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Start with the rule that governs your evidence
The current U.S. interagency model-risk guidance was issued on April 17, 2026 by the Office of the Comptroller of the Currency (OCC), Federal Reserve Board, and Federal Deposit Insurance Corporation (FDIC). It supersedes the earlier interagency guidance and uses a risk-based approach tailored to an institution’s risk profile, size, operational complexity, and model use. It is expected to be most relevant to banking organizations with more than $30 billion in total assets, although smaller organizations may also find it relevant when their model-risk exposure is significant.
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The guidance is not an enforceable or prescriptive standard. The OCC bulletin states: “The 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.” Treat it as a framework for demonstrating effective risk management, not as a universal form or checklist.
Its model definition covers a complex quantitative method, system, or approach that applies statistical, economic, or financial theories to input data to produce quantitative estimates. Simple arithmetic, deterministic rules, and software with no such theory underpinning its design or use are excluded. Generative and agentic AI are also outside this guidance’s scope. That exclusion does not mean those tools are risk-free: apply the institution’s broader governance, information-security, privacy, third-party, conduct, and operational-risk controls to them.
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The sources establish a U.S. banking baseline, not a complete legal answer for securities firms, insurers, payment companies, credit unions, non-U.S. institutions, or a particular use case. Identify the laws, supervisory expectations, contractual duties, and internal policies that apply to your organization before finalizing the audit file.
Design an evidence chain an auditor can follow
A reviewer should be able to start with a business purpose and trace each conclusion to an owner, artifact, decision, and date. Store the record in a controlled location with version history, access controls, retention rules, and links to the underlying evidence. A model card by itself is not enough if it cannot be connected to testing results, approvals, production behavior, and change decisions.
Minimum system record
| Record element | What to capture | Why it matters in an audit |
|---|---|---|
| Identity | System name, unique identifier, version, deployment date, environment, business owner, technical owner, and vendor (if any) | Prevents confusion between similar systems and establishes which version was reviewed. |
| Purpose and use | Intended business process, decisions supported, users, affected customers or stakeholders, approved uses, prohibited uses, and human-decision responsibilities | Shows what the system is supposed to do and where its outputs may not be used. |
| Inventory status | Whether the system is in the formal model inventory; the definition or policy applied; and the governance route used if it is outside that definition | Explains why a system received a particular level of oversight. |
| Risk context | Materiality, impact, complexity, data sensitivity, regulatory relevance, assumptions, limitations, and control environment | Demonstrates that review depth was proportionate to risk. |
| Evidence index | Requirements, design documents, data descriptions, test plans, results, validation reports, approvals, incidents, monitoring reports, and change records | Lets an auditor reproduce the path from requirement to conclusion. |
| Decision log | Approval, restriction, remediation, suspension, redevelopment, or retirement decisions; decision-maker; date; rationale; and conditions | Shows accountability and how exceptions were handled. |
Keep an artifact register
- Assign every artifact a stable identifier, owner, creation date, effective date, and superseded version.
- Record the system version, code or configuration hash where available, data snapshot or query definition, and execution environment for each test.
- Preserve negative results, rejected approaches, exceptions, and corrective actions rather than keeping only the final favorable output.
- Use read-only or otherwise tamper-evident storage for finalized approvals and test results, while retaining a controlled process for amendments.
- Map each artifact to the risk, requirement, or approval condition it supports.
Step 1: Define purpose, boundaries, and accountability
Write an intended-use statement
State the business question, the output produced, the decisions it may inform, and the decisions it may not make. Describe the population, geography, products, time period, and operating conditions covered by the evidence. If a human must review an output, specify what the reviewer must check and who retains final decision authority.
Document foreseeable misuse. Examples include applying a credit-risk score to a product or population absent from development data, treating a probabilistic output as a definitive determination, or allowing an internal assistant to take an action that was approved only for drafting. A boundary is useful only when it is reflected in access controls, procedures, training, and monitoring.
Classify the system and select its governance route
Explain whether the system meets your institution’s model definition and why. For a quantitative, non-generative, non-agentic model, identify the applicable model-risk policy, inventory tier, validation standard, and approval authority. For a generative or agentic system, record that it falls outside the interagency model definition and identify the broader governance controls that apply instead. Do not let an “out of scope” label end the analysis.
Name roles, not just departments
Assign accountable people for development, business use, validation or independent challenge, monitoring, incident response, vendor management, records, and final approval. Describe reporting lines and any potential conflict of interest. A committee name without a named decision owner makes responsibility difficult to prove.
Step 2: Record the risk and context assessment
Risk documentation should explain why the system received its review depth. The interagency guidance and NIST AI Risk Management Framework 1.0 emphasize context, data, assumptions, design choices, testing, outcomes, and ongoing monitoring rather than a one-time technical check.
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- Decision impact: Does the output affect credit, pricing, capital, liquidity, fraud controls, customer access, employment, compliance, or another consequential decision?
- Scale: How many customers, transactions, employees, or counterparties can be affected, and how quickly can the system act?
- Reversibility: Can a wrong output be detected and corrected before harm occurs?
- Complexity: Are the methods, features, dependencies, or interactions difficult to understand or challenge?
- Data sensitivity: Does the system use personal, confidential, proprietary, restricted, or third-party data?
- Change exposure: Can data, prompts, models, vendor services, policies, or connected tools change without a full redeployment?
- Concentration and dependency: Would an outage or common error affect a large portion of the institution or depend on a single provider?
Record the rating method, evidence considered, reviewer, date, and resulting control tier. If a factor is not applicable, say why rather than leaving a blank field.
Describe data and assumptions
Identify data sources, collection methods, ownership, lineage, sampling, labels, exclusions, transformations, retention, and access restrictions. Explain known gaps, representativeness limits, stale data, missing values, proxies, and any assumptions that could change the result. Connect each material assumption to a test, control, limitation, or monitoring signal.
State limitations in operational language
Write limitations so a user can act on them. “May perform poorly under distribution shift” is incomplete; specify which shift, how it will be detected, what threshold triggers escalation, and what users must do meanwhile. Include known error modes, unsupported populations, confidence or uncertainty information, interpretability constraints, latency limits, dependency failures, and conditions under which the output must not be used.
Step 3: Build a lifecycle testing and validation record
NIST AI RMF 1.0 treats testing, evaluation, verification, and validation as related but distinct activities across design, development, deployment, and operation. The interagency guidance likewise emphasizes developmental testing, validation, monitoring, and outcome analysis. Your record should show what was tested, under which conditions, by whom, against what expectation, and how the result affected approval.
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| Lifecycle stage | Questions to answer | Evidence to retain |
|---|---|---|
| Design | Do the method, objective, data plan, and assumptions fit the intended use? | Requirements, conceptual design, assumptions, data specification, risk assessment, and design-review decisions. |
| Development | Does the implementation behave as designed on appropriate data and scenarios? | Development datasets, preprocessing, code or configuration version, experiments, benchmark results, error analysis, and unresolved issues. |
| Validation or independent challenge | Can an appropriately independent party challenge conceptual soundness, implementation, performance, and limitations? | Validation plan and report, reviewer qualifications, challenged assumptions, reproduced results, findings, management responses, and closure evidence. |
| Deployment | Does the production integration preserve the tested behavior and controls? | Release approval, interface and access tests, data-pipeline checks, security and resilience results, rollback plan, user acceptance, and production comparison. |
| Operation | Does the system remain fit for use as data, outcomes, dependencies, and conditions change? | Monitoring reports, drift and performance analysis, incidents, overrides, complaints, exceptions, recalibration or retraining decisions, and periodic review. |
Use a reproducible test record
For every material test or evaluation, capture:
- Objective: the requirement, risk, assumption, or failure mode being examined.
- System identity: model or system version, configuration, prompt or policy version where relevant, dependencies, and environment.
- Data and conditions: dataset identifier, time period, sampling, exclusions, scenarios, hardware or service configuration, and any human-in-the-loop steps.
- Method: procedure, code or tool version, random seed where relevant, and the person who executed it.
- Expected result: acceptance criterion, tolerance, threshold, comparator, or qualitative requirement established before reviewing the outcome.
- Actual result: measurements, examples, error categories, confidence intervals or uncertainty information where appropriate, and observed deviations.
- Review: reviewer, date, independence, challenge raised, and whether the test was repeated.
- Disposition: pass, conditional pass, fail, exception, corrective action, owner, due date, and approval impact.
Keep the test plan as well as the result. A passing result without the planned criterion can look like an after-the-fact justification.
Separate test and evaluation from verification and validation
Use your organization’s defined terminology consistently. Testing and evaluation may measure behavior against requirements or scenarios. Verification asks whether the system was built or configured correctly against specifications. Validation asks whether it is appropriate for the intended use and remains supported by evidence. NIST describes these as distinct roles and activities; your policy should identify who performs each one and how independence is maintained.
Test outcomes, not only technical metrics
Where applicable, examine realized outcomes in the business process: approval or decline patterns, loss or error rates, customer complaints, overrides, false positives and negatives, service levels, and differences across relevant populations. Explain why each outcome measure is relevant and what action follows a deterioration. Do not claim fairness, accuracy, robustness, or compliance from a metric that was never defined, measured, and reviewed in context.
Step 4: Create a defensible approval package
An approval should be a decision with conditions, not a signature detached from evidence. The package should let the approver understand the intended use, risk rating, test and validation conclusions, unresolved limitations, required controls, and consequences of proceeding.
Approval-package contents
- System identity, purpose, scope, inventory classification, and accountable owner.
- Risk and materiality assessment, affected stakeholders, data description, assumptions, limitations, and control mapping.
- Test plan and results, validation or independent-challenge report, deployment-integration evidence, and open findings.
- Vendor evidence and oversight plan when an external model, data source, hosted service, or component is used.
- Operating procedures, user training, access restrictions, human review requirements, monitoring thresholds, incident process, and rollback or suspension plan.
- Approval decision, approver’s role, decision date, evidence considered, conditions, accepted limitations, expiry or review date, and escalation or reapproval triggers.
Make conditions measurable
“Monitor closely” is not an approval condition. Specify the metric, source, frequency, owner, threshold, reporting destination, and action. For example, a condition may require monthly outcome review, immediate escalation when a defined error or drift threshold is exceeded, and suspension of a use case until an independent review is complete. The exact threshold must come from the system’s risk assessment and evidence; do not invent a universal number.
Define reapproval triggers
- Material change to the method, model weights, prompt or policy, features, data source, decision rule, or connected tool.
- Use on a new product, population, geography, customer segment, or decision type.
- Material deterioration in performance, data quality, outcomes, resilience, or control effectiveness.
- Significant incident, complaint pattern, regulatory change, security event, or unauthorized use.
- Vendor model, service, subcontractor, hosting environment, or terms change that could affect behavior or access to evidence.
- Expiration of the approved review period or failure to complete a required monitoring or validation activity.
Step 5: Document operation, incidents, and change
Approval is the beginning of the operating record. Retain periodic monitoring results and the decisions they produced. The record should show whether the system continued to meet its intended purpose, not merely whether a dashboard was generated.
Monitoring record
- Performance and outcome measures tied to the original acceptance criteria.
- Input-data quality, missingness, distribution shift, feature or prompt changes, and upstream data incidents.
- Usage volume, latency, availability, access violations, overrides, manual adjustments, and fallback activity.
- Errors, customer complaints, adverse events, near misses, model or system incidents, and remediation status.
- Monitoring owner, review date, conclusion, escalation decision, and evidence of approval for any continued use under a limitation.
Maintain a change log
| Change type | Record | Decision point |
|---|---|---|
| Model or algorithm | Old and new versions, reason, affected behavior, test results, validation impact, and release date | Is full or targeted revalidation required? |
| Data | Source, schema, collection or labeling change, quality impact, and effective date | Does the intended population or risk assessment change? |
| Configuration or integration | Interfaces, thresholds, permissions, prompts, policies, dependencies, and deployment evidence | Does production behavior remain within approved boundaries? |
| Vendor | Provider notice, service or model version, subcontractor, contract change, evidence received, and gap assessment | Is continued use acceptable, conditional, or suspended? |
| Use case | New product, geography, customer group, decision, or user population | Does the change require a new risk assessment and approval? |
Record overlays and manual adjustments
When staff override, adjust, or supplement an output, capture the reason, authority, evidence, and effect on the decision. Frequent overlays may indicate a design limitation, data problem, changed environment, or control failure. Treat them as outcome evidence, not as invisible workarounds.
Handle vendors and opaque systems explicitly
Using a vendor does not transfer accountability. The interagency guidance expects organizations to understand a vendor model’s conceptual soundness, design, development data, performance, ongoing monitoring, and outcome analysis, even when code or methods are proprietary. NIST also emphasizes procurement, domain expertise, cross-functional governance, and third-party risk.
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- System purpose, architecture, versioning, intended and prohibited uses, and known limitations.
- Development-data description, performance evidence, validation or assurance reports, and change-management policy.
- Security, privacy, resilience, access, incident-notification, retention, and subcontractor information.
- Monitoring outputs, service-level information, issue history, and customer-impact or outcome analysis where available.
- Rights to obtain information, test integrations, audit controls, receive timely change notices, and terminate or restrict use when risk is unacceptable.
Document what the vendor would not provide, the risk created by that gap, compensating controls, the person who accepted the limitation, and the condition that would trigger reassessment. “Proprietary” explains a constraint; it does not demonstrate that the system is fit for your use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Define internal audit’s role correctly
Under the banking guidance, internal audit generally evaluates whether model-risk management is rigorous and effective and whether policy is implemented. It generally does not repeat model development or perform the validator’s work. Independence does not mean technical ignorance: auditors need enough subject-matter access to challenge governance, evidence quality, issue closure, and the reliability of management’s conclusions.
An audit procedure that follows the evidence chain
- Select systems using risk, materiality, recent change, incidents, vendor dependence, and prior findings.
- Reconcile the inventory to deployed systems, applications, data pipelines, access lists, and procurement records.
- Trace the intended-use statement to the risk assessment, test criteria, approval conditions, procedures, and monitoring metrics.
- Sample test results and verify that the recorded version, data, environment, method, expected result, actual result, reviewer, and disposition are complete.
- Assess whether independent challenge was actually independent and whether findings were resolved before approval or accepted by an authorized person.
- Inspect production monitoring, incidents, overrides, vendor notices, and change records for evidence that operating controls functioned.
- Confirm that escalation and reapproval triggers were defined, communicated, and used when conditions changed.
- Report control deficiencies with the affected risk, evidence, accountable owner, due date, and criteria for closure.
Choose a proportionate approval approach
Institutions commonly combine a central approval body with delegated decisions for lower-risk systems. The choice should follow risk rather than organizational fashion.
| Approach | Strengths | Risks and safeguards |
|---|---|---|
| Central committee approval | Consistent standards, cross-functional challenge, and visibility of concentrated exposures | Can become slow or ceremonial; publish decision criteria, quorum, evidence requirements, and escalation deadlines. |
| Risk-tiered delegated approval | Faster decisions for limited-impact systems while reserving senior review for material use | Inconsistent judgments or conflicts of interest; define tiers, independence rules, sampling, and central oversight. |
| Hybrid model | Delegates routine approvals and routes high-impact, novel, vendor-dependent, or exception cases centrally | Boundary disputes; maintain a single inventory, routing rules, and an appeal or escalation path. |
Compare systems or approval routes using the same axes: intended use and decision impact; model or system type and scope status; materiality and complexity; data and vendor transparency; testing and validation evidence; limitations and controls; accountability and independence of challenge; and monitoring and change triggers.
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“It is outside the model inventory, so no record is needed”
Fix: Record the classification decision and route the system to broader AI, technology, privacy, security, conduct, or third-party governance as appropriate. Generative and agentic systems are outside the interagency model definition, not outside risk management.
Only final test results are retained
Fix: Preserve the plan, acceptance criteria, data and environment, failures, exceptions, corrective actions, and independent challenge. Without them, an auditor cannot determine whether the result was meaningful or reproducible.
The approval is a signature with no conditions
Fix: Tie approval to named controls, monitoring thresholds, limitations, owners, review dates, and reapproval triggers. Record accepted residual risk and who had authority to accept it.
Vendor assurance is treated as a certificate
Fix: Map vendor evidence to your intended use, data, population, integration, outcomes, and operating controls. Track what is missing and apply compensating controls or restrictions.
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Monitoring exists but does not change decisions
Fix: For each metric, specify the threshold, reviewer, escalation route, and permitted actions. Link incidents and drift findings to restriction, remediation, revalidation, redevelopment, or retirement decisions.
Internal audit duplicates validation
Fix: Audit the design and operation of the model-risk framework, including whether validation was competent and independent, rather than silently taking on the validator’s responsibilities.
A practical final-review checklist
- Can a reviewer identify the exact system, version, owner, purpose, users, affected decisions, and approved boundaries?
- Is the inventory or out-of-inventory classification explained, with the governance route documented?
- Does the risk assessment show materiality, complexity, data context, assumptions, limitations, stakeholders, and control choices?
- For each material risk, is there a planned test, acceptance criterion, result, reviewer, disposition, and approval impact?
- Are conceptual soundness, implementation, outcomes, deployment integration, and ongoing monitoring addressed where applicable?
- Are development, use, validation, monitoring, vendor management, incident response, and approval responsibilities assigned to identifiable roles?
- Does the approval state conditions, accepted limitations, review date, and events requiring escalation or reapproval?
- Do production records include monitoring, incidents, overrides, vendor changes, data changes, and decisions about continued use?
- Can internal audit obtain the underlying evidence without relying on an undocumented explanation?
- Does the record state which conclusions are supported by evidence and which remain limitations or open issues?
Illustrative record structure
The following is an example structure, not a regulator-prescribed form. A financial institution might create one controlled record with these sections:
- System profile: identifier, version, owner, vendor, purpose, users, decisions, boundaries, and inventory classification.
- Risk assessment: impact, materiality, complexity, data, assumptions, stakeholders, limitations, controls, and rating rationale.
- Evidence index: design, data, test, validation, deployment, monitoring, incident, vendor, and change artifacts.
- Decision register: approval, conditions, exceptions, accepted risks, escalation, reapproval, restriction, remediation, and retirement decisions.
- Operating history: monitoring periods, findings, overrides, incidents, vendor notices, changes, and resulting actions.
- Audit support: control owner responses, issue closure evidence, access log, retention details, and links to authoritative records.
Use the structure as a map, then scale the depth. A low-impact deterministic tool may need a concise classification and control record; a complex model affecting consequential decisions may require extensive independent validation, outcome analysis, deployment testing, and continuous monitoring. The defensible principle is not “document everything equally.” It is “document enough for an independent reviewer to understand the risk, challenge the evidence, and see why the institution approved, restricted, changed, or stopped the system.”
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