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AI appears to have made major operations faster, more convincing and more profitable. Chainalysis found that operations linked on-chain to AI vendors averaged about $3.2 million in revenue, versus $719,000 for comparable operations without that link. That association is significant, but it is not a controlled test proving AI caused the difference.
What the $17 billion figure actually means
Chainalysis published its analysis on January 13, 2026. At that point, its blockchain attribution identified at least $14 billion in cryptocurrency transfers to scam and fraud addresses during 2025. Because analytics firms routinely identify previously unattributed wallets after the transactions occur, Chainalysis expects the eventual total to rise above $17 billion.
The estimate measures on-chain inflows to identified or suspected scam infrastructure. It can exclude cash or bank transfers, scams whose proceeds never touch a traceable blockchain, unreported incidents and wallets that have not yet been identified. It is therefore best described as a projected total of crypto scam and fraud inflows, not a complete global survey of victim losses. See Chainalysis’s methodology and estimate.
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| Figure | What it measures | How to interpret it |
|---|---|---|
| At least $14 billion | Crypto sent on-chain to scam and fraud addresses identified for 2025 | Observed minimum at the time of analysis |
| More than $17 billion | Chainalysis projection after additional wallet attribution | Estimated eventual total, not a finalized audit |
| $17.697 billion | Cyber-enabled fraud losses reported to the FBI’s IC3 in 2025 | All-category, complaint-based context—not crypto-specific |
The FBI recorded 452,868 complaints in its 2025 IC3 report and reported $17.697 billion in losses. That dataset covers many forms of internet fraud, uses reported complaints rather than blockchain attribution, and has different geographic and definitional boundaries. The two figures must not be added together or presented as confirmation of one another. The FBI report also cannot capture fraud that victims never report.
How AI changed the scam business
AI is an operational multiplier rather than one single scam category. Criminals can use it at almost every stage:
- Generate polished, localized messages and translate conversations instantly.
- Clone voices or create deepfake video of executives, public figures or supposed support staff.
- Run chatbots that maintain many conversations at once and tailor scripts to a target’s job, location or interests.
- Create fake trading dashboards, testimonials, advertisements and social profiles.
- Produce synthetic identity documents and other materials intended to defeat verification.
- Automate recruitment, money-mule coordination and portions of laundering operations.
Chainalysis identifies deepfakes, voice cloning, phishing bots, fake trading platforms, social-platform impersonation and AI-assisted pig-butchering as prominent uses. A wallet’s connection to an AI vendor is an analytical indicator; it does not prove that every message, payment or criminal involved AI. More detail is available in Chainalysis’s analysis of AI-powered crypto scams.
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AI-linked operations were more active in the Chainalysis comparison
| Metric | Operations linked to AI vendors | Other operations |
|---|---|---|
| Average revenue per operation | Approximately $3.2 million | Approximately $719,000 |
| Relative average revenue | About 4.5 times higher | Baseline |
| Median daily revenue | $4,838 | $518 |
| Average transfers per day | 35.1 | 3.89 |
The gap could also reflect differences in criminal organization, geography, victim selection, scale or access to other services. These are reported associations, not proof that adding an AI tool makes any particular scam 4.5 times more profitable.
Which scams drove the increase
Impersonation
Impersonation scams grew by more than 1,400% year over year in Chainalysis’s 2025 analysis, while average payment severity rose by more than 600%. Criminals posed as government agencies, exchanges, executives, service providers, support agents and trusted personal contacts. A familiar name, caller ID or verified-looking account is not independent proof of identity.
Pig-butchering and investment fraud
In these schemes, an operator builds trust through romance, friendship, professional networking or apparent investment advice. The target is moved to a fake trading platform displaying fabricated balances and profits. When the victim tries to withdraw, the operator demands invented taxes, compliance charges or release fees. AI can personalize the relationship and keep many targets engaged, but the central mechanism remains social engineering.
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SMS phishing and government impersonation
Smishing campaigns such as the E-ZPass operation used a trusted public-service identity to send victims to phishing pages. Chainalysis attributes that ecosystem to the Darcula or “Smishing Triad” network and says it reached users in at least eight U.S. states. Similar messages may lead to credential theft, wallet-draining sites or later investment pitches.
Fake exchange and support accounts
Attackers monitor Discord, Telegram, X and other communities for people asking questions. A fake support representative then requests a seed phrase, private key, remote access or a “verification” transfer. AI-generated replies can make the interaction immediate and professional. No legitimate exchange, wallet provider, law-enforcement agency or support representative needs a seed phrase or private key.
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Deepfake giveaways and celebrity impersonation
Synthetic video and cloned voices can promote fake giveaways, investment opportunities or emergency requests. The technology changes the appearance and credibility of the pitch; it does not change the underlying rule that an unsolicited request for crypto must be independently verified.
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The typical AI-enabled crypto scam sequence
- Targeting: Lead lists, social profiles, leaked data or public posts identify a person likely to respond.
- Contact: SMS, social media, dating platforms, email or a crypto community provides the opening.
- Credibility: Generated text, a cloned voice, deepfake video or an impersonated brand establishes authority or intimacy.
- Conversion: The victim is moved to a fake investment site, support channel or wallet interface.
- Pressure: Fabricated profits, an urgent security warning, a tax demand or a limited-time opportunity prompts action.
- Authorization: The victim sends crypto, signs a malicious approval or discloses credentials.
- Laundering: Funds move through chains, bridges, exchanges, brokers, mule accounts or other specialized services.
Why crypto is attractive to organized fraud networks
Global transfers can settle quickly, and confirmed transactions are often irreversible. Victims may be manipulated into authorizing their own payments, while stablecoins provide a relatively stable asset for moving value. Public blockchains are generally pseudonymous rather than anonymous: investigators can trace flows and cluster addresses, but identifying the person controlling an address still requires cooperation from exchanges, service providers and law enforcement.
The criminal economy is increasingly modular. Phishing-as-a-service providers, deepfake and content vendors, identity-document sellers, lead generators, multilingual script teams, social-account farms, money mules and laundering specialists can supply separate parts of one operation. Chainalysis has also described links between major scams and forced-labor compounds in Cambodia, Myanmar and elsewhere in Southeast Asia. Some people working in those compounds are trafficking victims compelled to conduct fraud, not willing partners.
For scale, Chainalysis’s broader 2026 crime report says stablecoins represented 84% of illicit crypto transaction volume in 2025 and illicit addresses received at least $154 billion overall. That broader total includes sanctions evasion and other illicit activity; it is not a measure of scam losses. See the broader crypto-crime report.
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Average payments became larger
Chainalysis reports that the average scam payment rose from $782 in 2024 to $2,764 in 2025, a 253% year-over-year increase. An average can be pulled upward by a small number of very large cases, so it does not describe the typical victim. It does indicate that successful campaigns increasingly extracted larger amounts from fewer targets.
What consumers should verify before sending crypto
- Use an independent channel: Find the organization’s official website or an existing phone number yourself; do not use contact details supplied in the message.
- Protect wallet credentials: Never share a seed phrase or private key.
- Inspect every request: Do not approve unfamiliar token permissions or sign a transaction you cannot explain.
- Check the destination separately: Confirm the full address through a second channel. A familiar name or ENS-style label is not proof of ownership.
- Resist urgency: “Move funds to safety,” “pay a tax” and “act now” are common pressure tactics.
- Ignore screenshots: Balances, charts and withdrawal confirmations on a website can be fabricated.
- Use test transfers carefully: A small transfer does not make a malicious smart-contract approval safe.
- Beware recovery offers: Guaranteed-recovery promises are often a second scam.
- Preserve evidence: Save transaction hashes, wallet addresses, messages, URLs, usernames, phone numbers and screenshots, then report promptly.
If you already sent funds
- If you still control the wallet, revoke suspicious token approvals and move remaining assets to a newly created wallet. Never reuse an exposed seed phrase.
- If the seed phrase or private key was exposed, treat the wallet as compromised even if no funds have moved.
- Contact any receiving exchange immediately with the transaction hash and destination address.
- Ask a stablecoin issuer whether it has a formal freeze or seizure process, without assuming a freeze will occur.
- Report to appropriate law-enforcement and fraud-reporting channels. Reporting does not guarantee recovery.
- Do not pay an alleged investigator, lawyer, hacker or recovery specialist in advance without independently verifying credentials.
What exchanges and financial institutions are doing
Institutional defenses combine wallet-risk intelligence, transaction screening, behavioral signals, intervention before settlement and information sharing with investigators. Chainalysis acquired the AI-powered fraud-detection company Alterya on January 13, 2025; Alterya said it monitored $8 billion per month across crypto and fiat rails, a vendor-reported product signal rather than an independent performance audit. OKX later announced adoption of Alterya for proactive scam prevention. Product details are described by Chainalysis and OKX’s adoption announcement.
These systems can flag known infrastructure and suspicious destinations, but they are not guarantees. Detection tools can miss new infrastructure, generate false positives and struggle when criminals use legitimate services. Public-ledger traceability can aid recovery and prosecution, while irreversible consumer-authorized transfers remain a major weakness.
The bottom line on the $17 billion headline
The most accurate summary is that Chainalysis observed at least $14 billion in 2025 crypto-scam and fraud inflows and projects that attribution will push the total above $17 billion. AI helped industrialize the most profitable operations, but no available figure assigns the entire projected total to AI. The FBI’s separate $17.697 billion number covers reported cyber-enabled fraud of all kinds and should not be combined with the Chainalysis estimate.
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