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.
Key takeaways:
- Deepfake fraud is redefining bank security risks: AI-generated voices and videos are now realistic enough to trick employees, customers, and even biometric systems. Fraudsters are using cloned voices and fake video calls to authorize transfers, bypass KYC, and impersonate executives.
- Traditional verification methods are failing: Voice biometrics, liveness checks, and manual reviews can’t reliably detect deepfakes. Criminals use real-time video manipulation and AI-enhanced human stand-ins, while employees still trust what they see and hear.
- Banks need layered, adaptive defenses: The most effective response combines multiple controls: AI-powered media forensics, behavioral analytics, cryptographic watermarks, multi-modal biometrics, and signed communication channels.
- Collaboration and learning are essential: Deepfake tactics evolve weekly. Banks should retrain detection models frequently, participate in fraud-sharing networks, and simulate attacks internally to test resilience.
- VALID Systems helps banks fight deepfake fraud in real time: VALID’s CheckDetect and Edge Data Consortium give financial institutions the tools to detect anomalies instantly, score transactions across channels, and share risk intelligence at scale.
What are deepfake fraud, voice cloning, and video impersonation?
"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 (audio deepfakes)
- Video impersonation (visual deepfakes)
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.
How deepfake fraudsters target financial institutions
Deepfake fraud can affect banks and financial services in several ways.
Here are the main types of attacks currently being seen:
1. Impersonating Customers for Account Takeover
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.
2. Impersonating executives (AI "CEO fraud")
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.
3. Deepfakes in onboarding and lending fraud
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.
4. Phishing and investor scams with AI impersonation
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.
Why traditional security controls are failing
Traditional anti-fraud measures are struggling in the face of deepfakes for several reasons:
- Voice authentication is breaking down: Many banks once considered voice biometrics secure, but modern voice clones now fool even top systems. What once proved identity now gives fraudsters one of the easiest ways to fake it.
- Fraudsters are fooling liveness checks: Verification tools that rely on prompts like blinking or turning one's head no longer guarantee authenticity. Real-time deepfakes can replicate those movements, and some criminals use human stand-ins improved by AI.
- Humans trust what they see and hear: Deepfakes manipulate instinct. A familiar face or voice feels credible, even when it isn't. That human bias makes employees and customers alike vulnerable to fraudsters who sound or look exactly like someone they trust.
- Legacy systems miss the signal: Traditional fraud models detect stolen passwords or unusual IP addresses, not lifelike impersonations. Deepfake scams often use legitimate credentials and appear fully authorized. By the time investigators uncover the deception, the damage has already happened.
6 advanced techniques to detect and defeat deepfakes
For banks facing a surge in synthetic impersonation attempts, these six strategies translate directly into operational controls:
1. Deploy AI-powered media forensics
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 AI video and audio analysis into call-center and KYC workflows.
- Flag inconsistencies such as unnatural blinking, static lighting, or flawless speech with no background variation.
- Use continuously trained forensic models that evolve as new deepfake techniques emerge.
- Automate alerts so that suspicious files or streams are escalated for immediate manual review.
Pro tip:
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.
2. Implement cryptographic watermarking and source validation
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:
- Require signed content for executive comms and sensitive customer videos, and reject or step up when signatures are missing or invalid.
- Prefer collaboration tools that sign live streams or attest to liveness, and record provenance alongside your workflow artifact (ticket, case, payment).
- Educate internal teams: "If it's important, send it signed." Roll out CAI tooling in creator apps and enforce checks at ingest.
3. Strengthen identity proofing with multi-modal biometrics
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:
- Use cross-modal authentication that evaluates face and voice simultaneously.
- Introduce one-time "liveness challenges" (like speaking a random phrase or performing a motion on camera).
- Issue cryptographic liveness tokens that deepfakes can't predict or reproduce.
- Escalate any identity verification that fails consistency checks across multiple signals.
4. Operationalize a continuous learning loop
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:
- Feed confirmed fraud cases back into model training pipelines.
- Participate in fraud intelligence-sharing consortiums to gain early warning of new attack vectors.
- Update AI models monthly with the latest synthetic data sets.
- Conduct quarterly “red team” simulations to test resilience against new cloning tools.
5. Shift trust to signed channels for high-risk actions
"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:
- Approve wires/limits via signed in-app messages (bank app PKI) instead of phone or email.
- Require two independent verifications for first-time recipients over a threshold: signed in-app + out-of-band secure callback.
- Mark executive comms accounts as "high-risk identities" and force CPA-signed video when giving payment instructions.
How VALID Systems helps banks detect and defeat deepfake fraud
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:
Real-time fraud decisioning
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.
Data intelligence at scale
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.
Machine learning and behavioral analytics
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.
Unified fraud strategy
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.