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Banks can reduce AI-assisted cyberattack risk by layering strong identity checks, account monitoring, payment-recipient scrutiny, staff training, secure AI systems, and timely threat sharing. No voice check, deepfake detector, or other single control can guarantee that every attack will be stopped.
How AI changes the threat to banks
AI can make familiar attacks more convincing and easier to scale. The Office of the Comptroller of the Currency (OCC) says attackers have used AI to amplify phishing, clone voices, and develop malware in its 2025 Cybersecurity and Financial System Resilience Report. These tools can help an attacker impersonate a customer, employee, executive, or trusted organization; they do not replace the underlying tactics of deception and unauthorized access.
Federal Reserve Governor Michael S. Barr said in an April 17, 2025 speech that deepfake attacks had increased twentyfold over the prior three years. That is a figure cited in his speech, not a bank-specific incident rate. Barr also described a reported 2024 case in which a deepfake video meeting impersonating an Arup CFO led an employee to transfer $25 million to thieves. It illustrates the potential consequences of impersonation, but was not a bank incident.
How banks can verify identity without trusting a voice or video alone
A familiar voice or realistic video should not, by itself, prove that someone is a customer or authorized employee. Synthetic media can imitate the signals people have traditionally used to recognize one another. Barr recommends that banks evolve identity verification as deepfake threats develop.
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Banks use identity verification processes that can include multi-factor authentication (MFA) and account monitoring. The Federal Reserve-hosted interagency guidance on authentication and access to financial institution services and systems frames authentication and access management as risk-management responsibilities. It does not endorse one product or prescribe a universal control for every institution.
Barr’s speech discusses possible technical approaches such as facial recognition, voice analysis, behavioral biometrics, metadata assessment, and additional verification. These are options to evaluate, not proven standalone solutions or blanket regulatory requirements. Banks should assess how well a signal works in their own environment and combine it with other evidence rather than treating a detector’s output as conclusive.
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How banks can protect payments from impersonation and fraud
Payment controls should consider both who is sending money and where it is going. A legitimate customer account can still be manipulated into sending funds to a fraudulent recipient, so verifying the sender alone is not enough.
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- Check the sender and access context. Use risk-based authentication and account monitoring to identify activity that does not fit the customer or account.
- Assess the recipient. Scrutinize recipients for large or unusual payments, especially when the destination or payment pattern raises concerns.
- Review flagged transfers before authorization. Analytics can flag suspicious patterns for additional review; the flag is a reason to investigate, not proof of fraud.
These steps reflect controls Barr recommends in his speech. Their purpose is to add review at consequential decision points, not to promise that every fraudulent transfer will be detected.
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How staff training and threat sharing strengthen defenses
People remain targets even when attackers use sophisticated tools. FINRA’s 2025 Annual Regulatory Oversight Report recommends periodic training to help staff identify and thwart phishing and social engineering. It describes AI-generated fake content such as imposter sites, false identification documents, and deepfake audio and video.
Training should help employees recognize suspicious requests and follow established verification and escalation procedures instead of acting on urgency or a convincing voice alone. Barr also recommends better information sharing and timely reporting of cyber incidents so financial institutions and other participants can respond more quickly to emerging threats. The data and capabilities available to share may differ by institution.
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How banks should secure their own AI systems
AI can support fraud detection and cyber defense, but AI systems can also be attacked. NIST’s voluntary technical guidance, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025, March 24, 2025), covers evasion, poisoning, privacy, and misuse attacks. For predictive AI it addresses evasion, poisoning, and privacy; for generative AI it also includes misuse. The taxonomy discusses mitigations and their limitations, and is not banking-specific regulation.
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NIST’s preliminary Cyber AI Profile groups its focus into securing AI system components, conducting AI-enabled cyber defense, and thwarting AI-enabled attacks. The cited page describes the profile as preliminary, so it should not be presented as final or mandatory guidance.
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In practice, banks should include AI components in their security and risk-management processes: consider the data and systems they depend on, assess how attacks could affect their outputs, and evaluate mitigations in the institution’s own environment. A model’s output is another input to a control decision, not a guarantee of safety.
Why layered, risk-based controls matter
Different controls address different failure points: identity checks act at access, account monitoring watches activity, recipient scrutiny applies at payment authorization, and training and incident reporting support response. Banks can apply additional review where the risk or potential impact is higher while considering customer friction, compatibility, data quality, and operating needs. Barr’s recommendations and the cited technical guidance support layered defenses; they do not establish that any particular biometric, deepfake detector, security key, or vendor product will work in every live banking environment.
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