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Which guidance applies to a financial-services AI tool?
The answer depends on the institution, the system, its use, and the jurisdiction. The strongest regulatory framing here is for U.S. banking organizations; the cited banking guidance should not be assumed to apply identically to nonbanks or institutions in other jurisdictions. NIST’s AI Risk Management Framework is voluntary and can support risk management, but it does not replace applicable law or supervisory obligations.
| Resource | What it helps assess | Important scope point |
|---|---|---|
| Interagency Guidance on Third-Party Relationships | Risk management over the third-party relationship, including due diligence, contracts, and ongoing monitoring. | Depth of oversight should reflect the relationship’s risk, complexity, and importance to the banking organization. |
| Federal Reserve Supervisory Guidance on Model Risk Management | Model governance and validation where the system falls within the guidance’s model-risk scope. | The guidance revised April 17, 2026 defines a model through quantitative estimation grounded in statistical, economic, or financial theory. It excludes generative and agentic AI; it says its principles apply to traditional statistical and quantitative models and non-generative, non-agentic AI. It is most relevant to banking organizations above $30 billion in assets, while noting that some smaller banks may also find it relevant because of significant model risk. |
| NIST AI Risk Management Framework | Lifecycle risk management involving management and other AI actors. | AI RMF 1.0 is voluntary; use it as a risk-management aid, not as a substitute for applicable requirements. |
| NIST Generative AI Profile | Additional procurement and governance considerations for generative AI, including privacy, intellectual-property, and information-security risks in third-party integrations. | Its suggested measures—such as software bills of materials, service-level agreements, attestation reports, and documented pre-deployment testing—are options to evaluate, not universal legal requirements. |
Do not treat a vendor’s use of the word “AI” as a risk classification. Determine whether the product is a statistical or quantitative model, non-generative AI, generative AI, an agentic system, or a combination. A system outside the 2026 model-risk guidance’s scope can still require controls under other applicable obligations and the institution’s own risk-management practices.
1. Define the use and assign accountability
Write a short use-case record before sending a questionnaire to the vendor. It should make clear what the product is being acquired to do and how it will fit into the institution’s work.
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- Purpose and users: the business objective, intended users, and whether customers or other people will interact with or be affected by the system.
- Decision and workflow role: which decisions or steps it informs, automates, recommends, or communicates, and where human review occurs.
- Data and connections: input and output data, sensitivity, system integrations, access permissions, and where results flow next.
- Failure consequences: plausible effects of inaccurate, unavailable, manipulated, or inappropriate outputs on customers, operations, or the institution’s financial condition.
- Ownership: a business owner accountable for the use, with risk, legal and compliance, security, privacy, procurement, and model-validation participants as appropriate.
Use this record to establish the review boundary: an internal productivity assistant and a tool that influences fraud detection, underwriting, or customer treatment should not automatically receive the same diligence. That is an application of proportionality, not a claim that all tools in either category have identical risk.
2. Set the third-party risk tier
Classify the relationship based on the service’s actual role, access, and consequences—not on the vendor’s marketing or the novelty of its technology. Consider whether the provider will handle sensitive or customer information, process transactions, support an essential technology or business service, contact customers, or affect important operations or the institution’s financial condition.
Use the classification to determine the depth of due diligence, required approvals, contract protections, and monitoring cadence. Federal banking guidance calls for more comprehensive oversight of higher-risk or critical activities. Record the rationale so reviewers can understand why the selected level of scrutiny fits the intended use.
3. Evaluate the provider, not just its model
Request evidence that relates to the service the institution will actually use. Review documents for scope, period covered, exceptions, and relevance; a certification or audit report is not blanket assurance about every product, deployment, or control.
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- Governance and competence: named accountability, risk-management processes, independent testing, remediation practices, relevant experience, staffing, key-person dependencies, and continuity planning.
- Business viability and authority: ownership, legal authority and relevant licenses, financial condition, and business strategy.
- Security and data handling: access controls, encryption, secure development, vulnerability testing, incident response, and the security implications of system connectivity.
- Resilience: recovery arrangements and the provider’s ability to sustain service during disruption.
- Subcontractors and dependencies: downstream parties’ roles, locations where relevant, data access, controls, notification duties, and change management.
- Assurance evidence: audit reports, SOC reports, certifications, or conformity assessments, including their exceptions and the service boundary they cover.
These are core areas of interagency third-party due diligence. Ask for evidence rather than accepting a general description of the provider’s program.
4. Ask what the AI service does—and what the provider will disclose
Request service-specific documentation covering the intended purpose, architecture and dependencies, model or service version, data provenance and use, known limitations and failure modes, performance evidence, change practices, and how updates will be communicated. Establish what the provider will disclose and what it will withhold.
A vendor may not provide underlying code, data, or methodology. That does not remove the institution’s responsibility to assess the service. The Federal Reserve’s model-risk guidance recognizes that vendor opacity can limit access to those materials while maintaining that validation principles remain applicable. Record the uncertainty, seek alternative evidence, apply compensating controls, or decline the use if the remaining risk is unacceptable. NIST likewise warns that third-party technologies may be complex or opaque and that a provider’s risk tolerances may not align with the deploying organization’s.
For generative AI, specifically establish how inputs and outputs are handled: whether prompts or customer information are retained or used for training; how privacy and intellectual-property rights are addressed; and what third-party components are involved. NIST’s Generative AI Profile highlights measures such as software bills of materials, service-level agreements, and attestation reports as potential ways to improve transparency and governance.
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5. Validate performance in the proposed workflow
Do not rely solely on vendor benchmarks. Set acceptance criteria before testing, then evaluate the service using representative tasks and data for the intended financial-services context. Include edge cases and conditions that could produce different outcomes for affected groups.
- Measure task-relevant accuracy and reliability, stability, and error types.
- Assess robustness and security behavior, including how the service responds to inappropriate or adversarial inputs where relevant.
- Determine what level of explainability or interpretability decision-makers need to understand and challenge outputs.
- Test whether staff can detect, correct, override, or escalate failures within the real workflow.
- Keep records of test data and methods, assumptions, results, limitations, approvals, and remediation decisions.
Validate the end-to-end process, not just the model in isolation. Data transformations, thresholds, integrations, human review, and downstream decisions can change how outputs affect customers or operations. For high-impact uses, make the accountability path explicit and ensure authorized staff can override, escalate, or suspend the system. NIST recommends iterative, documented testing, evaluation, validation, and verification; model-risk guidance calls for understanding conceptual soundness, design, development data, and performance where it applies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Put evidence, data protections, and exit rights in the contract
Match contractual protections to the service’s risk tier and operating role. The agreement should specify responsibilities and give the institution a practical way to verify the provider’s performance and respond to changes.
- Service and quality: precise scope, responsibilities, service levels, and quality measures suited to the work rather than only service volume.
- Data rights: permitted uses, access, retention, return or deletion, reuse and resale limits, and restrictions on training or disclosure.
- Information and reporting: timely access to performance, security, financial, audit, and control information.
- Changes and incidents: notice of incidents or breaches, material model or service changes, new subcontractors, and compliance lapses.
- Assurance and remediation: appropriate audit, independent assessment, remediation, and regulatory access rights.
- Downstream providers: approval or notice provisions, required flow-down controls, and provider accountability for subcontractors.
- Continuity and exit: continuity and recovery arrangements, joint testing where appropriate, transition support, termination rights, reasonable transition periods, data and records export, and secure deletion.
Interagency guidance identifies topics including data access, reporting, audit and remediation, subcontracting, resilience, termination, and regulatory supervision. For generative AI, NIST also identifies service-level agreements as a possible control. Tailor each term to the relationship rather than treating a template or checklist as sufficient by itself.
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7. Monitor changes and define escalation routes
Set a named owner and review cadence proportionate to risk. Monitor service performance and outcomes, complaints, incidents, audit findings, security and compliance changes, the provider’s financial condition, ownership or staffing changes, subcontractor changes, data locations, and contract performance. Reassess when the use case, model, data, integrations, or provider changes.
Define in advance who can restrict use, require corrective action, suspend the service, initiate a transition, or terminate the relationship. Federal Reserve guidance describes ongoing monitoring as a way to confirm the quality and sustainability of controls and contractual performance, escalate significant concerns, and respond to them. Its examples include material or repeat audit findings, deteriorating financial condition, security breaches, data loss, service interruptions, and compliance lapses; see Third Party Risk Management (May 2024).
How to compare two AI vendors for the same use
Use the same use case, acceptance criteria, and evidence requests for every provider. Compare the relationship as well as model performance; a strong benchmark cannot compensate for an unworkable data arrangement or an inability to govern changes.
| Comparison axis | Evidence to compare |
|---|---|
| Use-case fit | Results on representative tasks, error patterns, and documented limitations. |
| Transparency and assurance | Documentation, test results, change notices, audit scope, and evidence available for independent validation. |
| Data governance | Data access, retention, training reuse, location, deletion, privacy, and intellectual-property terms. |
| Security and resilience | Controls, system boundaries, incident response, recovery, and continuity arrangements. |
| Dependencies and exit | Subcontractor visibility, provider concentration, portability, switching costs, and practical transition feasibility. |
| Governability | Human oversight, monitoring hooks, logs, escalation, remediation, and contractual rights. |
| Total relationship risk | Financial stability, service criticality, customer impact, regulatory access, and operational dependence. |
These comparison axes synthesize NIST and federal banking guidance; they are not a ranking of vendors. If a provider cannot supply a comparable evidence set, treat that gap as a decision factor rather than assuming the missing information is favorable.
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At approval, record the intended use, risk classification, evidence reviewed, validation results, unresolved limitations, required controls, contract protections, accountable owner, monitoring cadence, and escalation or exit triggers. The decision should state whether the tool is approved for that defined use, approved only with specified controls, deferred pending remediation, or not accepted. Revisit the decision when the service or its context materially changes.
For a voluntary lifecycle structure, NIST describes the AI Risk Management Framework; the framework’s publication is also available as AI RMF 1.0. Applicability of laws and supervisory expectations beyond the cited U.S. banking guidance depends on institution type, jurisdiction, product, use, and affected customers.
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