Deepfake technology has established itself as a highly concerning and intriguing application of artificial intelligence, making people no longer trust what they see. The financial sector faces growing risks from deepfakes as CFOs and decision-makers take notice. The increasing realism of synthetic videos and audio paves the way for a rising danger of financial fraud, along with executive impersonation at unprecedented levels.
So, what exactly is “deepfake”, and why is it drawing so much attention from both technologists and financial leaders?
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What exactly is deepfake?
What is it?
The term “deepfake” refers to synthetic media videos, images, or audio, which are created or altered using artificial intelligence, particularly deep learning. The term represents a combination of “deep learning” and “fake” to highlight the use of advanced algorithms that produce content resembling authentic material.
The technology behind deepfakes enables the creation of artificial content which perfectly replicates the facial characteristics, voice patterns, and body movements of human subjects. The technology began in online communities as an experimental tool before evolving into a major threat to society.
Deepfakes present valuable potential for creative applications in film production and educational settings and digital communication platforms, yet their predominant use today involves destructive activities, including non-consensual pornography alongside identity theft and political manipulation and mass misinformation distribution.
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How does it work?
Deepfakes are generated using advanced machine learning techniques, most notably:
– Autoencoders: These neural networks compress and reconstruct facial or audio features, enabling the system to “learn” how someone looks or sounds.
– Generative Adversarial Networks (GANs): These consist of two competing networks – (a) the generator, which creates fake content, and (b) the discriminator, which tries to detect whether the content is real. Through repeated competition, the system gradually improves until the output becomes highly realistic.
These models acquire the ability to duplicate and control human facial expressions, together with voices and movements, with impressive accuracy.
How deepfakes work is that the technology first gathers hundreds or thousands of images and audio samples from a specific individual. The collected data enables AI models to detect personal characteristics, including facial shape and vocal quality as well as eye behaviour and verbal mannerisms.
After training, the AI model applies learned features to new source material. The system can substitute a video subject’s face with that of the target individual while replicating their voice within audio content.
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“Deepfake CFOs” – An emerging threat
In early 2024, a Hong Kong-based employee at a multinational company was deceived by what appeared to be a routine video conference with the firm’s CFO and several colleagues. Over the course of the call, the employee was instructed to carry out a series of confidential financial transactions resulting in 15 transfers totalling around HK$200 million (approximately US$25.6 million). The shocking twist: none of the people in the meeting were real. [1]
According to Hong Kong police, cybercriminals had used AI-generated video and audio to impersonate the company’s senior executives. Leveraging publicly available footage and voice samples, the attackers created convincing deepfake avatars that mimicked facial expressions, speech, and mannerisms with striking realism. This was not just a single fake identity; it was an entire group video call populated by synthetic participants.
Authorities described the incident as one of the most sophisticated scams they had encountered, noting that it marked the first time they had seen deepfake technology used at this scale in a group setting. The fraud only came to light after the employee followed up with the company’s UK headquarters and discovered the transactions were unauthorised.
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No systems were breached, and no emails were hacked. Instead, the attackers exploited human trust using advanced deepfake technology as a tool for highly targeted social engineering. The case underscores a growing threat: as generative AI becomes more accessible, so does the ability to fabricate reality with dangerous precision.
Recognising red flags in deepfakes
The research conducted by Ayesha Aslam and her team in 2025 indicates that deepfake videos preserve difficulty in mimicking genuine human mannerisms from authentic video footage. Viewers can detect manipulated content through these missing elements, which function as indicators before deepfakes cause any damage. [2]
Here are some of the most common red flags to watch for:
– Unnatural blinking: One of the most reliable giveaways is the eyes. Deepfakes may blink too rarely or at odd intervals, creating a robotic or stiff impression that does not match natural eye behaviour.
– Inconsistent facial movements: Pay attention to how the mouth, eyebrows, and cheeks move, especially during speech. If expressions feel delayed, mismatched with tone, or strangely frozen, they may be artificially generated.
– Shifting nose position: In authentic videos, facial features move in harmony. In some deepfakes, the nose can appear slightly misaligned or shift unnaturally, especially when the subject turns or moves.
– Unfocused or glassy eyes: Real eyes show subtle movement and focus. In deepfakes, eyes may seem fixed, lifeless, or disconnected from the surrounding action.
– Lighting and shadow inconsistencies: Shadows and lighting on a deepfaked face may not match the environment. Watch for odd highlights or shadows that don’t align with the scene’s light source.
While none of these signs alone prove a video is fake, spotting more than one should raise suspicion. As deepfake technology evolves, so must our ability to question what we see and trust only what passes closer scrutiny.
Tips for fortifying your defence
Deepfakes are getting harder to spot, but that does not mean we are helpless. With a few simple habits, anyone can become better at telling what is real and what is not. Here are three ways to protect yourself:
Strengthen detection capabilities
The technology behind deepfakes exists in AI systems, so it makes sense to use AI systems to recognise deepfakes. The tools possess capabilities to identify small details that ordinary people would not notice, such as irregular blinking patterns and pixel inconsistencies.
AI systems serve as an effective initial defence because they process vast amounts of content with speed and precision in contrast to human inspection. Tools such as Google SynthID, together with Intel’s FakeCatcher, function to identify questionable content prior to its distribution. Your organisation can enhance its threat response speed while maintaining better protection against risks when these technologies become part of your review process.
Reinforce physical protection
Deepfake threats aren’t just a tech problem. They’re a policy problem, too. Strengthening internal safeguards like clear procedures, employee training, and cyber insurance can help organisations stay resilient, even when the threat feels invisible.
– Enhance verification protocols: For high-value transactions, especially those that are started by phone or email, use dual-approval workflows and two-factor authentication. You should also add human confirmation steps to stop deepfake impersonation.
– Strengthen internal policies: Include AI-driven threat scenarios in cybersecurity policies—defining response workflows, incident escalation, and staff responsibilities to detect and address manipulated communications quickly.
– Leverage cyber insurance coverage: Review your cyber insurance policy to ensure it explicitly covers deepfake-related social engineering and fraudulent financial transfers. Work with brokers to negotiate terms and limits that align with evolving AI risks.
Adopt a “zero trust’” mindset
One of the simplest yet most powerful defences is education. To stay ahead of deepfake threats, employees need to hear about them regularly, not just during annual training.
The inclusion of generative AI topics in security updates and newsletters, and team meetings is essential. Focus on how to recognise manipulative content, like inconsistent facial movements or odd voice patterns.
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Just as important, encourage a workplace culture that values pause and verification. Promoting a culture at work that values pause and verification is equally important. Tell your team that it’s acceptable to take your time and check again. Most costly mistakes happen when people act too quickly or feel pressured. Trusting gut instincts and taking a moment to question suspicious requests can be the difference between safety and compromise.
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References:
[1] https://www.cfo.com/news/deepfake-cfo-hong-kong-25-million-fraud-cyber-crime/706529/
