
Minimises app size impact, maintaining smooth and efficient performance without unnecessary bloat.
Adaptable to Environmental Challenges
Handles issues such as poor lighting and network instability automatically, ensuring reliable performance in varying conditions.
Agent Mode
Supports third-party operators, such as a customer service agent or field representative, to assist in capturing and submitting biometric data on behalf of a customer.
Simplified Development Process
Eliminates the need for developers to manage retries, image quality, or network mechanics, reducing complexity.
Open-Source Accessibility
Open-source nature makes them readily available for developers to use and adapt. This transparency fosters trust and enables rapid innovation.
Key Insight 4 - Dynamic Liveness Detection is the Best Bet Against Generative AI Fraud
As fraud tactics evolve, businesses face increasing threats from deepfakes, AI-generated faces, and replay attacks. Dynamic liveness detection has emerged as the most effective defence against these sophisticated methods. By using real-time, unpredictable prompts, it ensures accurate user verification while blocking even the most advanced AI-driven fraud attempts.
Dynamic Liveness Detection: The Key to Modern Biometric Verification
At the forefront of this innovation is Smile ID’s Enhanced SmartSelfie™, a groundbreaking tool that combines dynamic liveness detection with cutting-edge fraud detection algorithms. Designed to counter the growing wave of generative AI fraud, Enhanced SmartSelfie™ provides a seamless and secure experience for legitimate users while ensuring robust protection for businesses.
Check out Enhanced SmartSelfie™ in action 👇
Key Features of Enhanced SmartSelfie™:
Enhanced Selfie Quality Analysis: Ensures only high-quality selfies are accepted, reducing errors and minimising fraud risks.
Advanced Fraud Detection: Leverages proprietary machine learning algorithms to analyse user behaviour and metadata in real time, blocking suspicious activity.
Real-Time UI Feedback: Provides instant guidance to users during the verification process, improving success rates and reducing onboarding friction.
Strict Mode for Compliance: Enforces rigorous image capture standards for high-security environments.
Certified Protection Against Advanced Biometric Fraud
Enhanced SmartSelfie™ has been rigorously tested and certified to meet the highest global standards for biometric liveness detection, including ISO/IEC 30107-3:2023 Level 2 certification. During testing, the system achieved 0% penetration, successfully thwarting all advanced attacks, from deepfakes to 3D masks.
Real-World Impact: Staying Ahead of Evolving Threats
Dynamic liveness detection is a practical solution tailored to real-world challenges. Smile ID’s biometric suite integrates Enhanced SmartSelfie™ to secure critical touchpoints throughout the customer journey. From onboarding to post-onboarding interactions like account logins and high-value transactions.
Key Insight 5 - East Africa Leads with 27% Rejection Rate
East Africa recorded the highest fraud rejection rate on the continent, reaching 27% in 2024. This spike was driven largely by poor-quality identity documents in countries like Zambia, Rwanda, and Sudan, which strained verification processes. While biometric fraud dominated, the rise in document fraud was significant, underscoring the shifting tactics of fraudsters.
Fraud patterns varied across regions:
East Africa: Highest document fraud rates, peaking in January and rising steadily after mid-year.
West Africa: Highest biometric fraud cases, with a major surge from October to December.
Southern Africa: A sharp increase in December, largely tied to misuse of retiring Green ID Books.
Central Africa: A late-year spike, continuing an upward trend from 2023.
National IDs remained the most targeted document type (27% fraud rate), reflecting their widespread use. However, the shift to digital IDs is showing promise. In South Africa, fraud rates for outdated Green ID Books were 500% higher than for Smart ID cards, highlighting the security gains of digital ID adoption.
In 2024, the financial sector emerged as the prime target for fraud, with digital banks experiencing peak attempts at 35% of all biometric and document verification fraud cases, followed closely by microfinance institutions at 30%.
Fraud trends by industry revealed distinct patterns:
Digital Banks: Consistently the hardest hit, reflecting their appeal for account takeover and identity farming schemes.
E-commerce: Spikes during peak shopping periods, hitting 29% in January and 26% in December.
Microfinance: Targeted by layered money laundering operations, driven by identity farming schemes.
Investment Services: Least targeted, with only a 10% peak.
The financial sector’s vulnerability stems from its high-value transactions and digital interfaces, which fraudsters exploit using stolen identities and insider-assisted account takeovers.
Drive Action Against Fraud
Fraud in 2024 was more sophisticated and regionally varied than ever. East Africa led in document fraud, West Africa in biometric fraud, and digital banks bore the brunt of sector-specific attacks. The trends are clear: Fraud tactics are evolving, and so must defences.
Smile ID remains committed to equipping businesses with cutting-edge solutions, from dynamic liveness detection to mobile SDKs optimised for African conditions. However, data alone won’t solve the problem—collaboration, prevention, and innovation will.
We are equipped to help you level up your KYC/AML compliance stack. Our team is ready to understand your needs, answer questions, and set up your account.
