Modern banking runs on recognition. A familiar voice authorizes a transfer. A smiling face passes a video check. A short clip confirms that a customer is who they claim to be. These signals feel natural, which is precisely why they're trusted.
That trust is now on test. Synthetic media allows criminals to clone an executive's voice from a short phone recording, manipulate facial movements in real time, and slip through "liveness" checks with pre-recorded video.
In one recent case, a UK firm wired more than US$25 million after speaking to what seemed like known colleagues on a group call. None of them were real.
This article breaks down how deepfake fraud works, why it's spreading through financial systems, and what steps institutions can take to strengthen verification before trust itself becomes a liability.
"Deepfakes" refer to synthetic media created or augmented by AI to impersonate real people in a highly believable way.
In the banking context, deepfake fraud means using AI-generated voices or videos to deceive financial institutions or their customers for illicit gain. This can take two primary forms:
Voice cloning uses AI models to replicate someone's speech patterns, accent, and tone. Video deepfakes use techniques such as Generative Adversarial Networks (GANs) to superimpose one person's likeness onto another's body or to create a lifelike talking head of the target.
Essentially, criminals can now "copy a voice, image, or even a video" with very little effort or skill.
The quality of these forgeries has advanced rapidly. AI voice generators can mimic a person's voice with only a few seconds of audio. That survey also found 70% of people aren't confident they can tell a cloned voice from the real thing. Meanwhile, researchers have shown that deepfake audio can bypass voice authentication systems with up to 99% success after only a handful of attempts.
Video deepfakes are likewise becoming indistinguishable from authentic footage as AI algorithms improve. If deepfakes can fool internet users, they can also fool bankers and clients – especially when fraudsters deploy them during high-pressure moments, such as wire transfer requests.
Deepfake fraud can affect banks and financial services in several ways.
Here are the main types of attacks currently being seen:
Fraudsters use AI voice clones to pose as legitimate banking clients over the phone. For example, a criminal armed with a few voice samples of a high-net-worth individual could call the bank's customer service line, sound exactly like the client, and instruct the representative to reset the client's account credentials or initiate a transfer.
In one demonstration, researchers noted that with just a short audio sample, criminals could impersonate "a close relative in a crisis" or even "a high-value bank client, seeking to complete a transaction". This kind of deepfake vishing (voice phishing) circumvents traditional identity checks.
Real example: In Arizona, a mother received a phone call from someone whose voice sounded exactly like her 15-year-old daughter, crying for help - a deepfake voice cloning kidnapping hoax. She was nearly convinced until she confirmed her daughter was safe.
That incident underscores how voice cloning already exploits emotional pressure – a tactic fraudsters can repurpose for financial impersonation.
Deepfake technology has turned the old "CEO scam" into one of the most dangerous forms of corporate fraud. Instead of fake emails, criminals now use realistic voice and video clones of executives to pressure employees into wiring money or revealing confidential information.
Real-life example:
The most striking example came in early 2024, when a deepfake video conference deceived the British engineering firm Arup. A finance employee joined a call that appeared to be routine with the company's CFO and several senior colleagues. Every face and voice on screen looked authentic. In reality, none of them were real. Over the course of 15 transactions, the employee transferred US$25 million to accounts controlled by criminals before the deception came to light.
AI-driven forgery is now creating entirely synthetic identities. Criminals are using generative tools to craft fake identity documents, altered selfies, and even live deepfake videos that pass standard Know-Your-Customer (KYC) checks.
A typical scam looks like this: an applicant submits a doctored driver's license and, when prompted for "liveness verification," presents a deepfake video built from stolen photos or social media clips. To a human reviewer, or even an automated verification system, the footage looks legitimate. Once approved, the fraudster can open accounts, apply for loans, or set up money-mule profiles that blend seamlessly into the system.
FinCEN published an alert in November 2024 warning that criminals are "altering or generating images used for identification documents" (such as passports, driver's licenses, etc.) to bypass identity checks at financial institutions.
The alert says that, beginning in 2023 and continuing into 2024, financial institutions have reported suspicious activity involving deepfake media used to circumvent identity verification and authentication steps.
Deepfakes have supercharged social engineering. What once came as a spam email now arrives as a believable video or voice message from someone the victim trusts. Scammers are using AI to impersonate financial advisors, CEOs, and even celebrities to manipulate markets and steal money.
These tactics are evolving quickly. Criminals now record fake "urgent" video messages that appear to come from real wealth managers or executives. The goal is to push clients into immediate action – transferring funds or buying into fraudulent opportunities before they can verify the source.
Real-life example:
In 2024, scammers spread deepfake videos of British TV doctor Michael Mosley that made it appear he was promoting a fake investment scheme. The videos spread across social media, driving unsuspecting investors to deposit funds into fake trading platforms. Authorities later confirmed that the doctor had no involvement and that the footage had been entirely AI-generated.
Traditional anti-fraud measures are struggling in the face of deepfakes for several reasons:
For banks facing a surge in synthetic impersonation attempts, these six strategies translate directly into operational controls:
Modern deepfakes leave subtle, machine-made "tells" in video and audio. You won't spot them consistently by eye or ear, so put detectors in the path of risky workflows (contact centers, video KYC, executive communications).
Action steps:
Integrate transaction based risk scoring into your existing fraud decisioning layer. VALID Systems' CheckDetect can use behavioral and transaction based data to identify out of pattern behavior and trigger instant holds or step-ups before funds move.
Instead of only catching fakes after the fact, banks can verify authentic content at its source. Watermarking adds verifiable metadata indicating whether an audio or video file was generated by AI or captured live.
What to implement:
Basic liveness (blink, turn head) is increasingly replicable or spoofable. You need dynamic challenges and cryptographic attestation from the capture device/service. Microsoft and others emphasize liveness and provenance together to counter real-time overlays and replay.
Action steps:
Deepfakes evolve weekly. Your defenses must update the same way. FinCEN's 2024 alert explicitly warns U.S. institutions about synthetic media across KYC and authentication, and recommends pattern-sharing and model updates.
What to implement:
"Call back to verify" isn’t enough, because the call itself can be the attack. Move approvals into channels you control and can cryptographically attest.
What to implement:
In battling deepfake fraud, one of the most powerful tools in a bank’s arsenal is AI-driven fraud scoring and cross-channel risk detection. While historically much of that has been in areas like check fraud, the underlying technology – real-time scoring, cross-institution intelligence sharing, behavioral analytics – is directly applicable to detecting deepfake modus operandi.
VALID Systems focuses on real-time fraud prevention for banks and credit unions, with a strong footprint in check fraud detection and cross-channel risk scoring. The same design principles that stop counterfeit and altered checks can help contain deepfake-driven events.
Here’s how VALID Systems helps banks strengthen fraud defenses:
VALID’s CheckDetect engine scores every deposit or transaction instantly, identifying the small percentage of items that pose the highest risk. By analyzing behavioral, transactional, and payer–payee data together, the system gives banks the insight to hold or clear funds confidently, without delaying legitimate activity.
Through the VALID Edge Data Consortium, participating institutions share anonymized data covering over 450 million accounts and trillions in annual transaction value. This shared intelligence helps members spot patterns faster, such as recurring payees, duplicate items, or coordinated fraud attempts appearing across multiple banks.
VALID’s models continuously learn from confirmed fraud cases to improve precision and reduce false positives. The platform applies behavioral analytics to understand how customers and accounts typically transact, making it easier to flag anomalies early in the process.
By combining real-time scoring, adaptive analytics, and industry-wide data sharing, VALID helps banks modernize their fraud programs for a faster, smarter defense.
In short: VALID Systems helps banks use good AI to beat bad AI by analyzing billions of data points across channels to detect deepfake-driven fraud in real time, before it costs millions.
Looking to strengthen your defenses against deepfake fraud?
Book a free consultation with VALID Systems and see how our real-time decisioning tools help banks detect and stop synthetic, high-risk transactions before they cause losses.