Criminals can use generative AI to make phishing and financial-fraud messages more convincing, but that is different from an AI independently deciding to steal credit-card numbers. Current FBI complaint figures do not show how many card-fraud cases involved AI. For protection, AI-assisted defenses can complement—not replace—practical controls such as multifactor authentication, secure email systems, and endpoint protection.
Is AI stealing credit-card numbers?
The more accurate concern is that a criminal may use AI to help steal them. Generative AI can produce convincing text for social engineering, phishing, and financial fraud. In a December 3, 2024 public service announcement, the FBI Internet Crime Complaint Center (IC3) warned: “Criminals use AI-generated text to appear believable to a reader in furtherance of social engineering, spear phishing, and financial fraud schemes such as romance, investment, and other confidence schemes or to overcome common indicators of fraud schemes.”
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That warning describes people using AI as a tool; it does not establish that an AI system independently chose to steal cards. A persuasive message might try to get someone to reveal account details, click a malicious link, or approve a fraudulent request. The presence of AI in a scam does not by itself tell you how the theft happened or whether a particular message is genuine.
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The FBI IC3’s 2025 annual report lists 22,364 complaints with the “AI Related” descriptor and 18,774 complaints classified as Credit Card/Check Fraud. These are separate measures, not a cross-tabulation: the report does not establish how many of the card-fraud complaints involved AI. The AI-related figure is not a count of AI-caused credit-card theft; the descriptor concerns the medium or tool used to facilitate a crime.
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For broader context, the Federal Trade Commission reported $12.5 billion in fraud losses in 2024. That total aggregates losses reported by consumers who reported fraud; it is not limited to AI-enabled scams or credit-card fraud. The FTC also said losses to scams that started online exceeded losses from more traditional contact methods.
AI-supported security versus traditional cybersecurity
These are not mutually exclusive approaches. Traditional security controls can block or resist common attack paths, while AI may help defenders analyze activity or support detection and response. NIST describes AI as having potential to augment defensive capabilities while emphasizing the need to manage its risks. Whether a control helps depends on the threat, how it is deployed, and how it is configured.
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There is no controlled head-to-head comparison in the sources cited here that proves AI-based cybersecurity is universally better than traditional controls. A useful comparison is what each control does and where it can fail:
| Security job | Examples of controls | What to keep in mind |
|---|---|---|
| Resist unauthorized account access | Multifactor authentication (MFA), including phishing-resistant FIDO authentication | Protection depends on enabling the control for the account; support varies by service. |
| Authenticate email | Email authentication protocols | This is one layer in a broader defense against deceptive messages, not a guarantee that every message is safe. |
| Detect activity on devices | Endpoint detection and response (EDR) | Detection depends on deployment and configuration, and suspicious activity may still require review. |
| Limit access across systems | Zero-trust access principles | Access decisions need to be applied to the systems and accounts they are meant to protect. |
| Support defensive analysis | AI-assisted analysis and detection | AI can augment defensive work, but introduces risks and is not established as a standalone replacement for security fundamentals. |
CISA’s guidance on sophisticated AI-enabled phishing recommends layered measures including MFA—preferably phishing-resistant FIDO authentication where supported—EDR, email authentication, and zero-trust principles. That guidance appears in an election-security context; it is useful for understanding defensive controls, but it is not evidence about the prevalence of consumer card fraud.
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How to reduce the risk of account and card fraud
- Enable MFA on important accounts. Start with email, financial, shopping, and social accounts. Where a service supports it, prefer phishing-resistant FIDO authentication. A FIDO2-compatible hardware security key is one option, but check that the account supports it.
- Use unique passwords. A password manager can help create and store a different password for each account. Its vault holds sensitive information, so protect it with a strong master passphrase and enable MFA on the manager where available.
- Slow down when a message creates urgency. Treat unsolicited urgent requests and unexpected links cautiously, even if the wording looks polished or personal.
- Verify sensitive requests independently. Contact the person or organization through a known, trusted channel rather than replying to the message or using its link. Be especially cautious about requests to share credentials, payment details, or authentication codes.
These steps reduce exposure; none guarantees that an account or card cannot be compromised.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Frequently Asked Questions
Can AI-generated phishing emails fool me?
Yes. Generative AI can help criminals write believable social-engineering and phishing messages. Verify sensitive requests through a known, trusted channel instead of relying on how polished a message looks.
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Is AI cybersecurity better than traditional cybersecurity?
The evidence cited here does not establish a universal winner. AI may augment defensive capabilities, while controls such as MFA, email authentication, endpoint detection and response, and zero-trust access address different security jobs.
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