How Can Banks Use Deepfake Detection?

Where Do Deepfakes Hit a Bank?

Deepfakes reach a bank through four doors: remote account opening, account access and recovery, payment authorisation, and scams aimed at customers. Each door uses a different kind of media, so each needs a different check.

Account opening is the best documented. In alert FIN-2024-Alert004, published on 13 November 2024, the US Financial Crimes Enforcement Network said that since 2023 it has seen more suspicious activity reports describing deepfake media. Criminals altered or generated photos for driver's licences and passports, combined them with stolen or invented personal data to build synthetic identities, and then used the accounts to receive and launder proceeds from other frauds, including as funnel accounts.

Account access is the second door. The FBI's Internet Crime Complaint Center warned on 3 December 2024 that criminals use cloned voices to impersonate account holders and gain access to bank accounts. Payment authorisation is the third: FinCEN's alert points to the widely reported February 2024 case in Hong Kong where a finance worker paid out about $25 million after a video call with a deepfaked chief financial officer.

The fourth door sits outside the bank's own channels. Family emergency, romance and investment scams use synthetic voices, faces and videos to persuade customers to send money themselves. Deloitte's Center for Financial Services estimated in May 2024 that generative AI could push US fraud losses from $12.3 billion in 2023 to $40 billion by 2027, a 32% compound annual growth rate.

How Do Banks Detect Deepfakes in Practice?

Banks detect deepfakes by sending the media a customer submits to a detection model at the moment of capture, scoring it, and acting on the score before the session completes. A selfie or short video arrives from the mobile app or web journey, the detector checks it for face manipulation, signs of a generative model and pixel-level edits, and returns a risk score with the signals behind it. The onboarding or recovery flow then continues, steps up to another check, or pauses for review.

The other half of detection happens after the fact. FinCEN reports that institutions often find deepfake identity documents when they re-review account opening files, and that reverse image searches sometimes match an ID photo to an online gallery of AI-generated faces. Accounts showing rapid transactions, high chargeback volumes or payments to higher-risk payees soon after opening are the usual trigger for that second look.

FinCEN's list of red flags is a practical specification for what to log. It includes a photo that is internally inconsistent or does not match the customer's stated age, a customer who uses a third-party webcam plugin during a live check, repeated technical glitches or requests to switch channels mid-verification, a refusal to use multifactor authentication, and a photo or video flagged by deepfake detection software. Our guide to remote bank customer onboarding shows how to wire those signals into one decision.

How Can Financial Organizations Detect and Prevent Deepfake Threats?

Financial organizations prevent deepfake fraud by placing detection at the points where media releases money or access and by backing every flag with a procedure that does not rely on the same channel. A practical rollout follows these steps:

Two design choices matter more than the model. Liveness alone does not answer whether the camera feed was real, because an attacker can inject a synthetic stream through a virtual camera and present a convincing live face. And a detector placed only at onboarding misses the larger losses that come later, when a cloned voice or deepfaked executive requests a payment.

• Map every journey where a customer or employee submits a face, voice or video, and rank them by the money or access each one releases.
• Run detection inline at account opening and recovery, and in batch on media submitted for disputes and fraud cases.
• Pair detection with liveness, injection and device checks so a clean score on an injected stream does not pass on its own.
• Require a callback to a number already on file, or a second approver, for payee changes and payments above a set value.
• Set score thresholds per journey from the cost of each error, and send uncertain scores to trained reviewers.
• Keep the score, signals, model version and decision with the case so investigators and SAR filers can use them.

How Do You Train Bank Employees to Recognize Deepfakes?

Train bank employees to follow a verification procedure whenever a request carries financial or access risk, because people cannot reliably tell real media from fake by looking or listening. In a February 2025 study by iProov, a biometrics vendor, only 0.1% of 2,000 UK and US participants correctly classified every real and fake image and video, and participants were 36% less likely to spot a fake video than a fake image. In a 2023 PLOS ONE study of 529 listeners, people identified speech deepfakes 73% of the time, and practice with examples improved accuracy by under 4 percentage points.

Effective programmes teach staff to treat urgency, secrecy and a sudden change of channel as the warning, whatever the face or voice looks like. They give contact centre agents, relationship managers and treasury staff a short script: end the call, ring back on a number from the bank's own records, and require a second approver for unusual payment instructions. The FBI's December 2024 guidance gives the same advice to customers, along with agreeing a code word with family members.

Run drills at least once a year with realistic synthetic voice and video, measure who followed the callback procedure, and update scripts when attackers change tactics. Our explainer on whether humans can detect deepfakes covers the research behind this approach.

Where Detection Helps and Where It Does Not

Detection helps most where the bank receives the media directly: selfies and videos at onboarding, recovery selfies, recorded calls and evidence files. In those places a model can read pixel, frequency and audio patterns that no reviewer can see, and it can do so on every session rather than a sample.

It helps least when the deception happens outside the bank's systems. If a scammer calls a customer with a cloned relative's voice and the customer then makes a payment in their own app, the bank never sees the deepfake. Transaction monitoring, payment friction and customer education carry that risk. Detection also cannot catch an insider who approves a fraudulent account, and a strong score on a single image says nothing about whether the identity document behind it is genuine.

Results also depend on input quality. Heavily compressed video, very short clips and poor lighting give any detector less signal, which is why thresholds and review queues should be tuned on the bank's own traffic during a proof of concept.

How Neural Defend Supports Bank Deepfake Detection

Neural Defend's NeuroVerify checks a face image, video or audio clip in one API call and returns one risk score with signal-level evidence. Six signals run in the same request: visual safety screening, live presence verification, face manipulation detection, AI generative model analysis, AI pixel-level manipulation and AI content compliance. Eligible image requests return in under 200 ms at p95 under defined test conditions, so the check fits inside an onboarding or recovery step without adding a visible wait.

On the public VendorBench-100 benchmark of 100 adversarial still images, Neural Defend ranked first of 36 systems with an MCC of 0.876, against 0.646 for Reality Defender in second place. The benchmark does not test video, audio or injection attacks, and one of its authors also co-wrote the paper on Neural Defend's ARCAS 1B model; the corpus and results are public so any bank can rerun them. You can compare vendors in more detail in our review of deepfake detection companies for banks.

Banks can run NeuroVerify as a managed cloud API, in a private cloud or VPC, as a secure container, on-premises or air-gapped. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701 and ISO/IEC 42001 certification and a SOC 2 Type II report. SDKs cover Android, iOS and Flutter on mobile and Python, TypeScript, Go and Java on the backend. Fraud investigation teams can use NeuroDetect to examine evidence files with heatmaps and a downloadable forensic report, and contact centres can trial voice checks with NeuroCall, currently in Beta. NeuroVerify does not analyse ID documents, so it sits next to your existing document verification provider.