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Banks can reduce AI-assisted cyberattack risk by layering identity checks, strong authentication, account monitoring, payment-recipient scrutiny, staff training, and well-governed AI systems. AI can make phishing, voice impersonation, and malware more convincing or easier to scale, but no single voice check, biometric, or detection tool can guarantee that an attack will be stopped.
How AI changes the threat to banks
AI does not replace familiar cybercrime methods; it can make them faster, more convincing, or easier to adapt. The Office of the Comptroller of the Currency says attackers have used AI to amplify phishing, clone voices, and develop malware in its Cybersecurity and Financial System Resilience Report 2025.
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In a speech on April 17, 2025, Federal Reserve Governor Michael S. Barr said deepfake attacks had increased twentyfold over the prior three years. That is a statement in Barr’s speech, not a bank-specific incident rate. He 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 was a corporate fraud case, not a bank incident. Barr said, “Banks are frontline defenders against deepfake-enabled fraud due to their direct involvement with financial transactions and customer data.” The speech presents his views, which he said do not necessarily represent those of his colleagues.
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A convincing voice or video impression is not, by itself, proof of identity. Barr describes synthetic voice and video impersonation and urges banks to evolve identity verification as these threats develop. Banks already use identity verification processes that can include multi-factor authentication and account monitoring, but the controls should be designed so an attacker cannot get through by imitating one familiar signal.
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The Federal Reserve-hosted interagency guidance treats authentication and access management as risk-management responsibilities. It recommends identifying threats with information from sharing organizations and reviews of actual or attempted breaches, identity theft, and fraud. A bank may consider a FIDO2 security key as one authentication factor, but customers should check their bank’s policy and whether its systems support the key; the guidance does not endorse a particular product.
Barr also describes facial recognition, voice analysis, behavioral biometrics, metadata assessment, and further verification as possible technical approaches. These are examples of options discussed in a speech, not universal regulatory requirements or established standalone defenses. Each should be assessed in the bank’s own environment, including its error rates, compatibility, and the possibility that attackers can spoof or manipulate signals.
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How banks can catch suspicious payments
Payment controls should assess both sides of a transaction: whether the person initiating it is authorized and whether the recipient and payment pattern are plausible. Barr recommends scrutinizing recipients of large or unusual payments, using analytics to flag suspicious patterns, and conducting additional review before authorizing flagged transfers. That extra check can interrupt a fraud attempt even when an attacker has persuaded an employee or customer through an impersonation.
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A risk-based payment review can combine signals rather than rely on a single threshold:
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- Sender and access: Check whether the account holder, device, and access pattern fit the account’s normal activity.
- Recipient: Examine new, changed, or otherwise unusual payment destinations, especially for large transfers.
- Transaction pattern: Use monitoring to identify activity that differs from the customer or account’s established behavior.
- Escalation: Route flagged payments for further review before authorization, with a process for resolving legitimate transactions as well as stopping suspicious ones.
These are layered controls, not proof that every fraudulent transfer will be detected. Monitoring can produce false positives, and its value depends on data quality and how the bank handles alerts.
How staff training and threat sharing strengthen defenses
Phishing and social engineering remain effective because they target people as well as systems. FINRA’s 2025 Annual Regulatory Oversight Report recommends periodic staff training to identify and thwart people-centric attacks. It points to AI-generated imposter sites, false identification documents, and deepfake audio and video as examples of deceptive content.
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Training should help staff recognize suspicious requests and follow verification and escalation procedures rather than treating an urgent call, message, or video meeting as sufficient authority to move money or disclose access information. Barr also recommends timely cyber-incident reporting and improved information sharing so banks and other participants can respond more quickly to emerging threats. Available intelligence and response capabilities will differ among institutions, so each bank needs a process suited to its operations and reporting obligations.
How banks should secure their own AI systems
AI can support fraud monitoring and cyber defense, but the systems banks use can also be attacked. NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025, March 24, 2025) describes evasion, poisoning, and privacy attacks against predictive AI, as well as evasion, poisoning, privacy, and misuse attacks against generative AI. It discusses mitigations and their limitations. This is technical voluntary guidance, not banking-specific regulation.
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NIST’s preliminary Cyber AI Profile organizes its focus around securing AI system components, conducting AI-enabled cyber defense, and thwarting AI-enabled attacks. The profile is identified as preliminary on the cited page; it should not be treated as final or mandatory guidance. In practice, banks should govern AI tools according to their role and risk, protect the data and components they depend on, and evaluate how they behave under attack rather than assuming an AI detector is inherently reliable.
What an effective defense looks like
A bank’s controls need to work together across account access, ongoing activity, payment authorization, staff decisions, and incident response. The appropriate mix depends on the institution’s systems and risk profile. The cited guidance supports layered, risk-based safeguards; it does not establish that any one biometric, detector, or authentication product will stop all AI-assisted attacks.
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