How Regulators Are Framing Artificial Intelligence in Banking
According to the American Bankers Association‘s coverage of federal policy debates, banks already operate under an extensive compliance framework that reaches most AI‑related risks, including fair lending, cybersecurity and model risk management, rather than existing in a “regulation‑free” zone for AI. As reported in the ABA Banking Journal‘s coverage of AI policy and regulatory reform, federal agencies are signaling that existing model‑risk guidance and consumer-protection rules apply to AI models just as they apply to traditional scorecards and decision engines, even as industry groups push for clearer, tailored guidance around AI use in financial services.
This regulatory posture creates tension inside institutions. Business leaders see AI as a lever to reduce costs, personalize service and sharpen fraud and credit detection, while risk and compliance teams are being asked to approve systems that can learn and adapt in ways legacy models never did.
Policy research from organizations like the Brookings Institution, which has examined bias and governance in AI‑based financial services, reinforces that supervisory discussions now focus as much on outcomes (fairness, explainability and control over data use) as on raw model performance.
According to ABA summaries of recent comment letters and frameworks for AI legislation, industry groups are pressing regulators to modernize model‑risk guidance through formal notice‑and‑comment processes, arguing that exam expectations for AI should be clarified without stifling responsible experimentation. This debate reinforces a central reality for banks: AI programs will scale only to the extent they line up with evolving regulatory doctrine rather than just an internal appetite for innovation.
Explainability as a Core Requirement for Bank AI Governance
As reported by Bank Director, supervisors and bank boards alike increasingly view explainability as a threshold requirement for AI rather than an optional enhancement. Directors are urged to probe whether management can clearly describe where AI is being used, what data it depends on, and how its outputs map back to the institution’s risk appetite and compliance obligations.
Bank Director further highlights that explainability is a governance obligation, not just a technical feature. Boards are encouraged to demand:
- Thorough documentation of AI use cases
- Clearly defined escalation paths when issues arise
- Regular reporting on bias, model drift and operational incidents
By contrast, research and policy commentary from organizations that include Brookings (cited above) warn that “black box” deployments, in which internal stakeholders cannot articulate how a system reaches decisions or how it is monitored, raise serious supervisory concerns.
Practitioner analyses of model‑risk practices from compliance consulting firms like Treliant (New York, N.Y.) note that some banks have slowed or shelved promising AI initiatives not because the technology failed, but because governance, documentation and testing practices lagged the sophistication of the models.
How Model Risk Management Is Adapting to Machine Learning
According to ABA reporting on federal policy and risk‑management expectations, regulators and industry groups increasingly frame AI as part of the existing model‑risk ecosystem rather than as a separate category, even as they acknowledge that AI’s complexity may require updated guidance. ABA and allied banking associations have explicitly called for traditional model‑risk frameworks to be adapted to handle AI while preserving core expectations for validation, performance monitoring and controls.
Industry commentary from risk‑consulting firms and vendors shows how that shift is playing out on the ground. Drawing again on Treliant’s analysis, banks are expanding what they classify as a “model,” pulling AI‑driven decision engines (for credit underwriting, fraud detection and even marketing) into formal model inventories with designated owners, documentation standards and testing requirements. Vendor releases, including Experian‘s announcement of its AI‑powered assistant for model‑risk management, describe tools aimed at centralizing model documentation, tracking approvals and continuously monitoring AI behavior against supervisory benchmarks such as the Federal Reserve’s SR 11‑7 framework.
Policy research from Brookings and others also points out that regulators are paying particular attention to fairness and bias, especially in AI‑based lending. Brookings’ analysis of bias in AI‑driven financial services, along with related coverage in trade and policy outlets, stresses that AI can either mitigate or exacerbate discrimination depending on training data and objectives, and notes that supervisors increasingly expect not only robust initial validation but ongoing testing to ensure models remain accurate, fair and aligned with their intended purpose.
Managing Third-Party AI Risk in Banking
According to interagency guidance issued jointly by the Federal Reserve, the FDIC and the OCC, engaging third‑party service providers does not diminish a bank’s responsibility to manage risk or comply with legal and regulatory requirements. The 2023 “Interagency Guidance on Third‑Party Relationships: Risk Management” makes this point explicitly, underscoring that banks remain fully accountable for activities conducted through vendors, including those involving sophisticated technology and data‑driven services.
State regulators are amplifying this message with AI‑relevant expectations. As outlined in the October 21, 2025, industry letter from the New York Department of Financial Services, supervised entities must manage cybersecurity and operational risks across the full lifecycle of third‑party service providers, including vendors that use advanced technologies such as AI in ways that affect customers or core operations. Legal and compliance analyses of the NYDFS letter, such as those published by law firms and compliance advisors, note that the agency expects banks to maintain strong controls over vendor AI models, including transparency into model behavior, limits on data use and well‑planned exit strategies that address data portability and ongoing access.
Commentary from law firms and third‑party‑risk specialists also indicates that this evolving guidance is changing how banks structure vendor relationships. These sources, including the FDIC’s discussion accompanying the interagency guidance, describe institutions revisiting contracts to secure audit rights for AI systems, constrain downstream data use and obtain detailed documentation on how models are trained, updated and governed, while also pointing to the relevance of that guidance for any AI‑enabled outsourcing.
What Bank Boards and Executives Need to Know About AI Regulation
As Bank Director and other governance‑focused outlets have stressed, AI strategy and regulatory strategy are now inseparable at the board level. Best‑practice advice urges directors to put AI on regular agendas, clarify which committees own oversight, and ensure that governance frameworks for AI address data quality, compliance, operational resilience and reputational risk. Industry reporting on AI‑governance best practices suggests that firms that invest early in enterprise‑level AI governance—spanning inventory, documentation, monitoring and escalation—are better positioned to scale high‑value use cases.
Travillian Group’s coverage of technology and innovation in banking reinforces this leadership angle. Articles such as “Can AI Fix Banking Compliance? Founder Kalyani Thinks So” and “How AI Is Reshaping Financial Product Discovery: A Follow‑Up with Fintel Connect” underscore that emerging leaders will need AI literacy that extends well beyond technical build skills. These pieces highlight growing demand for roles that bridge AI and governance, such as AI‑governance and ethics specialists or model‑risk leaders who can connect data science, compliance and supervisory expectations across community and regional banks.
Taken together, this mix of regulatory guidance, industry reporting and expert commentary points to a common conclusion: in the current environment, the most valuable AI for banks is often not the most complex, but the most defensible. Well‑documented, explainable systems that fit cleanly within existing risk frameworks — and that executives can confidently discuss with examiners, customers and boards — are the ones most likely to move beyond pilots and create durable advantage.
