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AI is changing how fraudsters impersonate people and evade identity checks, not inventing fraud itself. Synthetic voices, images, videos, social profiles, and identity documents can make deception more convincing, but official complaint data do not establish what share of fraud uses AI or how much AI has caused total losses to rise.
How is AI changing fraud?
Generative AI can help scammers produce convincing material for familiar schemes: a voice that sounds like a relative, a video that appears to show a trusted person, a fabricated social profile, or an altered identity document. The purpose is often the same as in older fraud—win trust, bypass a check, or persuade someone to hand over money or sensitive information. AI can change the speed, scale, and texture of the impersonation without changing that underlying goal.
One documented concern is identity verification. In a November 13, 2024 alert, the U.S. Treasury Department’s Financial Crimes Enforcement Network (FinCEN) said it had observed an increase in suspicious-activity reports describing suspected deepfake use, particularly fraudulent identity documents intended to circumvent identity verification and authentication. That is evidence of a risk reported to financial institutions, not a population-wide estimate of how common deepfake fraud is.
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The FBI’s April 2026 release on 2025 complaints also describes scammers using fake social profiles, voice clones, identification documents, and believable videos depicting public figures or loved ones. These are tactics reported in complaints; they do not mean every persuasive scam involves AI.
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What do the reported numbers show?
The figures below come from separate reporting systems, cover different categories, and should not be combined into one estimate of fraud. Complaint counts are reports received, not a complete count of incidents or losses.
| Source and release | Reported figure | What it covers |
|---|---|---|
| FBI Internet Crime Complaint Center (IC3), 2025 Internet Crime Report data, released April 6, 2026 | 22,364 AI-related complaints and nearly $893 million in reported losses; 1,008,597 total complaints | Complaints to IC3 for 2025. The AI-related figures are reported complaint totals, not a complete count of AI-caused fraud. |
| FBI IC3, 2024 Internet Crime Report data, released April 23, 2025 | 859,532 suspected internet-crime complaints and more than $16 billion in reported losses, a 33% increase from 2023 | Reported internet crime overall; the figures do not establish that AI caused the increase. |
| Federal Trade Commission (FTC), 2024 Consumer Sentinel data, released March 2025 | 2.6 million consumer fraud reports and more than 1.1 million identity-theft reports | Reports to the FTC’s Consumer Sentinel system and IdentityTheft.gov, respectively. These are not the same categories or population as IC3 complaints. |
| Government Accountability Office (GAO), citing the FBI’s 2023 Internet Crime Report | $3.31 billion in reported fake-investment scam losses in 2022 and $4.57 billion in 2023 | Reported losses for one fraudulently induced-payment scam type, not all payment fraud. |
The FBI’s AI-related complaint count is a useful indicator that people reported AI-linked cases to IC3; it cannot show the proportion of all fraud that uses AI. The FTC and FBI totals also cannot be added together because their systems, definitions, and complaint populations differ. GAO says there is no complete measure or estimate of losses from fraudulently induced payments, and underreporting is one reason the scale is difficult to establish.
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Why can AI-assisted scams be harder to spot?
Fraudulently induced payment scams manipulate someone with authority over an account into sending money. The payment may be authorized by the account holder even though deception prompted it. In GAO’s discussions with regulators and industry representatives, participants said scammers exploit emotion and vulnerability, and that generative AI can alter voices and images in ways that may make such scams harder for victims to detect.
In a December 2025 Science & Tech Spotlight, GAO also described malicious uses of generative AI, including deepfakes and the potential for paired generative systems to create and deliver phishing emails autonomously. The techniques are evolving, and GAO notes that no AI system can be fully secured. The possibility of automation is a reason to prepare for changing tactics, not proof that all phishing is automated or AI-generated.
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How can consumers verify a suspicious request?
When a request to send money or share sensitive information feels urgent—even if it appears to come from someone familiar—pause and verify it through a second channel you obtain independently. Do not rely on the phone number, link, or contact details supplied in the suspicious message.
- Stop the immediate action. Do not transfer money, share a code, or disclose account information while you are being pressured to act.
- Contact the person or organization independently. Use a number or channel you already know or find separately, rather than replying to the message or using its contact details.
- Check the request, not just the voice or video. Ask the supposed sender to confirm the specific transaction or request through that independent channel. Familiar-sounding media alone is not verification.
- Preserve relevant details. Document the interaction and keep messages or transaction information that may help your financial institution or investigators understand what happened.
- Contact your financial institution if money or account information is involved. The FBI advises doing so and filing with IC3 when cyber-enabled crime is suspected. Reporting can help law enforcement identify trends, but it does not guarantee recovery.
Where can organizations intervene?
No single check can reliably stop every synthetic identity or impersonation attempt. Controls work at different points: onboarding can challenge fraudulent identity documents, communication channels can help expose impersonation, payment processes can create time for verification, and reporting can support investigations and trend analysis.
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| Intervention point | Who can act | What the control can do | Important limit |
|---|---|---|---|
| Identity onboarding and authentication | Financial institutions and identity-verification providers | Monitor for red flags associated with deepfakes or fraudulent documents and strengthen verification procedures. | FinCEN describes observed typologies and red flags; it does not establish that one check or detector is foolproof. |
| Communication and impersonation | Consumers, businesses, telecommunications firms, and social platforms | Encourage independent verification, staff awareness, and reporting of suspicious activity or accounts. | A believable voice, image, or profile is not proof that the person or request is genuine. |
| Payment initiation | Banks and payment providers | Educate customers, train staff, and, for certain payments, add delays that give customers time to verify legitimacy. | A delay can create an opportunity to check; it cannot establish on its own whether a payment is fraudulent. |
| After an incident | Consumers, financial institutions, and law enforcement | Document and report cases so investigators can examine individual incidents and identify trends. | Complaint data are incomplete, and reporting does not guarantee a refund or recovery. |
For financial institutions, FinCEN’s November 2024 alert calls for vigilance around suspected deepfake use, monitoring relevant red flags, and reporting suspicious activity under applicable Bank Secrecy Act requirements. For the wider ecosystem, GAO reports that financial-industry representatives considered coordination with telecommunications providers, social media companies, and law enforcement necessary to reduce risk: a scam may begin on one service, exploit trust built elsewhere, and end in a payment channel.
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What is still unknown about AI-enabled fraud?
The official sources cited here do not establish what share of all fraud is AI-enabled, how much AI has contributed to changes in overall fraud losses, or that any particular consumer product reliably detects all synthetic media. Complaint totals depend on people reporting cases and on how agencies classify them; payment-scam losses are not completely measured. The sound conclusion is that AI is a documented tool in some fraud schemes, while its total contribution to fraud remains unquantified by these sources.
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