2025 Fraud Methodologies in Africa and Advanced Preventive Measures
Gift Arku
Marketing Associate
As we navigate the complexities of digital identity verification in 2025, the fight against fraud has reached unprecedented levels of sophistication. The latest findings from the 2025 Digital Identity Fraud in Africa Report highlight evolving fraud methodologies and cutting-edge preventive measures to counteract these threats. In this blog, we’ll explore key fraud trends from 2024 and the advanced strategies that are reshaping the fraud prevention landscape. For a deeper dive, download the full report here.
The Methodology of Fraud
Fraudsters constantly adapt to technological advancements, refining their techniques to exploit vulnerabilities. In 2024, document and biometric fraud continued to dominate, with notable trends such as identity farming and layered money laundering shaping the digital fraud ecosystem. Let’s break down some of the most prevalent methods:
Document Fraud
Document fraud remains one of the most challenging aspects of identity fraud. While traditional methods like forgery, alteration, and obscuration persist, advancements in verification technologies have rendered some of these techniques less effective. For example:
No ID Captured and Intentional Obscuration of IDs accounted for 36% of document fraud cases in 2023 but saw a sharp decline in 2024 due to improvements in SDKs, including border detection and auto-capture features.
Emerging techniques, however, demonstrate fraudsters’ ingenuity:
Screen Display Manipulation: Represented 50% of incidents in 2024. Fraudsters used high-resolution screens to display altered documents, bypassing physical checks.
Portrait Anomalies: Accounted for 27% of new fraud cases. This method involves altering photos on ID documents without modifying the physical document.
Digitally Altered Prints or Scans: Made up 10% of cases, with fraudsters editing documents and creating counterfeit copies.
Non-Document Submissions: Fraudsters submitted random images to exploit weak verification systems, making up 9% of incidents.
Photocopies: While less common (4%), photocopies obscure key security features, enabling fraud attempts.
Advanced Preventive Measures
A robust defence against document fraud requires a layered approach:
In 2024, Smile ID detected that 66% of fraud attempts were identity spoofs, identified through biometric verification. This underscores the importance of layered security systems.
Smile ID’s Document Verification
Smile ID’s document verification solutions are designed to meet the highest standards of accuracy, speed, and coverage. Supporting over 8,500 document types across 226 countries, we also offer the flexibility to add new document types upon request, ensuring seamless support for your unique business needs.
We deliver consistently higher pass rates than any other provider in Africa, without compromising security or speed.
Effectively tackle document fraud with Smile ID’s two document verification options:
Biometric fraud targets the systems that verify unique physical traits—such as facial features or fingerprints—by exploiting vulnerabilities in authentication technologies. Unlike document fraud, which manipulates identity records, biometric fraud focuses on bypassing the tools that confirm users' identities.
In 2024, fraudsters employed advanced tactics like digitally altered faces, replay attacks, and face-swapping technologies to deceive biometric systems. The rise of generative AI has further fueled these threats, enabling fraudsters to create hyper-realistic fake identities and manipulate biometric data with alarming precision.
Earlier biometric fraud methods often included:
No Face Match: Submitting identity documents without corresponding facial biometrics, exploiting systems lacking facial comparison.
Spoofing: Using static images, pre-recorded videos, or masks to impersonate users.
Duplication: Reusing the same biometric data across multiple accounts, avoiding detection by systems unable to spot repeated submissions.
Advances in biometric verification have mitigated many of these methods. For example, intentional face obfuscation, once a common fraud technique, has declined due to improved SDKs that prevent selfie captures without a clear face. Yet, fraudsters have adapted, now focusing on bypassing liveness detection systems. This shift has led to an increase in Liveness Fails—sophisticated attempts like deepfake videos and 3D-rendered faces designed to trick biometric verification systems.
Emerging Biometric Spoofing Techniques
As fraud tactics evolve, new threats continue to emerge. Key techniques from recent biometric spoofing rejections include:
Selfie Anomalies: Poor lighting, blurred features, or unnatural positioning in selfies—often signs of tampering or fraud attempts.
Screen Display Manipulation: Displaying altered biometric data (fake selfies/videos) on high-resolution screens to bypass facial recognition or liveness detection.
Group Presence Deception: Submitting selfies with multiple people in the background to confuse the system and hide the actual person being verified.
Photo of a Picture: Capturing a photo of a printed image to mimic a real selfie, though quality and context often expose the manipulation.
Deepfakes: Using AI to generate hyper-realistic fake images or videos to impersonate legitimate users and bypass verification systems.
The Rise of Generative AI Fraud Attacks
Generative AI emerged as a significant enabler for fraudsters in 2024. Platforms like “OnlyFake” lowered barriers to creating realistic fake IDs, enabling mass-scale fraud. For just $15 per ID, users could generate convincing fake documents, bypassing digital verification systems.
High-Profile Incidents
Generative AI’s impact wasn’t limited to document fraud:
Hong Kong Zoom Scam: Fraudsters used AI personas to impersonate executives, convincing a victim to wire $25 million.
African Union Impersonation: Attackers used deepfake videos to impersonate high-profile officials, orchestrating fraudulent meetings with global leaders.
These cases highlight the vulnerabilities of traditional verification methods. AI-generated IDs, deepfakes, and other advanced tactics demand more resilient solutions.
Liveness Detection: To fight Generative AI Attacks
Liveness detection—the process of verifying that a real person is present—has become critical in combating biometric fraud. Traditional methods like passive and active liveness detection have their limitations. To stay ahead, dynamic liveness detection has emerged as the gold standard:
Passive Liveness Detection: Relies on subtle cues but struggles with advanced attacks.
Active Liveness Detection: Introduces interactive steps (e.g., blinking or nodding), but predictability makes it vulnerable to replay attacks.
Dynamic Liveness Detection: Combines real-time prompts with unpredictability, thwarting even the most sophisticated fraud attempts.
ISO/IEC 30107-3:2023 Level 2 Certification: Demonstrates resilience against advanced attacks.
See Enhanced SmartSelfie™ in action here 👇
Staying Ahead of Evolving Fraud
The battle against fraud requires constant innovation. By adopting advanced measures like layered document verification and dynamic liveness detection, businesses can safeguard their systems against emerging threats.
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.
