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Evaluate an AI tool for financial risk management against a defined task, representative evidence, and the conditions in which your organization will use it—not a vendor demo or a universal score. Set the depth of review according to the consequences of error, the system’s role in decisions, and your institution’s context; then document approval conditions, human oversight, and ongoing monitoring.
Start by identifying what kind of system you are assessing
“AI tool” can describe systems with different designs and risk profiles. Before choosing tests or controls, document whether the candidate is a traditional statistical or quantitative model, a non-generative AI model, a generative AI system, or an agentic system. Also specify the financial risk function it supports, the decision or workflow it informs, and whether it can take actions or only provide information.
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For U.S. banking organizations, the Board of Governors of the Federal Reserve System’s interagency Supervisory Guidance on Model Risk Management, dated April 17, 2026, covers traditional statistical and quantitative models and non-generative, non-agentic AI models. It expressly excludes generative and agentic AI, describing them as novel and rapidly evolving. The guidance says existing risk management and governance practices should inform controls for systems outside its scope; exclusion is not a reason to treat those systems as risk-free.
The guidance says it is most relevant to banking organizations with over $30 billion in total assets. That is a relevance statement for banking organizations, not a universal legal threshold for every financial firm or every AI system. Identify your jurisdiction, institution type, size, and applicable supervisory requirements before drawing regulatory conclusions.
#1 Best Overall
| System type | What to establish | How the 2026 U.S. banking guidance applies |
|---|---|---|
| Traditional statistical or quantitative model | Its intended purpose, assumptions, data, validation evidence, and place in the decision process. | Within the guidance’s stated scope. |
| Non-generative, non-agentic AI model | Its intended purpose, how outputs are produced and used, performance evidence, and limitations. | Within the guidance’s stated scope. |
| Generative AI system | What it generates, what sources or tools it can use, how users verify outputs, and how sensitive inputs are handled. | Outside the guidance’s stated scope; use relevant existing governance practices and assess the system’s specific risks. |
| Agentic AI system | What actions it can take, what permissions it has, when it must stop for approval, and how actions can be reversed or contained. | Outside the guidance’s stated scope; assess its autonomy and controls rather than treating it as an ordinary model. |
Define the use case and consequences before testing
Write a short use-case statement that names the system, task, intended users, affected parties, inputs, outputs, deployment setting, and the decision or workflow the output supports. State what the system is not authorized to decide. Specify what happens if it is unavailable, wrong, delayed, or used outside its intended purpose.
Then rate the consequences of error, scale of use, reversibility, exposure, and reliance on the output. Decide risk tolerance before comparing vendors or selecting metrics. The Federal Reserve guidance takes a risk-based approach: the nature, scale, and use of models relative to business risks matter, and comparable models can warrant different practices at different organizations or for different purposes. The NIST AI RMF Core likewise organizes risk management around organizational priorities and risk tolerance.
- Record the jurisdictions and business units in scope.
- Identify who owns the business outcome, who validates the system, and who can approve or restrict its use.
- Map the people, customers, counterparties, or groups who may be affected.
- Set acceptable error consequences and the circumstances that require human review, escalation, or a stop in use.
Demand evidence that matches your intended deployment
Ask the provider and internal development team for evidence tied to the task and setting you have defined. A benchmark or demonstration cannot establish performance for a different institution, population, data distribution, workflow, or market environment. Testing should be documented, repeatable, and representative of expected operating conditions.
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Request a reviewable evidence package
- System description, intended purpose, assumptions, and known limitations.
- Descriptions of development and evaluation data, including how well they represent the proposed users, products, exposures, and conditions.
- Test design, metrics, results, and the criteria used to interpret them.
- Evidence addressing validity, reliability, security, resilience, privacy, fairness, and explainability where relevant to the use.
- Known failure modes, out-of-scope uses, and conditions under which performance may deteriorate.
- Documentation of how a result can be interpreted, challenged, and escalated by the people who rely on it.
NIST’s Measure function calls for tests before deployment and at regular intervals in operation, with documented performance and risk considerations. For generative AI, the NIST Generative AI Profile (NIST AI 600-1, July 26, 2024) cautions that available pre-deployment tests may be inadequate, unsystematic, or mismatched to deployment context. Anecdotal tests or tests designed for people do not by themselves establish validity or reliability in a particular domain.
Design tests around actual failure consequences
Translate the use case into tests that reveal whether the system remains fit for purpose under the conditions it will encounter. Include ordinary operating conditions as well as plausible changes in data, products, exposures, clients, and market conditions. Where people or groups may be affected, assess relevant fairness and bias risks. For outputs used in consequential decisions, test whether users can understand limitations and appropriately challenge results.
Do not invent a universal accuracy threshold or pass mark. The cited guidance does not provide a product score or vendor ranking. Set acceptance criteria for the specific task, risk tolerance, and evidence before deciding whether results are adequate.
Assess the vendor, data handling, and supply chain
Vendor-provided systems still require meaningful assessment. The Federal Reserve guidance recognizes that proprietary components can limit access to code, data, or methodology, but says vendor products remain subject to validation and ongoing outcome analysis. Ask for enough information to assess conceptual soundness, design, development data, output interpretation, limitations, and performance.
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If source code or training data cannot be disclosed, establish what other evidence you can examine and whether it is sufficient for your risk. Ask for independent or otherwise reviewable testing artifacts, performance information relevant to your use, and clear disclosure of material changes. An inability to inspect one artifact does not remove the need to validate the system’s accuracy, fitness for purpose, and reliability.
Questions to put to the provider
- What exact task is the system designed to support, and what uses are outside its intended purpose?
- What evidence supports performance in conditions similar to our proposed deployment?
- What assumptions, limitations, and failure modes are known, and how are they communicated to users?
- How are input data handled, retained, protected, and used? What privacy, intellectual-property, and information-security considerations apply?
- Which subcontractors, external services, or other system components are involved, and how are material changes communicated?
- What information can we inspect if proprietary restrictions limit access to code, data, or methodology?
- What monitoring, incident support, contingency, and exit arrangements will remain available?
For generative AI providers and integrations, extend diligence to input-data sources and handling, intellectual property, privacy, information security, subcontractors, and system components. NIST’s Generative AI Profile identifies software bills of materials, service-level agreements, and attestation reports as possible supports for transparency and third-party risk management. These artifacts can inform diligence; none alone proves that a system is safe or suitable.
Compare candidates on decision-relevant criteria
If multiple tools are genuinely in scope, use the same task definition, evidence expectations, and decision criteria for each. A comparison should help explain trade-offs in your context, not create a league table detached from intended use.
| Evaluation axis | What to compare |
|---|---|
| Task and context fit | Whether the system addresses the defined task and has evidence relevant to your deployment conditions. |
| Validity and reliability | Test design, representative evidence, performance limitations, and repeatability of results. |
| Robustness | How performance may respond to changes in data, products, exposures, clients, or market conditions. |
| Interpretability and challenge | Whether users can understand output limits, question results, and escalate concerns. |
| People and data protections | Relevant fairness, privacy, information-security, and resilience evidence. |
| Human and operational controls | Oversight, override, appeal, escalation, incident handling, and continuity arrangements. |
| Provider and lifecycle risk | Transparency, data provenance, change communication, monitoring support, and contingency options. |
These axes reflect concerns in the Federal Reserve guidance and NIST’s Map, Measure, and Manage functions; they are a comparison structure, not evidence that any candidate product performs well.
Make a documented decision and keep control after launch
Approval should be conditional on the use, evidence, and safeguards reviewed—not a blanket endorsement of a vendor or technology. Record the business decision, evidence considered, unresolved limitations, owners, conditions of use, controls, monitoring cadence, escalation triggers, and rationale for approval, restriction, or rejection.
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Set monitoring and change triggers
Choose monitoring that can identify deteriorating performance and changes that could make existing evidence less relevant. Define in advance who reviews signals and what action follows. Depending on the issue, responses may include an overlay, adjustment, redevelopment, restricted use, suspension, or retirement.
- Monitor outcomes and continued fitness for the intended task.
- Reassess when products, exposures, activities, clients, data relevance, or market conditions change materially.
- Require notification and review when the provider changes the system, data, components, or relevant operating conditions.
- Maintain incident, escalation, user feedback, and appeal routes appropriate to the workflow.
- Ensure an accountable owner can restrict or stop use when risk or performance exceeds tolerance.
NIST presents the AI RMF as a voluntary lifecycle framework, not a mandatory certification or substitute for applicable law; its status page also says the framework is being revised. Use its current status page when determining how it fits your process. Its Core’s Manage function supports ongoing prioritization, response, recovery, communication, and improvement.
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