What is a deepfake?
Deepfakes are artificially generated audio, video, or images designed to convincingly mimic real people — or create entirely fictional personas. The term “deepfake” stems from a combination of "deep learning" and "fake," reflecting the use of neural networks to produce synthetic media that appears authentic but is completely — you guessed it — fake.
Unlike traditional types of fraud that involve physically altering existing materials, deepfakes are something new. They represent a fundamental shift in fraud methodology, as they consist of entirely synthetic content that never existed in the real world. AI, the technology behind deepfakes, enables fraudsters to generate realistic identity documents, selfies, videos, and voice recordings without requiring any source material — or even any specific technical skills.
For financial institutions, deepfakes pose an incredible challenge. To be considered effective, modern identity verification systems must now detect not only physical tampering, but also entirely synthetic content.
How deepfakes are made
Modern deepfake technology leverages sophisticated machine learning algorithms, particularly generative adversarial networks (GANs), to create synthetic media that can fool both human reviewers and traditional verification systems.
Creating a deepfake typically starts with training AI models on large datasets of images, videos, or audio samples to learn patterns and characteristics of authentic content. These models then generate new content that mimics the learned patterns whilst creating entirely fictional output.
Today's deepfake tools have dramatically lowered barriers to entry for prospective fraudsters. "A lot of what used to be hard to create from scratch is now becoming easily possible with the emerging technologies around generative AI," explains Konstantinos Levantis, Data Scientist at Fourthline. "Being able to detect this type of fraud is quickly becoming essential."
Readily available AI tools enable users to generate realistic identity documents, selfies, and videos within minutes. As if that weren’t unsettling enough, these tools can be run on standard consumer hardware, making sophisticated fraud accessible to virtually anyone with an internet connection.
Types of deepfakes used in financial fraud
Deepfake technology shows up in different manifestations, some of which are seen more often in financial fraud:
Document deepfakes involve AI-generated identity documents that appear authentic but have no connection to any real government or issuing authority. These synthetic documents include all the security features, formatting, and design elements you'd expect from a real ID, despite being entirely fabricated.
Facial deepfakes create realistic human faces for identity documents or selfie verification that correspond to no real person. To help fight this type of fraud, advanced identity verification solutions use tools like liveness detection.
Voice deepfakes synthesise speech patterns to impersonate specific individuals for phone-based verification or social engineering attacks like vishing (voice phishing). As Fourthline Security Engineer Luigi Pardey notes, "if a system is able to generate the voice or depiction of somebody, then it makes you as a victim more trusting of the person trying to perpetrate the attack."
Video deepfakes combine facial and voice synthesis to create convincing video content for live verification processes or liveness detection systems. At this moment, truly convincing video deepfakes face some serious practical limitations. This means they aren’t widely deployed in financial fraud due to the high cost vs. benefit for a fraudster — a topic Pardey addresses in his article about social engineering attacks that attempt to exploit biometrics.
Why deepfakes represent a paradigm shift in fraud detection
Deepfakes have fundamentally changed how financial institutions approach identity verification and fraud prevention.
Traditional document verification focused on detecting physical tampering, altered text, missing security features, or poor-quality reproductions. These methods worked effectively when fraudsters could only manipulate existing authentic documents. The emergence of entirely synthetic content has shifted the verification paradigm, observes Levantis:
"Instead of inspecting documents for holograms or security features, we're increasingly saying: 'Sure, the holograms are there, everything looks fine — but does this document even exist in the real world, or has it been generated entirely by AI?'"
This shift requires verification systems to move beyond detecting physical tampering to identifying content that was never authentic to begin with. This has had a significant impact on the technical and analytical approaches needed for effective fraud prevention.
The sophistication spectrum
Deepfakes exist across a spectrum of sophistication, from basic AI-generated images to highly convincing multimedia presentations.
Basic synthetic content includes simple AI-generated faces or documents that may fool the casual observer, but whose artifacts are detectible under closer analysis.
Advanced deepfakes incorporate sophisticated algorithms that can create highly convincing synthetic media with minimal detectable flaws, requiring specialised detection systems to identify.
Adaptive deepfakes use AI models that learn and adapt to detection methods, creating an ongoing challenge for verification systems that must continuously evolve their approaches.
The quality of deepfake content continues improving rapidly. "This is a risk,” notes Pardey. “People were falling for this type of fraud before, when it was badly done. Now, when the documents are more concise or better formatted, it is more likely that somebody may think that they belong to an actual person," notes Pardey, highlighting how improved AI quality increases fraud effectiveness.
Possible future implications of deepfake technology
The continued evolution of deepfake technology will require proactive adaptation and investment in detection capabilities.
There’s almost no question that advanced AI models will continue improving the quality and accessibility of synthetic media generation. This will make deepfakes more prevalent — and increasingly difficult to distinguish from authentic content.
The industry's response must focus on developing sophisticated detection systems that can identify synthetic content whilst maintaining efficient customer verification processes. This requires ongoing investment in AI-powered detection technologies and continuous adaptation to emerging threats.
Regulatory frameworks will likely evolve to address deepfake-related fraud, potentially requiring enhanced verification standards and documentation of detection capabilities. Partnerships between financial institutions, technology providers, and regulatory bodies will become increasingly important for developing effective responses to deepfake threats whilst maintaining customer privacy and operational efficiency.
Deepfake FAQs
Can deepfakes be reliably detected?
Yes, but detection requires sophisticated AI-powered systems that analyse multiple aspects of submitted content. Current technologies can identify many deepfakes, but this remains an evolving footrace between generation and detection capabilities.
How can organisations protect themselves against deepfake fraud?
Effective protection requires multi-layered verification systems that combine traditional document analysis with AI-powered deepfake detection, behavioural analysis, and human expert review for complex cases. Many organisations partner with specialised providers like Fourthline rather than building their own detection capabilities internally.
Are deepfakes illegal?
Using deepfakes for fraud or identity theft is illegal in most jurisdictions. But the technology does have some potentially legitimate applications in entertainment, education, and other fields. Laws specifically addressing deepfakes continue evolving as the technology develops.
