Deepfake

In the last few years, deepfake has become one of the most concerning terms in both cybersecurity and digital identity management. Originally derived from “deep learning” and “fake,” the term refers to synthetic media – images, videos, or audio – created or manipulated using artificial intelligence to make them appear authentic. Deepfakes can imitate a person’s face, voice, or behavior so convincingly that even trained professionals or advanced detection tools may struggle to distinguish them from genuine content.

What is a Deepfake?

A deepfake is an AI-generated piece of media that replaces one person’s likeness or voice with another’s using neural networks. By training on large datasets of facial expressions, speech patterns, and movement, these models can produce hyper-realistic fabrications. While deepfakes initially gained attention through entertainment and social media, they have quickly become a significant threat to institutions and businesses – especially in the financial sector (see JuicyScore’s guide on generative AI-driven fraud for more information).

Deepfake technology now extends beyond simple video swaps. Audio deepfakes can convincingly simulate a CEO’s voice to authorize a fraudulent transfer. Visual deepfakes can mimic identity documents or real-time video calls during remote onboarding. And text-based deepfakes – generated through large language models – can reproduce writing or communication styles for social engineering attacks.

Why Deepfakes Matter for Financial Institutions

For banks, lenders, and insurers, deepfake fraud introduces a new dimension of digital risk. Traditional fraud detection systems rely on static checks – such as document verification or biometric matching – but deepfakes exploit precisely those layers. A synthetic video can bypass facial recognition. A voice clone can trick call-center verification. A digitally altered ID can pass low-quality KYC checks.

This type of fraud, often combined with account takeover, synthetic identity, or impersonation schemes, undermines trust at scale. It erodes confidence in digital onboarding and challenges institutions to verify who is truly behind a transaction.

Detecting and Preventing Deepfake Fraud

Preventing deepfake fraud requires a multi-layered, adaptive approach that looks beyond the surface of the content. Instead of focusing solely on visual or audio cues, leading institutions are turning to device intelligence and behavioral analytics to detect anomalies invisible to the human eye.

Device-level signals – such as hardware consistency, remote access patterns, and virtualization indicators – reveal whether a digital session is being conducted through a real device or a synthetic, manipulated environment. Combined with behavioral scoring, these insights can expose deepfake-driven attacks long before they reach the transaction stage.

JuicyScore’s technology, for instance, helps financial organizations identify subtle correlations between device behavior and user intent. By analyzing hundreds of non-personal parameters – including device integrity, randomization, and access context – businesses can strengthen authentication processes without relying on biometric data that can be easily faked or stolen.

Real-World Case: The Arup Deepfake Scam

In May 2024, the the striking case of a deepfake fraud. The UK-based engineering group Arup lost approximately $25 million (HK$200 million) after fraudsters used a digitally cloned version of its Chief Financial Officer to order transfers during a fake video conference in Hong Kong.

According to Hong Kong police, an employee received what appeared to be a legitimate message from the company’s UK-based CFO about a confidential transaction. The employee later joined a video call that included what seemed to be several colleagues and the CFO — all of whom were in fact AI-generated deepfakes. During this call, the employee executed a series of 15 transfers to five local bank accounts, amounting to millions of dollars.

The rise of deepfake technology makes it clear that human verification alone is no longer sufficient. Fraud prevention systems must evolve toward continuous, context-aware assessment that detects not just who is speaking or appearing, but how they are doing it – through their digital footprint, device trust signals, and behavioral consistency.

The Future of Deepfake Detection

As generative AI becomes more accessible, the line between real and synthetic will continue to blur. For financial institutions, that means integrating deepfake detection directly into their broader risk and compliance frameworks. It’s not only a matter of technology, but of policy, training, and customer trust.

Building digital resilience requires continuous testing of fraud controls, cross-validation of data sources, and adaptive scoring systems that evolve with every new pattern detected. Ultimately, institutions that can combine data-driven intelligence with ethical AI principles will be best positioned to manage the deepfake era – protecting both users and their own reputation.