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The current U.S. federal reference is the interagency Supervisory Guidance on Model Risk Management, issued April 17, 2026 by the Federal Reserve, Office of the Comptroller of the Currency and Federal Deposit Insurance Corporation. It supersedes Federal Reserve SR 11-7 and the 2021 interagency statement on model risk management for Bank Secrecy Act/anti-money-laundering systems. The guidance is risk-based and expressly excludes generative and agentic AI models.
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What model risk means in a bank
A banking model is a complex quantitative method, system or approach that applies statistical, economic or financial theories to input data to produce quantitative estimates. The 2026 interagency guidance does not treat every calculation as a model: simple arithmetic and deterministic rule-based processes without those theoretical underpinnings are outside its definition.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchModel risk is the possibility of an adverse consequence when a bank relies on such a model. Consequences can include financial losses, incorrect regulatory or management reporting, poor credit or market-risk decisions, ineffective fraud or anti-money-laundering controls, and decisions that are unreliable because a model no longer reflects the data or situation in which it is used.
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For an AI system, the risk may arise at several points:
- Inputs: data can be incomplete, biased, stale, incorrectly labeled or outside the conditions represented in development data.
- Assumptions and method: a model may simplify relationships that do not hold during stress or for a new customer segment.
- Implementation: production code, data pipelines, thresholds or interfaces can differ from the tested design.
- Output and use: staff may interpret a score incorrectly, apply it outside its approved purpose or treat an estimate as a certainty.
- Change over time: economic conditions, portfolio composition or customer behavior can cause performance to deteriorate.
Risk management therefore asks not only whether an algorithm is accurate in a test, but whether the bank understands where it can fail, limits its use, detects deterioration and can correct or withdraw it.
The interagency guidance describes a bank’s responsibility this way: “Each banking organization ultimately is responsible for adopting model risk management practices that are appropriate and effective for managing the specific risks the banking organization faces or is likely to face.” (Federal Reserve, April 17, 2026.)
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What the April 2026 guidance covers—and what it does not
The guidance is supervisory guidance, not a regulation that creates enforceable standards. The agencies say that failure to follow guidance alone will not result in supervisory criticism. However, a bank can face supervisory action for violating law or engaging in unsafe or unsound practices that stem from insufficient model risk management. The guidance is expected to be most relevant to banking organizations with more than $30 billion in assets, while smaller institutions may still need comparable controls when their model use or activities create significant risk. (Federal Reserve SR 26-2.)
| Question | Answer under the 2026 framework |
|---|---|
| Is it a prescriptive rule? | No. It sets supervisory expectations and calls for practices proportionate to each organization’s risks, size and complexity. |
| Which banks are most directly in scope? | Organizations above $30 billion in assets are the stated focus, but smaller banks are not automatically exempt when their exposures are significant. |
| Which models are covered? | Traditional statistical and quantitative models and non-generative, non-agentic AI models that fit the guidance’s definition. |
| Are generative and agentic AI models covered? | No. The guidance says they are “not within the scope of this guidance.” |
| Who decides the appropriate controls? | Each banking organization, using a risk-based assessment of its models and operating context. |
Federal Reserve Vice Chair for Supervision Michelle W. Bowman reiterated on May 1, 2026 that the revised guidance does not apply to generative or agentic AI. She said broader risk-management and governance practices should support adoption of those technologies. (Bowman’s May 1, 2026 speech.)
How a bank determines a model’s risk and materiality
The guidance does not assign every model the same testing package. It considers three connected ideas: inherent risk, exposure and purpose. Those factors inform materiality, and a model with greater materiality warrants more comprehensive oversight.
Inherent risk
Inherent risk comes from the model itself and the conditions around it. Relevant questions include:
- How complex are the assumptions, mathematics, code and dependencies?
- How reliable, relevant and complete are the inputs?
- Are the data constraints understood, including missing values, changing populations or limited historical observations?
- Could small changes in assumptions or inputs materially change the result?
Exposure and business impact
Exposure concerns how much the bank relies on the output. A model influencing a large loan portfolio, capital calculation, liquidity decision, customer eligibility or regulatory report has more consequential exposure than an internal tool used for a low-impact analysis. Portfolio size and the potential business impact are part of this assessment.
Purpose and materiality
Purpose asks what the model is used to do. Models supporting regulatory requirements or financial-risk management are generally higher risk than models used for less consequential purposes. A model can become more material when a bank expands it from an analytical aid into an automated decision process.
Individual and aggregate risk
Assessment must work at two levels. Individually, the bank evaluates each model. In aggregate, it considers interactions and dependencies: several models may rely on the same data source, assumption, vendor methodology or economic forecast. A weakness shared across that dependency can affect many decisions at once even if each model appears acceptable in isolation.
Effective challenge
Effective challenge is critical analysis by people with appropriate expertise, enough independence to remain objective and sufficient standing to influence a change. It is not a ceremonial sign-off. Challengers should be able to question the purpose, data, assumptions, testing evidence, limitations, monitoring results and proposed remediation.
The model-risk lifecycle, step by step
1. Define the purpose and intended use
Start with a written statement of what the model does, who uses it, which decisions it supports, the population and time period it addresses, and what it must not be used for. Development choices should align with that business purpose and the bank’s policy. A clear purpose determines which data, methodology and tests are relevant.
2. Design, develop and document the method
Development is a risk decision, not only a coding exercise. Document the theory, assumptions, data lineage, variable construction, exclusions, algorithms, thresholds, material changes and known limitations. Explain why the selected approach is suitable for the intended use and what conditions could make it unreliable.
3. Test in proportion to risk
Testing should match complexity and materiality. Depending on the model, evidence can include out-of-sample testing, out-of-time testing, comparison of alternative assumptions or methods, sensitivity analysis, and assessment of data quality and relevance. Testing should expose how the model behaves under conditions different from those used to fit it.
4. Validate before first use
Validation assesses whether the model performs as expected, how reliable it is and where its limitations lie. It generally occurs before first use. If urgent circumstances require temporary use before validation is complete, the bank should give greater attention to limitations, communicate them to stakeholders and apply compensating controls. The guidance emphasizes that validation quality depends on the rigor and effectiveness of the review, not on a particular organizational chart.
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Users need practical information about appropriate interpretation, confidence limits, prohibited uses, overrides and escalation points. Extending a model beyond its approved purpose adds uncertainty. The bank should analyze the new use and review whether data, testing, validation, approvals and controls remain adequate before making that extension.
6. Monitor performance and outcomes
Monitoring continues after deployment. Track inputs, outputs, errors, exceptions, overrides, stability and relevant outcomes. Compare observed results with expectations and look for persistent deviations or deterioration. Monitoring should be capable of identifying a change in population, environment or data quality before the model’s output causes widespread harm.
7. Adjust, recalibrate or redevelop
When monitoring shows deterioration, the response may be adjustment, recalibration, redevelopment, tighter use restrictions or retirement. The choice should address the cause rather than merely treating a symptom. Changes require documented impact analysis and, where appropriate, renewed validation and approval.
8. Govern, inventory and document
Policies should assign accountability across development, validation, approval, monitoring, issue management and retirement. A model inventory should contain enough information to understand risks both individually and in aggregate. Useful fields include owner, purpose, users, materiality tier, data sources, dependencies, validation status, limitations, approved changes, monitoring results, open findings, exceptions and retirement date. Documentation supports continuity when staff change, tracks recommendations and exceptions, and demonstrates how issues were remediated.
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The guidance leaves the exact control set to each bank. A practical implementation is to tier models using the factors the agencies identify, then map each tier to evidence and review intensity rather than imposing identical requirements everywhere.
| Assessment factor | Lower-risk indication | Higher-risk indication | Control implication |
|---|---|---|---|
| Complexity and assumptions | Limited variables, transparent method and few dependencies | Complex methodology, opaque dependencies or sensitive assumptions | Increase technical review, alternative-method testing and documentation. |
| Input data | Stable, well-understood and quality-controlled data | Novel, sparse, rapidly changing or externally sourced data | Increase lineage checks, quality tests and drift monitoring. |
| Exposure | Small population or advisory use | Large portfolio or direct effect on customers, capital or reporting | Increase approval authority, monitoring frequency and contingency planning. |
| Purpose | Exploratory or low-consequence analysis | Regulatory, credit, market, liquidity, fraud or other financial-risk decision | Require stronger validation, effective challenge and documented limits. |
| Institutional complexity | Few models and simple operations | Many interacting models, shared vendors or multiple business lines | Strengthen inventory, dependency mapping and aggregate-risk review. |
This is an implementation framework, not a new regulatory checklist. The bank should record why a model received its tier and revisit that decision when its purpose, data, exposure or performance changes.
Generative and agentic AI need a separate control path
It is incorrect to describe the April 2026 guidance as directly regulating large language models, generative assistants or agentic systems. The document expressly excludes generative and agentic AI because those technologies are novel and rapidly evolving. Non-generative AI models that fit the guidance’s definition remain within scope.
Exclusion does not mean no oversight. For a generative or agentic system, the bank should use its broader risk-management, information-security, third-party, privacy, operational-resilience and governance practices to decide controls. Those controls may address authorization, human review, data handling, confidentiality, logging, change management, incident response and limits on autonomous actions. The exact design depends on the system’s use and risk; the 2026 model-risk guidance itself does not prescribe it.
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Vendor and third-party models
A proprietary product can restrict access to source code, training data or methodology, but that does not transfer responsibility away from the bank. Before and during use, the bank should seek enough information to understand the vendor model’s conceptual soundness, design, development data and performance.
- Define the approved purpose, users, inputs, outputs and materiality.
- Review available development, validation and performance evidence, including known limitations.
- Assess how vendor updates, data changes and service interruptions could affect results.
- Monitor outcomes and performance rather than relying solely on the vendor’s assurances.
- Document all bank customizations, overrides, thresholds and integrations, and evaluate their effect.
- Record information gaps, compensating controls, escalation routes and exit or contingency options.
Vendor opacity is a reason to increase understanding and controls, not a reason to omit a model from the inventory.
Common failures and practical fixes
Using a model outside its approved purpose
Failure: A score developed for one portfolio or decision is silently reused for another. Fix: require a new-use assessment covering population, data, assumptions, performance, approvals and controls before deployment.
Treating validation as a one-time certificate
Failure: A model passes pre-launch testing but no one checks whether outcomes remain reliable. Fix: connect validation to ongoing monitoring, outcome analysis and defined triggers for recalibration or redevelopment.
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Overlooking shared dependencies
Failure: Every model is reviewed separately while several depend on the same data feed or assumption. Fix: map dependencies in the inventory and conduct aggregate-risk reviews.
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Weak independent challenge
Failure: Reviewers lack expertise, independence or authority to change a favored model. Fix: assign objective challengers with the standing to require additional evidence, restrictions or remediation.
Ignoring data limitations
Failure: A high score is treated as reliable despite missing, stale or unrepresentative data. Fix: document data constraints, test relevance and completeness, monitor drift and communicate limitations to users.
Letting documentation lag behind production
Failure: The inventory and model document describe an old version while code, thresholds or vendors have changed. Fix: make change control, versioning, approvals and post-change review part of deployment.
Relying on vendor claims without outcome evidence
Failure: A bank accepts a black-box product because the vendor supplied a brochure or generic validation report. Fix: obtain evidence proportionate to the bank’s exposure, test the configured product and monitor results in the bank’s own environment.
What effective AI model risk management looks like
An effective program gives decision-makers a current view of every material model, its purpose, limitations, dependencies, validation status and observed performance. It makes responsibility explicit, gives independent experts a real ability to challenge design and use, and scales testing and monitoring to risk rather than to a one-size-fits-all template.
For U.S. banks, the April 17, 2026 interagency guidance is the central reference for traditional and non-generative AI model risk. It replaces SR 11-7 and the 2021 BSA/AML model-risk statement, but it does not turn generative or agentic AI into ordinary in-scope models. The durable principle is proportional accountability: understand what a model is intended to do, test whether it can do that reliably, control how it is used, monitor what happens in production and act when evidence changes.
Frequently Asked Questions
Does the 2026 U.S. model-risk guidance ban generative AI in banks?
No. It excludes generative and agentic AI from its scope. Banks must instead use broader risk-management and governance practices appropriate to each system’s use and risk.
Are banks below $30 billion exempt from model-risk management?
No. The guidance is expected to be most relevant above $30 billion in assets, but smaller institutions may face significant model risk and should apply proportionate controls.
Is model validation the same as model risk management?
No. Validation is one lifecycle component. A complete program also covers purpose, development, intended use, monitoring, governance, inventory, documentation, effective challenge and vendor oversight.
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