Deepfake Technology is Evolving: How Can AI Combat Deepfake Fraud in Financial Systems?
Artificial intelligence (AI) is positively revolutionising industries worldwide. However, it is also enabling more sophisticated scams. Among these, deepfake fraud in financial services is rapidly taking over the rest, with 40% of all deepfake attacks now targeting the sector. How can the financial industry battle it? Can AI be used to fight AI-Driven Identity Fraud?
Deepfake technology, the new face of fraud
Three years ago, synthetic identities and forged documents were the main concerns in financial fraud. Today, financial fraud experts recognise several AI-powered methods, with deepfakes as a leading threat. By producing realistic fake audio, video, and images—replicating voices, facial expressions, and interactions—deepfakes pose serious risks in the financial sector, often causing substantial financial losses.
Most common types of AI-driven identity fraud
We can take the story posted on The Guardian as a reference, where a finance worker in Hong Kong was tricked into transferring $25 million after a deepfake call impersonated a corporate executive. According to Signicat’s 2024 report on AI-Driven Identity Fraud, 42.5% of fraud attempts are now AI-driven, with deepfake fraud rates surging by 2137% over three years. This escalation calls for urgent integration of deepfake fraud prevention technology.
Deepfake scams in financial services in numbers
The financial sector is one of the industries most at risk from artificial intelligence and fraud scams, with an alarming 40% of all deepfake attacks directed at it. Without immediate investments in advanced AI-driven fraud detection for deepfake videos, these numbers are only projected to grow.
Deepfake attacks by industry
As numbers show, the financial sector is highly vulnerable to artificial intelligence and fraud scams, especially deepfakes, with losses totalling an unprecedented $158 billion in 2023 (according to the U.S. Federal Trade Commission). Without swift investment in AI-driven fraud detection for deepfake videos and strategies, losses are only projected to climb. By 2029, e-commerce fraud alone could reach $107 billion annually, propelled by AI-enabled scams. Yet fewer than 25% of businesses employ effective deepfake and fraud prevention technology.
Fraudsters are exploiting deepfake risks in the fintech ecosystem, targeting organisations dependent on digital identity verification. For instance, executive impersonation via deepfake video calls can lead to fraudulent wire transfers, and synthetic deepfakes bypass identity checks to compromise sensitive accounts. Three characteristics highlight AI and deepfake detection challenges: speed, scalability, and sophistication.
7 Deepfake challenges in fraud prevention
Although speed, scalability, and sophistication are the three deepfake detection challenges experts have top of mind, we can go beyond and list the 7 most crucial challenges in fraud prevention.
- Growing Realism. One core aspect of deepfake challenges in fraud prevention is how these manipulated videos and audio are nearly impossible to distinguish from authentic content, posing a significant threat to online security.
- Identity Theft and Impersonation. Deepfake technology allows fraudsters to replicate high-ranking executives or public figures with alarming accuracy, paving the way for unauthorised transactions and reputational harm.
- Rapid Evolution of Techniques. Attackers continually refine their methods and technologies, outpacing basic detection tools and reinforcing the need for AI solutions for detecting deepfake fraud.
- Scalability and Reach. Automated deepfake generation enables scammers to target multiple entities simultaneously, drastically increasing potential losses.
- Limited Awareness. Many people remain unaware of the sophistication behind modern deepfake technology, making them more vulnerable to scams and social engineering attacks.
- Resource-Intensive Detection. Detecting subtle cues in audio or video files requires sophisticated fraud prevention strategies for deepfakes, along with advanced algorithms and significant computing power. The usual lack of these resources inside organisations makes scaling their detection efforts effectively a challenge.
- Regulatory and Legal Ambiguities. The rapid advancement of deepfake technology outpaces current regulations, creating uncertainties around accountability and enforcement.
Deepfake technology: presentation attacks vs. injection attacks
Cybercriminals continuously adjust and evolve their strategies to exploit system vulnerabilities, especially via presentation attacks and injection attacks.
Presentation Attacks (Spoofing) are the use of fake biometric credentials (e.g. deepfake videos or images) to defeat identity checks. These attacks rose from 7.58% in 2021 to a projected 12.83% by 2024.
Injection Attacks, on the other hand, involve the insertion of synthetic or deepfake videos into authentication streams, bypassing conventional fraud detection layers. Alarmingly, injection attacks have seen an increase from 1.51% in 2021 to 6.27% in 2024.
These threats demand robust adoption of AI-driven fraud detection tools to combat deepfake threats to online security.
Evolution of presentation attacks vs injection attacks
Combating Deepfake Fraud with Advanced Strategies
To outpace these evolving threats, financial organisations must employ multi-layered approaches, incorporating AI and deepfake detection tools:
- Multi-Factor Authentication (MFA): Adds layers of identification, making it harder for fraudsters to penetrate systems.
- AI-Powered Detection Systems: Machine learning algorithms continuously evolve to identify and block suspicious anomalies in real-time.
- Continuous Employee Training: Staff should learn to identify warning signs early, aided by systems detecting deepfake audio fraud with AI.
The Role of AI in Preventing Deepfake Scams
Fascinatingly, the best way of combating deepfake fraud is with AI itself. Advanced AI-powered systems can detect slight inconsistencies in audio, video, and images—imperceptible to human eyes and ears. From spotting unusual voice tones or minute visual lags to identifying synthetic biometric artefacts, these AI solutions for detecting deepfake fraud are invaluable. By automating detection, institutions can respond to threats in real time, minimising their impact.
Signicat demonstrates how institutions can leverage AI solutions to detect deepfake fraud effectively, with solutions that illustrate best-in-class practices to protect financial systems from threats, such as:
- eID Hub: Ensures secure onboarding and login, significantly reducing the risk of deepfake fraud in financial services.
- RiskFlow Orchestration: Customises fraud prevention strategies for deepfakes and other types of fraud with behavioural analytics and device profiling.
- VideoID: Uses liveness detection and biometric checks to stop deepfake identity fraud, featuring AI-driven fraud detection for deepfake videos.
Trends in Business: Confusion and Stress Over AI Threats
The "Battle Against AI-Driven Identity Fraud" report estimates that 38% of revenue losses companies face due to fraud are directly linked to AI-driven identity theft. However, quantifying the exact financial impact of AI-driven fraud is difficult. For instance, Juniper Research predicts that e-commerce fraud alone will rise from $44.3 billion in 2024 to $107 billion in 2029—an alarming 141% increase in just five years. And that’s just e-commerce merchants.
The good news is that more than 75% of businesses are proactively addressing the growing threat of AI-driven identity fraud by planning technology upgrades and boosting their cybersecurity budgets. The bad news? Fewer than a 25% have actually begun implementing these changes.
Fraud decision-makers, as revealed in the surveys, often cite a lack of expertise, time, and budget as the biggest challenges. But it’s not just about resources. There is a significant knowledge gap. While fraud decision-makers are aware of AI as a major threat to identity security, fewer than a third are familiar with specific techniques like AI-generated identity document forgeries, deepfakes, or voice impersonation. This lack of understanding highlights that the full impact of AI-driven fraud is not yet fully grasped. Financial institutions must take immediate action to upgrade their fraud detection capabilities and defend against these rapidly escalating threats.