Evaluate an AI tool against a defined financial-risk task, your institution’s data and operating conditions, and the consequences of error—not a vendor demo or a universal score. Before choosing a system, document what decision it supports, who relies on it, what evidence shows it works in that setting, what controls will constrain it, and how you will detect when it should be changed or stopped.
What system are you evaluating, and what will it be allowed to do?
Start with the proposed use, not the product category. Describe the risk function, the decision or workflow the output informs, the users, the people or organizations that may be affected, and what happens if the system is unavailable or wrong. State what the system may recommend or calculate—and what it must not decide on its own.
Classify the system accurately. The distinction matters for U.S. banking guidance, but it does not by itself determine whether a tool is safe or suitable.
| System type | What to establish | U.S. banking guidance boundary |
|---|---|---|
| Traditional statistical or quantitative model | Purpose, assumptions, data, methodology, outputs, and the decision it supports. | The revised interagency model risk guidance applies to this category. |
| Non-generative, non-agentic AI model | How the system produces its outputs, how they are interpreted, and what limits or controls apply. | The guidance also covers this category. |
| Generative AI system | How it handles prompts and data, produces content, and can be constrained and checked in the intended workflow. | Generative AI is explicitly outside the guidance’s scope. |
| Agentic AI system | Which actions it can take, what permissions it has, and where approval or intervention is required. | Agentic AI is explicitly outside the guidance’s scope. |
The Federal Reserve’s revised interagency guidance, dated April 17, 2026, states: “Generative AI and agentic AI models are novel and rapidly evolving. As such, they are not within the scope of this guidance.” That exclusion is not an exemption from managing risk: the guidance says existing risk-management and governance practices should inform controls for tools outside its scope. The document is most relevant to banking organizations with over $30 billion in total assets; that is a relevance indicator for banks, not a universal threshold for financial firms or AI systems. See the Federal Reserve’s supervisory guidance for its scope and qualifications.
#1 Best Overall
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Also record jurisdiction, institution type and size, deployment setting, data sources, degree of human involvement, and whether the use is internal or customer-facing. Regulatory implications depend on those facts; do not assume U.S. banking guidance governs every firm or use case.
How much evaluation is warranted?
Set risk tolerance before selecting a vendor or performance metric. Scale the evaluation to the potential consequences of error, how widely the tool will be used, how reversible its effects are, and how much users will rely on its outputs. A system used to inform a review is different from one whose output can directly trigger consequential action.
The Federal Reserve guidance takes a risk-based approach: model practices should reflect an institution’s model risk profile, size, and complexity, and comparable models can warrant different practices depending on the institution and purpose. Its relevance threshold is not a pass/fail rule. NIST likewise frames AI risk management around organizational priorities and tolerance rather than a single prescribed score.
Rank #2
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Write down the tolerable failure modes, who can accept residual risk, and what evidence is required for approval. If the team cannot specify what an unacceptable result looks like, it cannot make a defensible go/no-go decision.
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Ask the provider and internal development team for evidence that can be reviewed and reproduced. A product description or benchmark result alone does not establish suitability for your institution’s workflow.
- System and purpose: intended use, design, assumptions, output interpretation, limitations, and prohibited uses.
- Data and development: a description of development and evaluation data, their relevance to the intended population and setting, and known gaps or representativeness limits.
- Testing: test design, metrics, results, failure cases, and evidence under conditions resembling the proposed deployment. Ask for methods and artifacts detailed enough for independent review and repeatable analysis.
- Operational behavior: performance boundaries, known failure modes, dependencies, and what happens when inputs are missing, unusual, or outside the system’s expected range.
- Change history: how updates to the model, data, components, or service are evaluated and communicated.
Require tests that reflect your institution’s task, population, data distribution, workflow, and market environment. A benchmark or demonstration using different conditions is not a substitute. This is especially important for generative AI: NIST’s Generative AI Profile (NIST AI 600-1, July 26, 2024) warns that available pre-deployment tests may be inadequate, inconsistently applied, or mismatched to deployment. It also cautions that anecdotal testing, or tests designed for people, does not guarantee domain-specific validity or reliability.
Rank #3
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The NIST AI RMF Core provides a useful structure for checking validity and reliability alongside security, resilience, privacy, fairness, and explainability. Treat these as questions to assess in context, not as a claim that one test can prove overall safety.
What should you ask an AI vendor?
Ask whether the provider can explain conceptual soundness, design, development data, output interpretation, limitations, and change history well enough to support meaningful validation. The Federal Reserve guidance recognizes that proprietary components may restrict access to code, data, or methodology, but vendor products remain subject to validation and ongoing outcome analysis. A refusal to disclose source code does not remove your institution’s need to assess accuracy, fitness for purpose, and reliability.
Where the system depends on generative AI providers or integrations, extend diligence to data handling and provenance, 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 transparency and third-party-risk tools. They can inform review; none alone proves the system is safe or fit for your use.
Rank #4
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- What can we inspect or test if proprietary material cannot be disclosed?
- How are material model, service, data, and subprocessor changes notified and assessed?
- What data does the provider retain, and how is it used, protected, and deleted?
- What incident, continuity, and exit arrangements are available if the service changes or becomes unavailable?
Document unanswered questions and treat material gaps as decision risks, not as evidence of acceptable performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare tools for the same task?
Compare candidates against the same use case, evidence standard, and deployment assumptions. The axes below organize the review; they do not imply that any candidate performs well or that there is a universal ranking.
| Evaluation axis | Question for the review | Evidence to examine |
|---|---|---|
| Task and context fit | Does the system address the defined decision in the proposed workflow? | Intended-use description, workflow mapping, and context-matched testing. |
| Validity and reliability | Are outputs sufficiently dependable for the stated purpose? | Documented methods, representative test results, limitations, and outcome analysis. |
| Robustness | How does performance respond to changing data, products, exposures, clients, or market conditions? | Stress or scenario tests, known sensitivities, and monitoring plans. |
| Interpretability and challenge | Can users understand the output’s limits and question or override it appropriately? | Output explanations, user guidance, review procedures, and override arrangements. |
| Fairness and affected parties | Could the system produce materially different or harmful outcomes for people or groups? | Relevant impact assessment and tests, where people or groups may be affected. |
| Privacy, security, and resilience | Are information and service risks controlled for the deployment? | Data-handling practices, security review, resilience evidence, and contingency plans. |
| Governance and supplier risk | Can the institution oversee the tool and respond to provider changes? | Accountability, transparency, change notices, third-party controls, and exit options. |
| Lifecycle operations | Can the organization monitor, investigate incidents, and sustain controls over time? | Monitoring ownership, alert thresholds, incident procedures, and operational burden. |
Weight the axes according to the consequences and exposure defined for the use case. Do not collapse material weaknesses into a single score that conceals where evidence is missing or a control is inadequate.
Best Value
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How do you decide, document, and monitor after launch?
Make the approval record useful to the next reviewer, not just a procurement file. Record the decision and rationale, evidence reviewed, unresolved limitations, accountable owners, conditions of use, controls, monitoring cadence, escalation triggers, and approval or rejection. Specify how users can report problems and how affected people can seek review or appeal when relevant.
Before deployment, define the response to deterioration or changing conditions: add a control or overlay, adjust or redevelop the system, restrict its use, or suspend and retire it. Set triggers that reflect the use case, including changes in products, exposures, activities, clients, data relevance, and market conditions. Assign someone authority to act when a trigger is reached.
Monitor in operation, not only at initial approval. Review performance and outcomes at planned intervals and when incidents, material changes, or emerging conditions warrant it. Maintain an escalation and incident process, along with a contingency for service interruption or withdrawal. NIST’s Manage function treats risk treatment as continuing prioritization, response, recovery, communication, and improvement—not a one-time sign-off.
How should NIST AI RMF fit into the process?
Use the NIST AI Risk Management Framework as a voluntary lifecycle organizer, not a financial regulation, certification, or substitute for applicable law. Its Map, Measure, and Manage functions can help structure context-setting, evidence review, and ongoing response; Govern provides the broader organizational lens. The framework status page says AI RMF 1.0 is voluntary and that NIST is revising it, so check the current NIST AI RMF status when relying on it.
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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 matchFor U.S. banks, read the framework alongside the applicable supervisory expectations and internal policies. The Federal Reserve’s April 17, 2026 guidance covers traditional statistical and quantitative models and non-generative, non-agentic AI, while explicitly excluding generative and agentic AI. Its boundary should inform the governance discussion, not be stretched into a claim that those excluded systems need no controls.
