The Tool Desk
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The April 17, 2026 interagency model risk guidance from the Federal Reserve Board, Office of the Comptroller of the Currency (OCC) and Federal Deposit Insurance Corporation (FDIC) provides a risk-based framework for model development and use, validation and monitoring, governance, controls, and vendor products. It is supervisory guidance rather than an enforceable, prescriptive checklist. The framework is most relevant to banking organizations with more than $30 billion in assets, but smaller institutions with significant model-risk exposure may also find it relevant.
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What an independent AI review is—and what it is not
An independent review is a structured challenge of an AI-enabled system and the controls around it. Reviewers examine the system’s purpose, conceptual design, data, assumptions, implementation, performance, limitations, governance and ongoing monitoring. They should be able to require changes, restrict use or escalate unresolved risk.
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The 2026 guidance defines a model as a complex quantitative method, system or approach that applies statistical, economic or financial theories to input data to produce quantitative estimates. Simple arithmetic and deterministic rule-based software without those theories are outside that definition. An AI-enabled workflow therefore needs a documented classification decision rather than an automatic label. If it is not a model under that definition, the institution still needs to identify the risk framework that governs it.
The 2026 regulatory boundary
The revised interagency model risk guidance was issued on April 17, 2026. It addresses model development and use, validation and monitoring, governance and controls, and vendor and other third-party products. The agencies identify organizations with more than $30 billion in assets as the primary audience, while noting that the guidance may also be relevant to smaller institutions with substantial model-risk exposure.
The guidance is supervisory: it describes practices examiners may expect to see, but it is not an enforceable rule that prescribes one process for every institution. Applicable obligations still depend on the institution, its activities, jurisdiction and the way a system is used.
Generative and agentic AI models are expressly excluded from the 2026 model-risk guidance. The agencies nevertheless expect organizations to use their existing risk-management and governance practices to determine suitable controls for tools, processes and systems outside the guidance. The OCC has said the agencies plan a future request for information about model risk management, including generative, agentic and AI-based models; that planned action is not a current requirement.
The seven-part review checklist
| Review stage | Questions the reviewer must answer | Core evidence |
|---|---|---|
| 1. Define the system | What is it, who owns it, what decisions does it influence, and where are the boundaries? | Inventory record, architecture, version history, use-case statement and dependency map |
| 2. Rate risk | How material is the system individually and in combination with related systems? | Risk assessment, exposure analysis, impact rating and rationale for review depth |
| 3. Examine design and data | Are the methodology, assumptions, inputs and development tests appropriate? | Design documentation, data lineage, assumptions register and development-test results |
| 4. Test outcomes | Does the system perform as intended, and can deterioration or misuse be detected? | Validation tests, outcome analysis, thresholds, drift reports and limitation log |
| 5. Challenge governance | Are roles, approvals, independence and escalation strong enough to change decisions? | Policies, charters, approvals, conflict disclosures and meeting records |
| 6. Review third parties | Can the institution understand and control a provider’s service, dependencies and exit? | Due-diligence file, contract, independent reports, monitoring results and exit plan |
| 7. Report and remediate | Are findings assigned, time-bound, escalated and closed with evidence? | Finding register, action plans, interim controls, closure tests and management reports |
1. Define the system, purpose and boundary
Build an authoritative system record
Start with an inventory record that names the system, version, owner, provider, deployment date and material changes since the previous review. Include the production environment, interfaces, data stores, prompts or configuration files where relevant, and connected models or decision engines. A system that appears to be one product may depend on several scoring models, retrieval services, identity components or vendor APIs; the review boundary must show those dependencies.
Describe intended and prohibited use
Write a plain-language purpose statement. Identify the users, affected customers or employees, decisions and operational processes influenced, and the point at which a human can accept, override or reject an output. State prohibited uses and out-of-scope populations. For example, a tool approved to prioritize manual case review should not silently become an automated approval or denial engine.
Document deployment location, jurisdictions, data residency constraints and retention settings. Record whether outputs are advisory, used as one input among many, or determinative. The same technology can create very different risk depending on that role.
Explain classification and dependencies
State whether the system meets the 2026 guidance’s model definition. If it does not, identify the alternative framework—such as technology, privacy, information-security, conduct or operational-risk governance—and explain why. Capture upstream data providers, downstream decisions, shared assumptions and common infrastructure that could create correlated failure across several systems.
Set the reviewer’s mandate
The review plan should name the reviewer, qualifications, reporting line, scope, period covered and conflicts of interest. Effective challenge requires objective experts with enough technical and business knowledge, independence to remain impartial, and organizational standing to influence a decision. A reviewer who can only recommend changes but cannot reach an approval authority has limited practical independence.
2. Rate risk and set the review depth
Risk rating determines how much testing, documentation and monitoring are justified. Do not assign the same review package to every system. Explain why the chosen depth and frequency are proportionate to the system’s risk, purpose and institutional complexity.
Assess inherent risk
- Complexity: Consider architecture, model type, interactions, explainability limits and the number of components that must work together.
- Assumptions: Identify economic, behavioral, financial or operational assumptions and how sensitive outputs are to each one.
- Data: Evaluate quality, representativeness, coverage, freshness, provenance, missingness and known limitations.
- Purpose and impact: Rate the consequences of an incorrect, delayed, biased or unavailable output for customers, capital, liquidity, compliance, safety and operations.
- Exposure: Measure the number, value and duration of decisions or transactions influenced, including indirect exposure through staff workflows.
- Change velocity: Consider how often code, prompts, model weights, data sources, thresholds or vendors change.
Consider materiality in aggregate
Review systems together where they share data, assumptions, methods, vendors or infrastructure. Several individually modest tools can produce a material concentration if they fail under the same economic conditions or depend on one unavailable service. The assessment should identify correlated failure and explain whether controls operate at portfolio level as well as system level.
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Choose review intensity
High-materiality systems generally warrant broader conceptual review, more independent testing, tighter approval gates, more frequent monitoring and faster escalation. Lower-risk systems may use a narrower test plan, but the rationale must be recorded. A risk rating should be revisited after a major change, a new use case, a control failure or a material shift in exposure.
3. Examine design, data and development evidence
Test the conceptual design
Read the purpose statement and methodology as if you had to build the system yourself. Determine whether the chosen approach can answer the stated question, whether assumptions are plausible, and whether qualitative judgments are documented. Ask what the system cannot infer and which conditions invalidate its output.
Trace data from source to output
Map each material input to its source, transformation, storage location and point of use. Evaluate selection rules, missing-value treatment, labeling, sampling, class balance, representativeness and the age of the data. Check whether test data reflect the intended population, products, channels and jurisdictions. A high headline accuracy can conceal poor performance for an under-represented segment or a newly introduced product.
Review development testing
Inspect out-of-sample or out-of-time tests when they are appropriate to the system. Compare alternative assumptions, methods or feature sets. Confirm that test conditions are not accidentally reusing training information. Review critical inputs and data-quality checks, and require an explanation for material exclusions or overrides.
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Check for use beyond the original application
Compare the approved use case with actual users, interfaces, thresholds and decisions. If a system has been extended to another product, customer group, geography or risk decision, require additional analysis. The original validation may not cover a changed population, a different loss function or a new human workflow.
4. Test performance, outcomes and limitations
Connect outputs to real-world outcomes
Evaluate whether the system performs as expected for its intended purpose, not merely whether it reproduces a development metric. Compare outputs with relevant observed outcomes and established performance thresholds. Select measures that fit the use: calibration, error rates, ranking quality, stability, false-positive and false-negative consequences, or operational service levels.
Use validation and monitoring evidence
Review conceptual-soundness validation, outcome analysis, back-testing or outlier analysis where applicable, and independent monitoring results. Ask whether the tests cover stressed conditions, seasonal effects, product changes and data interruptions. The reviewer should be able to reproduce material calculations or obtain sufficient evidence from a provider to evaluate them.
Identify deterioration and limits
Record errors, drift, unstable segments, known blind spots and conditions that require restriction, recalibration, redevelopment or retirement. Define trigger levels, who receives an alert, how quickly the issue must be investigated and what temporary limits apply. A limitation that is known but not tied to a control is not being managed.
Make validation timing explicit
Validation normally precedes first use. If urgent use begins before full validation, document the reason, unresolved limitations, interim controls, approval authority, expiry date and plan for completing validation. Approval should not erase the distinction between tested performance and an emergency exception.
Verify user understanding
Users need instructions that explain what an output means, what it does not mean, when to override it and how to report an anomaly. Training and interface design should prevent staff from treating a probabilistic recommendation as a guaranteed answer. Monitoring and corrective-action processes must continue after approval.
5. Review governance, accountability and independence
Map responsibilities across the lifecycle
Policies should assign accountability for development, implementation, use, validation, monitoring, incident response, remediation and retirement. Approval authorities must be clear, including who can accept residual risk and who can suspend a system. Maintain a current responsibility matrix rather than relying on informal knowledge.
Protect effective challenge
Reviewers should not be evaluating work for which they were responsible, rewarded or pressured to approve. Examine reporting lines, workload, access to information and the ability to escalate. The interagency guidance describes effective challenge as work performed by people with “the appropriate expertise to conduct a critical and objective challenge, sufficient independence to maintain objectivity, as well as the organizational standing and influence to effect any change.”
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Separate assurance from management activity
When internal audit is involved, keep its assurance role distinct from management’s development or validation work. Audit may assess whether the framework operates effectively; it should not become the owner of a model, write management’s validation, or approve a risk that management must accept.
Control external review work
External specialists can provide expertise, but management remains accountable. Define delegated responsibilities, required qualifications, access rights, quality review, deliverables and escalation routes. Integrate external findings into the institution’s broader risk register and track them to closure.
6. Add third-party and vendor checks
Outsourcing a model, data feed, hosting service or review activity does not outsource institutional responsibility. The interagency third-party guidance states that a banking organization’s use of third parties “does not diminish its responsibility to meet these requirements to the same extent as if its activities were performed by the banking organization in-house.”
Perform risk-proportionate due diligence
Match due diligence to the relationship’s risk and complexity. Examine, as applicable:
- Legal and regulatory compliance history
- Financial condition and business continuity capability
- Relevant experience, qualifications and key personnel
- Risk-management, governance and control structure
- Information-security architecture, access controls and vulnerability management
- Systems, data handling, retention and geographic locations
- Resilience, disaster recovery and recovery testing
- Subcontractors, fourth parties and dependencies
Obtain independent audit or testing reports where available, then verify that their scope, period, exceptions and testing methods actually match the service and intended use. A generic certification cannot substitute for evidence about the specific control or data flow that matters to the institution.
Write enforceable contract terms
Contracts should cover service levels, security obligations, incident notification, audit and information rights, data ownership and return, subcontractor approval, model or system changes, records retention, regulatory access, business continuity and termination assistance. Specify what happens when a provider changes a model, data source, hosting location or material control.
Monitor the relationship in production
Track service performance, control effectiveness, security incidents, vulnerabilities, customer complaints, continuity exercises, subcontractor changes and material provider events. Compare actual performance with contract thresholds and the system’s approved risk appetite. Escalate repeated breaches rather than treating each incident as an isolated ticket.
Plan a controlled exit
Maintain a practical transition plan covering replacement providers, internal fallback procedures, data return or destruction, credential and access removal, records preservation, customer communications and testing. Consider how long a transition will take and whether customers could be harmed by an abrupt shutdown. Exit planning is part of resilience, not paperwork reserved for contract termination.
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Use a finding record that supports decisions
Every finding should contain:
- The evidence and test procedure supporting the conclusion
- The risk significance and affected uses, customers or processes
- The accountable owner and approving authority
- Corrective action, measurable acceptance criteria and due date
- Interim restrictions or compensating controls
- Escalation path and consequences of missing the date
- Closure evidence and independent verification
Describe the decision effect explicitly. A finding may require reduced limits, additional human review, suspension of a feature, tighter monitoring, recalibration, redevelopment or retirement. Do not close an issue merely because a policy was updated; verify that the control operates and addresses the underlying risk.
Escalate in proportion to severity
Periodic reports should show open findings, overdue actions, exceptions, incidents, risk trends and changes in exposure. Route material issues to the committee, senior management or board level specified by the governance framework. Management should understand residual risk, not just the number of tests passed.
How to compare several AI systems or providers
When selecting review depth or comparing alternatives, use the same decision axes for each system:
| Comparison axis | Questions to ask |
|---|---|
| Intended use and customer impact | What decision is influenced, who can be harmed, and how reversible is an error? |
| Materiality and exposure | How many decisions and what value are affected, individually and in aggregate? |
| Complexity and data limits | Can the institution explain the method, assumptions, inputs and known gaps? |
| Validation evidence | Are tests independent, current, representative and sufficient for the use case? |
| Human oversight | Can a trained user understand, override and escalate an output? |
| Provider transparency | Will the provider supply documentation, testing evidence, audit access and change notices? |
| Security and resilience | How are access, incidents, recovery, subcontractors and geographic dependencies controlled? |
| Monitoring and remediation | Are thresholds, alerts, owners and corrective actions defined? |
| Transition and exit | Can data, records and operations move to another solution without unacceptable disruption? |
Use documented evidence rather than vendor marketing claims. A system with slightly better benchmark performance may be the riskier choice if its data lineage, change controls or exit options are opaque.
How to handle generative and agentic AI outside the 2026 model guidance
Do not present the 2026 model-risk guidance as a direct prescriptive checklist for generative or agentic systems; it excludes them. Instead, classify the use, identify applicable technology, privacy, security, conduct, operational and third-party controls, and document why those controls fit the risk.
For a generative or agentic deployment, the review should still establish the system boundary, permitted tasks, data access, human approval points, logging, tool permissions, change process, failure handling, provider dependencies and customer impact. Treat the classification and governance rationale as a first-class decision. The agencies’ planned future request for information does not itself impose a present obligation, so avoid claiming that it does.
Common review failures and practical fixes
- Accuracy-only testing: Add conceptual review, data limitations, outcome analysis, drift monitoring and use restrictions.
- Rubber-stamp independence: Give reviewers direct escalation rights, protected reporting lines and authority to delay approval.
- Unclear scope: Inventory versions, interfaces, connected models, prompts, data feeds and actual uses before testing.
- Generic vendor evidence: Match audit reports and certifications to the service, period, locations and controls actually relied upon.
- Approval without monitoring: Define thresholds, alert owners, review frequency and corrective-action deadlines before launch.
- Findings without consequences: Link each issue to permissible use, limits, compensating controls or suspension criteria.
- No exit plan: Test data retrieval, replacement procedures, access removal and customer-impact communications before they are needed.
- Confusing guidance with law: State which obligations apply to the institution and which practices are supervisory expectations or internal policy.
A practical review sequence
- Open the review: Confirm scope, system version, intended use, reviewer competence, independence and reporting route.
- Classify and rate: Decide whether the system meets the model definition, identify the governing framework and assign inherent and aggregate risk.
- Collect evidence: Obtain design, data, development, implementation, monitoring, governance and vendor records.
- Challenge the design: Test assumptions, data relevance, alternative methods, out-of-sample behavior and use beyond the approved purpose.
- Test outcomes: Compare outputs with real-world results, thresholds, stress conditions and known limitations.
- Evaluate controls: Confirm approvals, segregation, user training, monitoring, incident response, vendor oversight and continuity.
- Issue findings: Record evidence, significance, owner, due date, interim limits and escalation requirements.
- Decide and follow up: Approve, restrict, remediate, recalibrate, redevelop or retire the system; verify closure independently.
- Revisit on change: Trigger a new or expanded review after material changes, incidents, new uses, provider changes or significant exposure shifts.
What a complete evidence file contains
A defensible file lets a knowledgeable person reconstruct the review and the decision. It normally includes the system inventory record; purpose and prohibited-use statement; architecture and dependency map; risk and materiality assessment; data lineage and quality results; methodology and assumptions; development and validation tests; performance and monitoring reports; limitations and threshold register; governance approvals; conflict and independence declarations; user procedures and training; vendor due diligence, contracts and independent reports; incident and continuity records; findings and remediation evidence; and the final approval or restriction decision.
Retention periods, access controls and change histories should follow the institution’s applicable records, privacy and security requirements. Preserve the version of the system and data configuration that was actually reviewed; a later configuration cannot silently replace the evidence for an earlier decision.
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