Practical guide
AI-Enabled Knowledge Work in Banking: Start With a Use-Case Control Card
Banking teams should not treat every AI-assisted task as the same risk. Drafting an internal summary, analyzing model output, preparing customer communication, and supporting a controlled decision differ in data, review, and consequence. A use-case control card makes the intended use and evidence path explicit before a workflow is expanded.
Banking teams should not treat every AI-assisted task as the same risk. Drafting an internal summary, analyzing model output, preparing customer communication, and supporting a controlled decision differ in data, review, and consequence. A use-case control card makes the intended use and evidence path explicit before a workflow is expanded.
Describe the use, not the technology label
Record the business process, users, affected customers or stakeholders, input data, model or tool role, output destination, human decision, potential harm, and accountable owners. State whether the AI drafts, ranks, predicts, recommends, or executes. Capture third-party dependencies and the conditions under which the output must be rejected or escalated.
In April 2026, the Federal Reserve issued SR 26-2 with revised model risk management guidance, superseding SR 11-7 and emphasizing a risk-based approach tailored to an organization’s model profile, size, complexity, and use (Federal Reserve SR 26-2). Applicability depends on the institution and use; this article does not interpret supervisory obligations.
Sources: [1]
Build the control card
Use nine fields: intended decision, permitted users, approved data, model role, required source evidence, validation or testing route, human review, monitoring signal, and stop condition. Link the card to current policies and system versions. If the workflow relies on a vendor claim, record the evidence available to the institution rather than copying the vendor’s assurance language.
NIST’s voluntary AI RMF provides govern, map, measure, and manage functions for use-case-aware risk work (NIST AI RMF). It can help organize questions across the lifecycle but does not certify a banking control or replace applicable regulation, supervisory guidance, or institution-specific standards.
Sources: [2]
Worked artifact: credit-policy briefing
Imagine a synthetic internal assistant that drafts a weekly briefing from approved credit-policy documents. The card permits summarization for trained policy staff and prohibits customer communication or credit decisions. Inputs must come from a versioned repository. Every material statement requires a source reference, and the reviewer must check effective dates and conflicts.
A test set includes current rules, superseded versions, an absent answer, and contradictory regional guidance. The stop condition is any unsupported statement presented as current policy. When that condition occurs, the workflow returns to controlled testing. The evidence packet contains the source snapshot, test questions, outputs, reviewer dispositions, tool version, and release decision.
Revisit the card when context moves
Review the use case after model, source, permission, policy, vendor, or population changes. Monitor for missing citations, stale sources, override patterns, and use outside the approved destination. A passed test in one configuration does not establish continuing effectiveness or suitability for a different banking process.
Sources
- 1.Federal Reserve SR 26-2 · www.federalreserve.gov
- 2.NIST AI RMF · National Institute of Standards and Technology