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Effective AI governance in banking starts with scope, accountability and consequences. A bank should inventory every model and AI-enabled use case, identify which decisions and customers it can affect, apply controls proportionate to risk, and continue testing and monitoring after deployment. The current U.S. federal reference is the interagency model-risk guidance issued by the Office of the Comptroller of the Currency (OCC), Federal Reserve and Federal Deposit Insurance Corporation on April 17, 2026. It supersedes SR 11-7 and the 2021 interagency model-risk statement for BSA/AML systems.
That guidance covers complex quantitative models, including non-generative and non-agentic artificial-intelligence models. It does not cover generative or agentic AI. Banks must therefore combine model-risk practices with their broader risk, compliance, security, privacy and operational-governance frameworks for those systems.
What the April 2026 bank model-risk guidance covers
The April 17, 2026 guidance is a risk-based supervisory framework, not a universal checklist. The agencies say controls should reflect a bank’s model-risk profile, size, operational complexity and extent of model use. The guidance is expected to be most relevant to organizations with more than $30 billion in total assets, but a smaller institution can still face substantial model risk when models are numerous, complex, embedded in important activities or used in nontraditional ways.
The OCC defines a covered model as “a complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates.” This can include machine-learning systems used for underwriting, valuation, risk measurement or other quantitative estimates.
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Simple arithmetic and deterministic, rule-based software without statistical, economic or financial theory are outside that definition. They may still create technology, cybersecurity, compliance or operational risk, but a bank should not label every automated tool a model for purposes of this guidance.
The agencies describe the guidance as non-prescriptive. OCC Bulletin 2026-13 states: “This guidance does not set forth enforceable standards or prescriptive requirements; accordingly, non-compliance with this guidance will not result in supervisory criticism against a banking organization.” That does not protect a bank from criticism or enforcement for breaking a law, using an unsafe or unsound process, misleading customers, or failing to control risks that result from inadequate management.
Does SR 11-7 still apply as the standalone standard?
No. The April 2026 interagency revision superseded SR 11-7 and the 2021 interagency statement on model risk management for BSA/AML systems. Banks should update policies, inventories, committee materials and training that still cite SR 11-7 as the current standalone reference. Historical documentation can retain the older documents for traceability, but current governance should be mapped to the revised guidance and the bank’s own risk framework.
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Does the guidance apply to generative or agentic AI?
No. Generative and agentic AI are explicitly outside the April 2026 model-risk guidance because their capabilities and risks are evolving. The guidance itself does not prescribe controls for a conversational assistant that generates text, a coding system that produces content, or an agent that can plan and take actions.
Those systems are not ungoverned. The Federal Reserve’s Vice Chair for Supervision Michelle W. Bowman said supervisors expect banks to use existing risk-management and governance frameworks while determining controls appropriate to each deployment. A bank should document why a system is in or out of the model inventory and then apply controls from the frameworks that address its actual risks.
Why AI model governance reduces operational and financial risk
Model risk is the possibility of adverse consequences when decisions rely on a model that is incorrect or misused. In banking, a defect can move through a process at machine speed and affect many accounts before a human notices.
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Potential pathways include:
- Bad inputs or assumptions: incomplete, stale, biased or poorly defined data can produce unreliable estimates.
- Performance deterioration: economic conditions, customer behavior or product mix can change after development.
- Misuse: employees may apply a model outside its approved population, purpose or operating limits.
- Weak implementation: a sound model can fail when code, interfaces, data pipelines or access controls do not match the approved design.
- Insufficient challenge: a vendor’s reputation or a model’s accuracy claim is not a substitute for evidence relevant to the bank’s use.
- Uncontrolled access: a broadly available tool can influence decisions that were never assessed during approval.
The OCC gives examples such as credit underwriting, valuation of exposures and positions, risk measurement, safeguarding client assets, and capital and reserve adequacy. Errors in these areas can create operational rework, customer harm, misstated financial or risk information, losses, liquidity or capital pressure, and regulatory consequences. These are plausible risk mechanisms, not a published statistic about losses caused by AI models; the official sources do not quantify such losses.
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Governance does not guarantee that a model is correct. It creates evidence, challenge and escalation so that defects, inappropriate use and deterioration are more likely to be found before they drive material outcomes.
How should banks govern AI models?
A practical program follows the model lifecycle while adding explicit treatment for customer impact, access and third parties.
1. Build one inventory and classify every use case
Start with a central inventory that covers internally developed models, purchased models, embedded vendor capabilities and employee-accessible AI tools. Record at least:
- the model or system name, version and owner;
- purpose, inputs, outputs and business process;
- the decisions it informs or makes;
- affected products, legal entities and customer populations;
- users, access channels and human review points;
- development, implementation and change history;
- vendor, subcontractor and data dependencies;
- approval status, limitations, validation evidence and monitoring results; and
- retirement date or replacement plan, when applicable.
Classify each item into at least three groups: covered quantitative or non-generative AI models; deterministic or rule-based automation; and generative or agentic AI. This prevents a bank from claiming that the 2026 guidance covers a system it expressly excludes, while ensuring excluded systems still receive appropriate controls.
2. Rate consequence and exposure
Use a documented tiering method rather than treating every model alike. Consider the decision’s financial and legal significance, the number and vulnerability of affected customers, model complexity, prevalence, degree of automation, reversibility of an error, data sensitivity and vendor dependence.
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Bowman identified three practical questions: “Will it be used for material tasks? Is it broadly accessible to employees or limited? And does its use directly affect consumers and customers, as with credit determinations?” Add those questions to intake and periodic review. A tool used by a small analytics team for internal drafting is different from one that sets credit terms, determines fraud holds or feeds regulatory capital calculations.
| Category | Typical examples | Governance treatment |
|---|---|---|
| Covered quantitative or non-generative AI model | Credit underwriting score, valuation model, loss estimate or machine-learning risk model | Apply lifecycle controls, independent or appropriately separated validation, implementation testing, monitoring, documented limitations and accountable approval. |
| Deterministic automation | Fixed threshold, arithmetic calculation or rule engine without statistical, economic or financial theory | Outside the model definition; govern through software, change, security, compliance and operational controls appropriate to its consequences. |
| Generative AI | System that creates text, code, images or other content from prompts | Outside the 2026 guidance; use broader controls for data protection, acceptable use, human review, hallucination risk, records and customer impact. |
| Agentic AI | System that plans tasks, calls tools or takes actions with limited intervention | Outside the 2026 guidance; add permissions, action limits, logging, testing of failure paths, approvals and rapid shutdown capability. |
3. Assign accountable ownership
Every material model needs a business owner who is responsible for purpose, permitted use, resources and outcomes. Model-risk, compliance, information security, privacy, technology and internal audit should have clearly defined challenge or assurance roles. A committee or senior executive should approve high-impact uses and accept residual risk; the board or a board committee should receive information that is meaningful for its oversight duties.
Policies should state who can approve a new use, who can change a model, who can waive a control, how exceptions expire, and who can suspend production. Segregation between development and validation should be proportionate to risk, but the person who built a model should not be the only person deciding that it works.
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Validation should test whether the model is suitable for its stated purpose, not merely whether it produces plausible outputs. Review the conceptual basis, assumptions, data lineage, feature definitions, sampling, bias and representativeness, code or configuration, performance measures, stability and limitations.
For a vendor or proprietary model, limited access to source code does not remove the bank’s responsibility. Obtain enough documentation, testing evidence, audit rights, service commitments and incident information to understand the model’s intended use and limitations. Where transparency is limited, narrow the use, add compensating controls or reject the deployment.
5. Test implementation and the surrounding process
A validated model can still fail in production because of an incorrect data transformation, a broken interface, a changed field definition, a permissions error or a manual workaround. Test the implemented version against the approved version, reconcile inputs and outputs, verify fallback procedures and confirm that users see the right warnings and limitations.
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For generative and agentic systems, test the entire workflow rather than only the model response. Include prompt or input manipulation, confidential-data leakage, unsafe instructions, fabricated content, tool-call failures, excessive permissions, duplicate actions and human override.
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Monitoring should cover both model behavior and the way people use it. Select indicators tied to the approved purpose, such as accuracy, error rates, drift, override rates, approval or decline patterns, exceptions, complaints, security events and downstream losses. Set thresholds, an owner, an escalation path and a documented response.
Reassess a model when data, economic conditions, products, regulations, vendor versions or user populations change. A material deterioration should trigger investigation, temporary limits, recalibration, additional review or suspension. Monitoring frequency should reflect exposure and change, not an arbitrary calendar rule.
7. Control changes and retirement
Version models, prompts, configurations, data pipelines and vendor releases. Require impact analysis and approval before material changes, and preserve records showing what was tested. A retirement process should revoke access, stop automated decisions, retain required records and communicate replacement or fallback arrangements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do community banks need annual model validation?
Not automatically. In OCC Bulletin 2025-26, Senior Deputy Comptroller and Chief National Bank Examiner James M. Gallagher wrote: “OCC’s guidance on model risk management does not, and should not be interpreted to, require community banks to perform annual model validation.” The appropriate frequency and scope depend on risk exposures, business activities, and the complexity and extent of model use.
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How can a bank monitor vendor AI models?
Vendor procurement is the beginning of oversight, not the end. Before approval, document the vendor’s intended use, training or data dependencies where available, performance evidence, known limitations, update process, subcontractors, hosting location, security controls and incident-notification obligations.
Contract and oversight terms should address:
- the bank’s rights to documentation, testing and audit evidence;
- notification and approval of material model or version changes;
- data ownership, retention, deletion and permitted secondary use;
- service availability, business continuity and exit assistance;
- access controls, logging and investigation support;
- customer-impact incidents and regulatory cooperation; and
- independent assurance or testing that can be reviewed by the bank.
After deployment, compare observed performance with the evidence supplied at onboarding, track vendor releases, review incidents and confirm that the actual use has not expanded beyond the approved purpose. If the vendor cannot provide enough information for the risk tier, the bank should reduce exposure or use compensating controls rather than treating opacity as an exception with no owner.
What should senior management and the board see?
Reporting should connect technical indicators to decisions and risk appetite. A useful dashboard identifies the number of active models by risk tier, new and retired uses, overdue validation or remediation, material performance changes, customer complaints, overrides, incidents, vendor concentration and open exceptions. It should show whether any system directly affects consumers or material financial measures.
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Escalation criteria should be explicit. Examples include statistically or operationally significant deterioration, unexplained demographic disparities, unauthorized use, a material vendor change, a security or privacy event, loss of required data quality, or an inability to execute a human fallback. Reports should state the owner, deadline, interim control and decision authority, not just a red status.
A practical implementation sequence
- Map the population: collect model, automation, generative-AI and agentic-AI use cases from business, technology, procurement and risk teams.
- Define scope: document which items meet the 2026 model definition and which require other governance frameworks.
- Tier consequences: score materiality, customer impact, complexity, access, prevalence, reversibility and vendor dependence.
- Close ownership gaps: name business owners, validators, approvers, monitoring owners and escalation authorities.
- Baseline evidence: gather purpose statements, data lineage, assumptions, testing, limitations, implementation records and contracts.
- Set monitoring: select indicators and thresholds, assign review frequency based on risk, and rehearse response to deterioration.
- Remediate highest exposure first: prioritize credit, capital, reserves, valuation, fraud, payments and customer-facing uses.
- Report and refresh: give management and the board decision-relevant metrics, and reassess the inventory when products, vendors, conditions or regulations change.
What the guidance means for a bank’s risk posture
The central question is not whether a system is marketed as AI. It is whether the bank understands what the system does, has evidence that it is appropriate for the stated purpose, controls who can use it, detects when it stops working as expected and can intervene before an error becomes a material customer or financial outcome.
For covered quantitative and non-generative AI models, the April 2026 guidance supplies the current federal model-risk reference. For generative and agentic AI, the same discipline—clear purpose, accountable ownership, proportionate testing, access control, monitoring and escalation—must be assembled through the bank’s broader governance framework because those systems are outside the guidance’s scope.
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