Biometric Security Can Defeat Deepfake Bank Fraud, But Are Consumers Ready?
May 19, 2025
Deepfake bank fraud rates are surging. Recent analysis by Statista shows a 3,000% increase in deepfake fraud attempts in the US.
Banks are working to respond, but digital experience, risk, and compliance teams often find themselves caught between competing priorities like having to reduce customer complaints about cumbersome authentication processes while meeting increasingly strict compliance requirements.
Successfully combating deepfake bank fraud requires implementing a robust biometric authentication system, as standard fraud detection methods are simply not equipped to handle this sophisticated threat.
In this article, we'll build the business case for using biometric security to fight deepfake fraud and provide clear examples of how these scams actually work.
Deepfake Bank Fraud Examples
Over the past 12-18 months, deepfake bank fraud has evolved from science fiction (or extremely rare occurrences) into headline news. Here are two notable examples, one successful attack and one that was fortunately stopped:
Successful Deepfake Fraud: Hong Kong Bank (2023)
A finance worker attended what appeared to be a legitimate Zoom call with multiple colleagues and the company CFO. In reality, every participant was a deepfake using synthetic video and voice technology.
The finance worker was instructed to transfer $25 million, and the funds were moved before anyone discovered the deception, as reported by CNBC.
Foiled Deepfake Fraud: Bunq CEO Impersonation (2023)
An employee at Bunq, a Dutch digital bank, received an email supposedly from CEO Ali Niknam that included a video deepfake requesting an urgent large transfer.
The scam was prevented by chance when the targeted employee contacted the CEO through a separate channel to verify the request, as detailed in Ali Niknam's LinkedIn post about the incident.
In both cases, deepfake technology was used to impersonate banking executives and target high-stakes individuals. However, similar attack methods can also target customer interactions.
A Plausible Scenario
Consider this scenario:
Emma is a high-net-worth client who maintains multiple accounts with a private bank in London. She travels frequently and typically manages her finances remotely, including through video calls with her relationship manager.
An attacker gathers Emma's personal details from social media, public records, and leaked data. Finding old interview clips online, they build a convincing deepfake of her face and voice.
The fraudster contacts the bank's private wealth department, claiming Emma has a new phone and email address and is traveling in Southeast Asia, needing to initiate a large fund transfer.
When asked for identity verification, the fraudster agrees to a video call, saying: "I know you need to see me on camera. I'm in a rush, but let's do it."
During the call, a convincing deepfake of Emma appears, matching her appearance, speaking in her voice, and even mimicking her typical mannerisms.
The attacker requests a £2.1 million wire transfer to an overseas account.
Satisfied with the seemingly normal video call, the relationship manager proceeds with the request.
Two-factor authentication codes are intercepted via SIM swap or compromised email.
The bank initiates the transfer, believing they're serving a verified high-value client.
Internal red flags are eventually triggered because:
The destination account was newly created
The transfer doesn't match Emma's transaction history
The client typically confirms large transfers with a follow-up call, which didn't happen
By this time, however, the fraud has already occurred, and the bank must absorb the loss as they have greenlighted the transaction on their end before discovering the fraud.
A single incident like this can severely damage a financial institution. Multiple deepfake fraud cases could be catastrophic for both revenue and reputation.
As generative AI technology advances and deepfakes become easier to create, many banks face a potentially existential threat. It's time to fundamentally reconsider customer authentication protocols.
Why Deepfake Fraud Defeats Traditional Detection Systems
Traditional fraud detection systems aren't designed to identify deepfakes. Passwords, multi-factor authentication, device/IP checks, and post-login behavioral analysis can all be bypassed using stolen credentials, SIM swaps, or sophisticated deepfake audio and video.
During typical user interactions, fraud detection systems fail against deepfakes due to five critical weaknesses:
No method for detecting synthetic media or manipulated behavior. There's no live biometric proof that a user is genuine.
Score-based detection (probability of fraud) that often allows risky transactions. Risk detection platforms often require only "step-up" authentication, which the deepfake is able to pass.
After-the-fact response (alerts on anomalies or unusual patterns) that fails to prevent fraud before it occurs
Login-focused detection with insufficient post-login session protection
Fragmented detection with different systems across mobile and website interfaces
Even one-time biometric checks, such as facial recognition, fingerprinting, or voice verification during login, aren't sufficient. Defeating deepfake fraud increasingly requires robust, continuous biometric verification.
Biometric Liveness Detection and Matching: The Solution to Deepfake Fraud
When a bank deploys a solution that enables Biometric Liveness Detection throughout the entire session, it can validate a user's identity throughout a transaction. Not just at login, but for the entire session.
For instance, if a customer initiates a video call with their bank, a live biometric verification solution can use real-time facial biometrics and liveness detection to analyze the video feed, verifying that the user is a real human, not a deepfake.
This happens through authentication cues such as:
Spontaneous micro-expressions.
Gaze tracking.
Randomized liveness prompts.
Even when a deepfake appears convincing to human observers, advanced biometric solutions can flag it as synthetic before a transaction completes, halting verification and preventing attackers from proceeding to the transaction stage.
With real-time analysis of biometric liveness, deepfake fraud can be prevented without having to deploy extra layers of behavioural analysis.
Are Consumers Ready for Anti-Deepfake Bank Fraud Technology?
The short answer is yes.
Recent data indicates consumers are increasingly comfortable with biometric authentication:
According to data from surveys conducted in 2023 and 2024:
43% of younger consumers already use facial recognition regularly.
55% of companies are deploying biometric verification right now.
Moving Forward: A New Approach to Fraud Prevention
The current fraud detection model wasn't built to handle deepfake and AI-powered fraud. Traditional systems continue to drain resources, frustrate customers, and miss evolving threats.
It's time to shift from detecting fraud after it happens to blocking it in real time.
A preventative approach to deepfake fraud offers the most sustainable method for reducing current and future fraud losses while simultaneously improving the customer experience.
Our team partners with banks to deliver real-time deepfake fraud prevention that works across all channels without adding friction to the customer journey.
Contact us today to learn how we can help protect your financial institution from the growing threat of deepfake fraud.