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Ever wondered if the person on the other side of a video call or phone conversation is actually who they appear to be?
Until recently, seeing a familiar face or hearing a known voice was often enough to build trust. Today, that trust can be recreated.
With deepfake technology, fraudsters can imitate voices, faces and even real-time conversations with surprising accuracy. In banking, this can turn a familiar voice or seemingly genuine video call into a fraudulent payment instruction, account request or impersonation attempt.
For financial institutions, deepfake fraud creates a challenge that extends beyond detecting fake audio or video. Banks also need to recognise the unusual behaviour, suspicious transactions and beneficiary activity that may follow an impersonation attempt.
This article looks at how deepfake fraud in banking works, why conventional controls can struggle with AI-generated impersonation, the warning signs financial institutions should monitor and how deepfake fraud detection can extend beyond the synthetic media itself.
What Is Deepfake Fraud in Banking?
Deepfake fraud uses artificial intelligence to create or manipulate audio, video and images so they appear to represent a real person. Fraudsters can use deepfake technology to clone an executive’s voice, impersonate a customer during a video call or create synthetic media that makes fraudulent instructions appear legitimate.
For banks and financial institutions, these attacks can be used to support payment fraud, account takeover attempts, identity fraud and social engineering scams.
Deepfake attacks can also be combined with manipulated identity documents or synthetic identities to challenge identity verification, facial authentication and other authentication controls.
The financial impact can be significant. In 2026, fraudsters reportedly targeted an Italian private bank using messages and AI voice cloning as part of an executive impersonation scheme. The incident resulted in a series of overseas transfers worth tens of millions of euros.
Cases like this demonstrate why deepfake fraud is becoming a financial crime concern rather than simply a problem of manipulated media. FinCEN has also warned financial institutions about the use of deepfake media in fraud schemes, including attempts to bypass identity verification and authentication controls.
How Deepfake Fraud Targets Banks and Financial Institutions
Deepfake scams can appear at several points in the banking journey.
Voice Cloning and Executive Impersonation
A fraudster may use a deepfake voice or an AI-generated voice to impersonate a senior executive, customer, supplier or other trusted individual to commit fraud. In some cases, AI can create deepfakes from as little as three seconds of audio.
The cloned voice can make an urgent transfer request or change in beneficiary details appear more credible, particularly when combined with phishing emails, compromised messaging accounts, or voice messages. Scams using a senior executive’s voice are designed to create urgency and can lead to major financial losses.
These attacks also rely on social engineering tactics to pressure staff into bypassing normal verification steps. The FBI has also warned about the growing use of generative AI and voice cloning in impersonation and social-engineering scams.
Video Deepfakes
Deepfake videos can make it appear that a genuine person is participating in a video call. Liveness detection can help identify a live individual versus deepfake videos during verification.
A convincing deepfake may be built from publicly available images and then used for financial gain. This can be used to support payment fraud, account recovery attempts or fraudulent instructions that would normally require additional verification. Deepfake celebrity endorsements can also trick fans into buying fake products.
Synthetic Identities
Deepfake technology can also be combined with fabricated or manipulated identity information and deepfake images. Fraudsters may attempt to use synthetic media as fake biometric data to bypass facial authentication, liveness detection or remote identity verification processes during sensitive transactions.
While the method varies, the objective remains similar: make fraudulent activity appear legitimate long enough for the financial action to succeed.
Why Deepfake Fraud Is Difficult to Detect
Deepfake fraud works because it targets something financial institutions and their customers rely on every day: trust. AI deepfake fraud exploits human cognitive biases for deception.
A familiar voice can make an unusual request seem reasonable, which is why critical thinking matters when urgency or secrecy is used to push action. A realistic video call can reduce suspicion. A successfully authenticated account can make the transaction that follows appear genuine. That means deepfake detection alone cannot provide the full picture.
Consider a customer who passes authentication but then:
initiates an unusually large transaction,
adds a new beneficiary,
suddenly changes their normal payment behaviour,
accesses the account through an unusual device or channel, or
rapidly moves money across additional accounts.
Any one of these events might have a legitimate explanation.
When several appear together, the risk changes.
Effective fraud detection therefore needs to consider both the impersonation risk and the financial activity surrounding it, since connected fraud detection across channels may point not only to known fraud patterns but also to new fraudulent activities.
Deepfake Fraud Detection: What Should Banks Look For?
Financial institutions may not always see the deepfake itself. A fraudulent phone call, WhatsApp conversation or external video meeting can happen completely outside the bank’s systems.
The transaction created by that interaction, however, enters the financial institution’s environment. That creates another opportunity for fraud detection.
Unusual Customer Behaviour
Changes in transaction value, frequency, timing or account activity, along with customer behavior, can help identify behaviour that falls outside a customer’s normal pattern.
A high-value transfer may be normal for one business but highly unusual for another. Behavioural analytics can help provide that context by assessing user behavior signals such as device use, navigation habits and other behavioural biometrics for fraud prevention when activity departs from a normal pattern.
New or Unusual Beneficiaries
A first-time beneficiary can become more significant when combined with other fraud indicators.
Banks can assess whether the beneficiary has appeared previously, whether other customers are sending funds to the same entity and whether the account is connected to known suspicious activity.
Rapid Movement of Funds
Fraud does not necessarily end when the first payment is completed.
Funds may move quickly through several accounts, payment channels or jurisdictions. Mule accounts can also be used to receive and redistribute fraudulent payments.
Monitoring what happens after the initial transaction can therefore reveal risk that was not obvious when the payment was first made, especially when supported by continuous, AI-driven fraud monitoring.
Multiple Risk Signals
One red flag should not automatically mean fraud.
A stronger risk picture emerges when unusual behaviour, beneficiary risk, transaction patterns and connected account activity are considered together, with machine learning algorithms and anomaly detection helping connect these signals in context.
AI systems can help distinguish legitimate transactions from potentially fraudulent transactions by analyzing data across related events.
This is particularly important for deepfake scams, where the fraudulent instruction itself may appear highly convincing.
From Voice Cloning to Payment Fraud
Consider a voice cloning attack.
The deepfake may only exist for the duration of a phone call.
But if that call succeeds, it creates a much longer financial trail:
AI impersonation → fraudulent instruction → fraudulent transactions → beneficiary account → movement of funds
Each step provides another opportunity to identify risk. Fraud teams should also assess whether the activity could expose sensitive data or sensitive information, not only money movement.
A financial institution may have little visibility into how convincing the initial voice was. It can, however, assess whether the transaction fits the customer’s historical behaviour, whether the beneficiary introduces additional risk and what happens to the funds after the payment.
This is why deepfake fraud prevention cannot rely only on identifying whether media is artificial.
The activity created by the deepfake matters just as much.
How Fraud Monitoring Can Help Detect Deepfake Fraud
Modern fraud detection systems can help financial institutions evaluate multiple risk signals rather than treating every event independently.
Imagine a large payment that would not normally trigger concern.
Now add: a new beneficiary + unusual transaction timing + a change in customer behaviour + rapid onward movement of funds.
The same transaction now carries considerably more context.
Behavioural analytics, transaction monitoring, risk scoring, machine learning, and data analysis can help fraud teams identify these deviations and prioritise suspicious activity for investigation.
These fraud detection systems can analyse data in real time to spot potential AI fraud even when the attack pattern is new.
For deepfake fraud prevention, this provides an important additional layer of defence.
Even when an AI-generated voice or video successfully convinces a person, the resulting financial activity may still reveal that something is wrong.
This kind of detection approach is already familiar in the banking industry and in modern payment systems. Research into synthetic-content detection is still evolving, which is why financial institutions should avoid relying on deepfake detection alone and instead combine it with behavioural and transaction-level controls.
Strengthening Fraud Detection With ZIGRAM
Deepfake impersonation may begin outside a financial institution’s systems, but the resulting risk can surface through transactions, behaviours, accounts and connected entities.
ZIGRAM’s Fraud Fighter supports Fraud Monitoring & Detection through real-time fraud monitoring, intelligent risk rating and behaviour analytics.
These capabilities can help financial institutions identify changes in activity, assess suspicious transaction journeys and bring additional context into fraud investigations.
Where deepfake fraud also involves mule accounts, suspicious fund movement or wider financial crime risks, ZIGRAM’s Complete FRAML System connects applications across fraud, scams, mule networks, money laundering, sanctions exposure and investigations, supporting broader financial crime and compliance frameworks.
The objective is not to claim that every AI-generated voice or deepfake video can be identified. It is to recognise when the financial activity resulting from that interaction begins to look suspicious.
How Can Financial Institutions Prevent Deepfake Fraud?
Prevention works best as layered security, combining updated security protocols with advanced security measures across people, processes and systems. Financial institutions can strengthen their response by combining secure identity verification, authentication methods and access management with behavioural analytics, transaction monitoring, beneficiary risk assessment and fraud investigation; Multi-Factor Authentication (MFA) significantly reduces unauthorized access risks.
Higher-risk or unusual payment instructions may also require additional verification through trusted channels. Staff training and phishing simulations can reduce susceptibility to deepfake fraud by 52%.
Financial institutions should review fraud techniques regularly and update security protocols as cyber threats evolve. The key is to avoid relying on a single indicator of authenticity. A familiar face, voice or successfully authenticated account should be considered alongside the behaviour and financial activity surrounding it.
Deepfakes Change the Impersonation, Not the Fundamentals of Fraud
AI deepfake fraud is making impersonation easier to create and harder to recognise, and incidents surged 700% in 2023. In fintech, incidents also rose 700% in 2023, and comapnies reported a 54% increase in fraud-related Notices since 2024. But once the impersonation leads to a payment, it begins producing financial signals. Customer behaviour changes. Beneficiaries appear. Transactions move. Funds may pass through connected accounts.
For financial institutions, these signals provide an important opportunity for detection.
As deepfake scams become more convincing, stronger fraud detection will depend not only on recognising whether a voice or video is artificial, but also on understanding whether the financial activity behind it makes sense, as rising losses push organisations to invest more heavily in prevention.
Frequently Asked Questions
What is deepfake fraud?
Deepfake fraud uses AI-generated or manipulated media to impersonate real people and make fraudulent activity appear legitimate, including a deepfake voice, deepfake videos, or deepfake images used to commit fraud or obtain financial gain.
How is deepfake technology used in banking fraud?
Fraudsters can use voice cloning, video deepfakes and synthetic identities to impersonate customers or executives, support fraudulent payment instructions and attempt to bypass identity verification or authentication controls. They may also target biometric data by making fake audio or video appear legitimate during checks.
How can banks detect deepfake fraud?
Banks can combine deepfake detection and secure authentication with behavioural analytics, transaction monitoring, beneficiary analysis, and other fraud risk signals, using ai systems that apply anomaly detection and machine learning algorithms to spot unusual patterns.
