U.S. Banking Regulators Expand AI Supervision: What Financial Institutions Need to Know

U.S. banking regulators aren’t waiting for new rules to start scrutinizing how banks use AI.

The use of AI within financial institutions continues to expand across both customer-facing and back-office operations. As institutions deploy AI in areas such as credit underwriting, fraud detection and compliance monitoring, regulatory attention has increased in parallel, raising the stakes for financial institutions that haven’t yet mapped their AI risk.

U.S. banking regulators are now incorporating AI into existing supervisory processes rather than issuing standalone regulatory frameworks. This approach reflects both the pace of industry adoption and the need to evaluate risks using established oversight mechanisms while regulatory expectations continue to evolve.

AI in Bank Supervisory Examinations

AI has become an embedded topic within routine supervisory examinations conducted by federal banking agencies. Institutions are increasingly expected to identify and document where AI is used across their operations, particularly in activities that carry elevated levels of regulatory or consumer risk.

These higher-risk areas include lending decisions, customer identification processes and sanctions screening. By incorporating AI into standard examination procedures, regulators are extending traditional supervisory coverage to include emerging technologies without fundamentally changing the structure of exams.

AI Governance and Operational Controls

Regulatory focus is centering on the governance structures supporting AI deployment. Institutions are expected to demonstrate that AI systems operate within defined control environments that include oversight mechanisms and clear lines of responsibility.

Supervisory inquiries are increasingly focused on whether appropriate guardrails are in place, including human oversight of automated processes and defined escalation or intervention protocols. Institutions are also expected to maintain contingency capabilities, including the ability to suspend or disable AI-driven functions in the event of unexpected outcomes.

AI Data Management and Model Transparency

The use of AI introduces additional complexity in how institutions manage and govern data. Regulators are assessing whether institutions maintain appropriate controls over the data inputs used in AI models, including ensuring that data is accessed, processed and retained in accordance with internal policies and regulatory requirements.

There is also increasing emphasis on model transparency and explainability, particularly in areas such as lending where decisions must be understood and supportable under existing consumer protection requirements.

AI Third-Party Risk and Vendor Oversight

As financial institutions continue to rely on external providers to support AI capabilities, third-party risk management has emerged as a key area of supervisory focus. Regulators are evaluating how institutions manage risks associated with vendors that develop, host or operate AI solutions.

Institutions are expected to demonstrate that vendor risk management programs address data protection, control environments and contingency planning in the event of vendor disruption.

AI Risk Management Under Existing Regulatory Frameworks

U.S. banking regulators are applying established risk management frameworks to evaluate AI-related risks. This includes leveraging existing guidance related to model risk management, third-party risk, operational resilience and consumer protection.

This principles-based approach allows regulators to assess AI within the context of known risk categories while maintaining flexibility as the technology continues to evolve.

The integration of AI into supervisory processes reflects a broader shift in how regulators are addressing innovation within the banking sector. By embedding AI considerations into routine examinations, regulators are reinforcing that emerging technologies must operate within established governance, control and risk management frameworks.

How Schneider Downs Can Help

Financial institutions are under mounting pressure to govern AI within existing regulatory frameworks—no new rulebook, just higher expectations for transparency, controls and exam readiness. Many are still working out how to turn those expectations into practical, defensible operating models.

Schneider Downs closes that gap. Our Risk Advisory and IT Risk Advisory teams work directly with institutions to integrate AI into existing risk frameworks, strengthen governance and data controls, manage third-party AI risk and prepare for exams—helping clients adopt AI responsibly while staying ahead of supervisory expectations.

For more information, please contact our team at [email protected].

About Schneider Downs Financial Services

The Schneider Downs Financial Services industry group supports financial institutions as they navigate evolving risk, regulatory and governance challenges. Our professionals work with institutions to strengthen internal audit, risk advisory and related risk management programs that support sound decision-making, operational effectiveness and regulatory alignment.

Through services spanning internal audit, risk advisory, IT risk advisory, third-party risk management, fraud risk advisory and enterprise risk and compliance, we help financial institutions design and enhance resilient, risk-based programs aligned with their strategic objectives and operating environment.

To learn more, visit our Financial Services Industry Group page.