Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
For 2025, financial-sector experts forecast a fraud environment in which generative AI could help attackers automate phishing and impersonate people by voice or video, while also helping institutions detect suspicious activity. They expected liveness checks to feature more often in digital identity verification—but as one layer of protection, not a stand-alone answer. These were forecasts, not a measured account of how common each threat became.
What did experts predict fraud would look like in 2025?
In a forecast roundup, Dan Barta, SAS principal industry consultant for enterprise fraud and risk strategy, described generative AI as “both a curse and a blessing, used by both the fraudster and the fraud fighter.” The central prediction was a contest: the same broad class of technology could help produce deceptive content and help financial institutions identify suspicious behavior.
Barta warned that AI-generated voice and video could make impersonation harder to spot. The roundup did not quantify how often such attacks occurred or establish that deepfakes were impossible to detect. Thomas French, a SAS senior financial industry consultant for fraud, separately forecast that fraud-as-a-service and inexpensive, accessible AI tools could lower the effort and expertise needed to automate phishing and other digital schemes. These are named experts’ assessments, not measured prevalence findings.
How might generative AI change fraud?
Voice and video impersonation
Synthetic or altered voice and video can be used to make an impersonation more convincing. Barta identified this as a concern in a forecast for 2025; the cited roundup does not establish an economy-wide deepfake fraud rate. A convincing recording should therefore be treated as one signal, not proof of identity.
#1 Best Overall
More accessible, scalable schemes
French’s forecast was that fraud-as-a-service and accessible generative-AI tools could make it easier to automate phishing and related digital schemes. The practical concern is reduced friction for attackers, rather than a claim that every scammer uses AI or that automation guarantees success.
AI on the defensive side
AI is also used in financial services for work such as fraud detection and money-laundering prevention. The related risk context emphasizes that organizations need data capabilities and oversight, including the ability to explain AI-supported decisions. A model’s output is not self-explanatory: institutions still need governance around how decisions are made and handled.
Rank #2
- 78 pages (45 self-teaching + 33 quizzes/answers)
What is liveness testing, and what can it do?
Liveness testing examines a biometric sample to assess whether it comes from a live person rather than a presentation such as a photo, video, or mask. Barta forecast that liveness checks would become more common within multifactor digital identity authentication.
Liveness is a check on the presented biometric, not a complete identity or fraud assessment. It should be considered alongside other authentication factors and appropriate review. The roundup does not provide comparative product testing, so it does not establish that a particular liveness method is more accurate or less intrusive than another.
How can banks and other institutions respond?
The forecast themes point to layered controls rather than reliance on one detector. The appropriate mix depends on the attack being addressed; the sources do not rank products or provide measured effectiveness comparisons.
| Control area | What it addresses | How to use it | Trade-offs to assess |
|---|---|---|---|
| Identity and liveness checks | Impersonation using a biometric presentation, including a photo, video, or mask | Use as part of multifactor authentication, not as the sole basis for trust | Customer friction, false rejection, privacy, and how the check fits other factors |
| Device, activity, and transaction signals | Suspicious behavior around account access or payments | Combine relevant signals with identity checks and human review where warranted | Data quality, explainability, oversight, and the consequences of a mistaken decision |
| Phishing and digital-scheme controls | Attempts to deceive users or automate digital fraud | Build detection and response around the organization’s exposure and available data | Data capability and the need to explain AI-supported decisions |
This is a decision framework, not a performance comparison: the cited sources support the relevance of these control areas but do not provide test results for specific tools. Institutions evaluating controls should ask what attack each addresses, how it complements other factors, what happens when a legitimate customer is flagged, and whether the decision can be explained and governed.
Rank #4
What do the available fraud figures actually show?
Fraud percentages are meaningful only with their publisher, dataset, and time period attached. Veriff’s Identity Fraud Report 2026 describes its own 2025 verification activity; its figures should not be read as rates for all consumers, banks, or online transactions.
Free tools Windows power users keep installed
One-click scans. No signup required.
| Reported figure | What it measures | Scope and limitation |
|---|---|---|
| 4.18% | Verification attempts Veriff classified as fraudulent | Veriff customer verification data for 2025, reported in its Identity Fraud Report 2026; not an economy-wide fraud rate |
| 300% more likely than in 2024 | Likelihood that digitally presented media was AI-generated or altered | Veriff’s 2025 data as reported in its Identity Fraud Report 2026; this is not a claim that all deepfake fraud rose by 300% |
A Vouched 2024 forecast roundup also cited more than $3.1 trillion in illicit funds flowing through the global financial system. The passage does not establish the primary study or its methodology, so the figure should not be treated as a verified measurement here.
Quick Recap
How should readers interpret 2025 fraud forecasts?
- Separate forecasts from outcomes. SAS experts described risks they anticipated for 2025; that does not establish how prevalent those threats became.
- Keep each statistic tied to its dataset. Veriff’s reported rates describe verification activity on its platform, not the general population.
- Expect layered defenses. Liveness can contribute to identity checks, but the forecast presented it as part of multifactor authentication.
- Account for oversight. AI-supported fraud decisions raise explainability and governance concerns as well as detection opportunities.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
