The Tool Desk
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1. Define the decision, people affected, and audit scope
Start with the financial decision, not the model in isolation. Record the product, decision point, intended and actual uses, affected population, owner, and the potential impact of an incorrect or unfair result. Identify the jurisdiction and the legal and supervisory requirements that apply to this institution and use. A checklist cannot settle those questions for every lender, bank, investment firm, insurer, or location.
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Map the entire decision chain. An AI model may recommend or rank applicants, flag cases for review, or make or trigger a decision automatically. Include the inputs and transformations, model or vendor service, thresholds, policy rules, human review and overrides, and downstream action. Note routes for escalation, appeal, or correction, and identify who is accountable at each step.
Set the audit depth using the decision’s materiality and the system’s risk and complexity. The U.S. Federal Reserve, OCC, and FDIC’s 2026 Supervisory Guidance on Model Risk Management calls for a tailored, risk-based approach rather than a single checklist. It is supervisory guidance, not a universal law; it says it is expected to be most relevant to banking organizations with more than $30 billion in total assets, while it may also be relevant to smaller institutions with significant model-risk exposure. Confirm whether and how it applies to the organization and use under review.
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2. Check the model’s purpose, assumptions, and data
Test whether the model measures what its owner claims
Obtain the model’s purpose, methodology, assumptions, intended-use limits, known weaknesses, and development documentation. Ask whether the target it predicts—and any proxy label used during development—actually represents the financial outcome the institution says it predicts. A model can perform well against a convenient label while failing to answer the real decision question.
Trace data quality and relevance
Review where data came from, how they were collected and transformed, and whether they are complete, accurate, representative, and current for the production population. Check missing values, measurement error, coverage gaps, and changes over time. Compare the development data with the people and decision context in which the model is now used.
Inspect how features were constructed and whether a variable could act as a proxy for a protected characteristic or encode historical institutional or societal patterns. Consider the labels and collection process as well as the feature list: bias can enter through the broader social and technical setting, not just through an obviously sensitive input. NIST’s AI Risk Management Framework takes this context-dependent, socio-technical view and identifies credit underwriting as a financial-services use case.
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Use independent evidence, not just development results
Review development testing and independent validation. Where appropriate for the model and decision, evaluate data not used to fit the model, test on a later time period, compare with a reasonable baseline or incumbent process, back-test predictions against observed outcomes, and investigate outliers. Compare outputs with real-world results and the stated business objective. Consider alternative assumptions or methods where they could materially change the decision.
Break performance down by decision type and relevant cohort, then inspect individual cases where predictions or decisions appear unexpectedly wrong. Choose measures that fit the task: overall error alone may conceal a problem affecting a particular type of decision or group. Set acceptance thresholds before interpreting test results, document what the tests cannot establish, and investigate persistent deviations. Possible responses include recalibration, adjustment, redevelopment, restricting the model’s use, or increasing monitoring.
Evaluate the whole decision, including vendor systems
Test whether policy overlays, thresholds, human overrides, or downstream steps change a model output in ways that create additional errors. A vendor’s confidentiality does not remove the need to understand a model’s design, development data, performance, limitations, and continued fitness for the institution’s purpose. If information is unavailable, record what is missing and how compensating monitoring or restrictions address the uncertainty.
4. Test for bias in the context of the decision
Define plausible harms and appropriate comparisons
Specify how an unfair or harmful outcome could occur in this use. In credit, for example, examine allocation, pricing, service quality, and exclusion from a financial opportunity. Choose legally and contextually relevant groups and intersections, subject to lawful data access and privacy safeguards; document why those groups are relevant.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsCompare outcomes and task-relevant measures across groups. Depending on the decision, that may include approval or denial patterns, error rates, or whether predictions correspond to observed outcomes at similar rates. Examine aggregate patterns and individual files. A difference is a reason to investigate, not a verdict by itself: understand the decision context, measure, data, and possible causes before drawing a conclusion.
Follow disparities through the pipeline
When a difference appears, trace it through data collection and labels, feature choices and proxies, model behavior, thresholds, policy rules, human overrides, and downstream effects. Ask whether the same pattern appears in particular decision types or only after a specific process step. Record the evidence, metric and threshold choices, limitations, rationale, and any mitigation. NIST emphasizes that bias testing should be situated in the application domain rather than treated as a context-free statistical exercise.
5. Verify explanations and consumer-facing controls
For a covered credit decision, test whether the principal reasons communicated for an adverse action accurately reflect the factors that actually drove it. The CFPB’s Circular 2022-03 states that using a complex algorithm does not excuse a creditor from providing specific reasons. A generic notice is not a substitute for tracing how the decision was made.
Follow the path from model features and policy rules to reason-code selection and the notice delivered to the applicant. Test edge cases, overrides, and combinations of factors; retain enough records to reproduce the decision and notice. Also examine complaint handling, routes to correct information or seek review, and whether staff can recognize use outside the model’s intended conditions.
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Keep this obligation distinct from narrower rules for particular financial uses. For example, the CFPB’s automated valuation model rule concerns specified mortgage collateral valuations and related quality-control policies, including random sample testing and nondiscrimination controls. It is not a general rule for every financial AI system.
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6. Review governance, vendors, and change control
Check that responsibilities are clear and that someone independent of model development can challenge the evidence. Review approval records, documentation, access controls, version management, incident handling, and safeguards against use outside validated conditions. For vendor systems, seek information sufficient to evaluate conceptual soundness, development data, performance, customizations, limitations, and ongoing reliability.
Reassess the model when its data, population, methodology, vendor, policy, or operating environment changes. The 2026 interagency guidance says model risk remains even after validation and calls for ongoing monitoring and periodic review. It covers traditional quantitative models and non-generative, non-agentic AI models; generative and agentic AI are outside its scope, though the guidance says its governance and control principles should inform treatment of tools it does not cover.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Make the audit reproducible and monitor production
Keep a versioned record that lets another reviewer understand and repeat the tests. Include the data snapshot, model or code version, configuration, cohort definitions, metrics, thresholds, results, reviewer, and remediation decisions. Re-run critical checks after material changes and on a schedule suited to the risk.
In production, watch for performance drift, data changes, unexpected disparities, unusual error patterns, and trends in overrides or complaints. Define who reviews a signal, what triggers escalation, and what actions are available if the model no longer performs as intended. Monitoring should cover the decision process, not only the model’s headline accuracy.
Choosing audit methods and testing tools
Evaluate methods or tools against the decision you need to audit, rather than treating a software platform as the audit itself.
| What to evaluate | Audit question |
|---|---|
| Decision and real-world outcomes | Does the method test the target financial decision against observed outcomes? |
| Cohorts and cases | Can reviewers examine both group-level patterns and individual decisions? |
| Reproducibility | Can tests, configurations, versions, and results be traced and repeated? |
| Vendor or black-box systems | Can the institution obtain enough evidence to assess performance and fitness for purpose? |
| Privacy and security | Are data access, protection, and residency suitable for the audit? |
| Controls and remediation | Can findings connect to existing model-risk controls, required explanations, and corrective action? |
NIST’s Dioptra is open-source software for AI testing and reproducible workflows, with modular, reusable, traceable experiments. It is not established as an end-to-end banking compliance solution. The NIST AI RMF Playbook offers voluntary actions organized around Govern, Map, Measure, and Manage. Neither resource replaces jurisdiction-specific legal analysis or independent audit judgment; verify Dioptra’s version and security suitability before deployment.
What the audit can establish
A useful audit produces evidence about whether the system is fit for its stated use, where it makes errors, whether outcomes differ in ways that need investigation, whether explanations match decision logic, and which controls or changes are warranted. It does not turn one metric into a universal fairness verdict or, by itself, establish legal compliance. The Federal Reserve, OCC, and FDIC warn that model risk can contribute to financial loss, errors in financial statements and reporting, and flawed financial and risk-management decisions.
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