A bank can have model outputs, a methodology document, and an approaching validation date, yet still have substantial work to do before an independent reviewer can effectively assess the model.
The model owner holds one part of the story. Technology holds another. Business users understand how the outputs influence decisions, while risk teams maintain their own assessments. Testing results may be stored in separate files with little explanation of what was tested, why it mattered, or what the results mean.
In my experience, bringing these pieces together is a core part of model risk management. It often raises questions that should have been addressed earlier: Does the testing reflect the model's actual use? Are its limitations understood? Can a reviewer follow the reasoning from the business purpose through the methodology and data to the results?
A complete package makes that reasoning accessible and also makes unresolved questions visible, so the bank can address them before they become avoidable obstacles during validation.
Before assembling documents or scheduling additional runs, clarify the model's intended purpose, who uses it, and which decisions depend on its outputs. A model supporting trading decisions may raise different questions than one supporting credit decisions, even when parts of the underlying mathematics are similar.
Agree on the model version and the uses in scope, the relevant data, the conditions under which performance matters, and the consequences of an unreliable result. Identify changes since the last review and uses that extend beyond the original design.
This gives the testing plan a purpose. Historical performance, sensitivity analysis, benchmarking, and stress testing can each help answer specific questions, as appropriate. Selecting tests because they are familiar, without linking them to the model's use and risk, can leave the most consequential questions unanswered.
The revised supervisory guidance associated with SR 26-2 takes a risk-based approach and explicitly avoids prescriptive standards. The practical task is to develop evidence appropriate to the model and its use, with a clear rationale for the scope and depth of the work. This guidance is from the Federal Reserve model risk guidance.
Producing a large volume of output does not establish that a model performs adequately. The package needs to explain the question behind each test, the data and configuration used, the basis for interpreting the results, and the implications of any identified weaknesses.
Where practical, establish assessment criteria before evaluating results. If a threshold requires judgment, explain the judgment. When a result falls outside expectations, investigate the cause and document what it means for the model's use. An unexplained exception should not disappear into an appendix.
This is where we see an opportunity to do much more than improve documentation. AntePartners helps define the agreed-upon testing work, analyze results, investigate gaps, and prepare supporting evidence. When the bank or its provider executes model runs, responsibilities for supplying data, configurations, and outputs need to be explicit.
The resulting documentation should reflect the work that has actually been performed. If testing reveals a weakness, the package should explain the proposed response, any interim restrictions or controls, and the work still required.
A reviewer should be able to move from a question to the supporting evidence without reconstructing the model's history from email threads and unrelated folders.
We organize documentation so the business purpose, methodology, data, testing, findings, and limitations can be reviewed together. An evidence index links relevant regulatory guidance and the bank's standards to the supporting analysis. It also identifies gaps and responsible owners.
The following is a simplified illustration of that structure. It is neither a client example nor a universal regulatory checklist.
| Review question | Supporting evidence | What the Index records |
|---|---|---|
| Is the model appropriate for its intended use? | Use statement, methodology, assumptions, and rationale for key choices | Relevant guidance or bank standard; exact document section; unresolved questions |
| Are the inputs suitable? | Data sources, transformations, quality checks, and known gaps | Data version; evidence location; accountable owner |
| How does performance change under relevant conditions? | Agreed test plan, configurations, results, and analysis | Test purpose; assessment criteria; exceptions and follow-up |
| How will limitations affect decisions? | Limitations assessment, use restrictions, controls, and remediation actions | Business impact; action owner; review date; open status |
Completeness is assessed against the agreed scope. Every mapped area needs substantive support or an explicit gap. A reference to a policy does not, by itself, demonstrate that the model was tested or that a control operates.
A coherent package can include controlled links to supporting records rather than placing every file in a single document. What matters is that the reviewer can locate the correct version and follow the evidence without losing the connection between the analysis and its conclusions.
The model owner cannot answer every question alone. Business users need to explain how they rely on the output. Technology and data teams need to establish how inputs and configurations are controlled. Risk teams need to assess the implications of weaknesses and the adequacy of the response.
Bring these groups together early enough to resolve inconsistencies. If the business uses a model in a way that differs from its methodology, that is a substantive issue to investigate. If a test cannot be reproduced because the configuration was not retained, assigning someone to rewrite the report will not resolve the issue.
Assign each outstanding item an owner, a specific deliverable, and a near-term review point. Measure progress by completed analyses and resolved questions, with supporting evidence available for review. A final document deadline alone offers little warning that essential work is slipping.
Good preparation gives independent validators a stronger basis for assessing conceptual soundness, performance, limitations, and suitability for use. It does not determine their conclusions or eliminate the need for their own challenge.
Agree early on submission expectations and practical evidence requirements while preserving the validator's independence. Keep responsibilities clear when a firm supports development, testing, or documentation. Any separate validation role requires appropriate independence and conflict assessment.
The bank should expect questions. A well-prepared package makes those questions more productive because the underlying facts, analysis, and unresolved issues are already clear.
If validation is approaching, begin with a focused assessment of the model's intended use, available testing, documentation, and open issues. Establish what can already be supported, what requires further investigation, and who will complete the work.
That assessment should lead to a practical work plan with measurable deliverables. It should also establish how the package will remain current as the model, its data, or its use evolves.
At AntePartners, we combine hands-on model risk management with documentation structured for review. Our aim is to help the bank better understand the model and provide independent reviewers with a complete, traceable basis for their assessment.
If a model is approaching validation and your team is still compiling the evidence, contact AntePartners about the testing, analysis, and documentation needed to support the review.
