For 2025, financial-sector experts forecast a contest: fraudsters could use generative AI to scale phishing and impersonate people with synthetic voices or video, while banks could use AI and stronger identity checks to detect suspicious activity. These were forecasts, not proof that every predicted trend occurred. The defenses discussed—especially liveness checks—were expected to work as parts of layered authentication, not as guarantees against fraud.
What did experts predict fraud would look like in 2025?
In a 2025 forecast roundup, Dan Barta, Principal Industry Consultant for Enterprise Fraud and Risk Strategy at SAS, summed up the two-sided role of generative AI: “Generative AI is both a curse and a blessing, used by both the fraudster and the fraud fighter.” The forecast was that the same broad capabilities could help criminals create convincing impersonations and help financial institutions identify risk.
Voice and video impersonation
Barta warned that generative AI could produce voice and video deepfakes that are increasingly difficult to detect. This is an expert assessment in a forecast roundup, not a measured estimate of how common deepfake fraud was or evidence that all such material is undetectable. SAS’s 2025 fraud predictions describe the concern and the proposed role for liveness checks.
More accessible, automated schemes
Thomas French, Senior Financial Industry Consultant for Fraud at SAS, forecast that fraud-as-a-service and inexpensive, accessible generative-AI tools could lower the effort and expertise needed to automate phishing and other digital schemes. That describes a potential lowering of barriers, not a claim that every fraudster uses these tools or that all attacks became automated.
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How can deepfakes challenge identity checks?
A voice or video that appears to come from a real person can complicate remote identity checks and attempts to confirm who is making a request. A biometric match alone may not establish that the sample comes from a live person in front of the device: it could be a photo, a recording, a video, or another representation.
The SAS roundup describes liveness testing as assessing a biometric sample to distinguish a live person from a fake representation such as a photo, video, or mask. Barta forecast that liveness tests would become more common in multifactor digital identity authentication. The forecast does not make liveness a standalone answer; it is one check that can be combined with other authentication and review measures.
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Which defenses were identified, and what do they address?
The forecast material points to a layered approach rather than a single tool. The following distinction is useful when considering controls; it is not a ranking of products or a claim that any one control prevents every attack.
| Control or capability | What it checks | Fit and limitation |
|---|---|---|
| Multifactor authentication | More than one factor in a digital identity check | The SAS roundup forecast wider use of liveness within multifactor authentication; it did not establish a specific configuration or comparative effectiveness. |
| Liveness testing | Whether a biometric sample appears to come from a live person rather than a representation | Relevant to photo, video, or mask-based spoofing, but described as one component of identity authentication, not a complete fraud-control system. |
| AI-supported fraud detection | Patterns or activity that may indicate fraud | AI is used in financial services for fraud detection and money-laundering prevention, but decisions need oversight and explainability, as discussed in the Vouched overview of fraud risk in financial services. |
| Human review and governance | Context around an alert or consequential automated decision | Supports oversight and the ability to explain AI-supported decisions; the cited material does not supply comparative test results or a universal review procedure. |
For a bank or other financial institution evaluating controls, compare what attack each measure addresses—impersonation, synthetic identity, phishing, or payment fraud—and whether it checks identity, liveness, device or activity signals, or transaction behavior. Also weigh false rejections and customer friction against privacy, explainability, and governance needs. The available forecast material does not establish which vendor or product performs best.
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What do the available figures actually show?
Fraud statistics are only meaningful when the publisher, population, time period, and measurement are clear. The figures below describe different scopes and should not be combined into one general fraud rate.
| Figure | What it measures—and what it does not |
|---|---|
| 4.18% of verification attempts were fraudulent | Veriff’s own customer verification data for 2025, reported in its Identity Fraud Report 2026. This is not a rate for all online activity, bank transactions, or the economy. |
| Digitally presented media was 300% more likely to be AI-generated or altered than in 2024 | Veriff’s comparison in the same report, based on its verification activity. It does not mean deepfake fraud overall rose by 300%. |
A Vouched roundup says that more than $3.1 trillion in illicit funds flowed through the global financial system, attributing the figure to 2024. The passage does not establish the primary study or methodology, so it should not be treated as a verified measurement of fraud losses or as directly comparable with Veriff’s verification figures. See Vouched’s financial-services fraud-risk discussion for its stated context.
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What should consumers and financial institutions take away?
For consumers
Do not treat a familiar-sounding voice or realistic video as proof that a request is genuine. If a message or call asks you to move money, share credentials, or approve an unexpected transaction, verify it through a separate, trusted channel. The forecasts identify impersonation as a risk; they do not provide a consumer checklist or establish that a particular verification method is foolproof.
For institutions
Plan for both sides of AI use: criminals may use it to make impersonation and phishing easier to scale, while detection systems may use AI to surface suspicious activity. Identity and liveness checks can contribute to layered authentication, but controls also need oversight and explainable decisions. The cited forecasts identify these priorities without supplying product-level performance data.
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The practical point is to assess controls against specific threats and operational trade-offs, rather than infer a universal trend from a single percentage. Veriff’s figures describe its own verification data; the SAS statements are forecasts made for 2025, not measured prevalence claims.
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