Supervisory Guidance on Model Risk Management¶
Board of Governors of the Federal Reserve System, & Office of the Comptroller of the Currency. (2011). Supervisory Guidance on Model Risk Management.
Retired. This is not a bibliographic entry. Our extractor captured an inline prose definition from an article and stored it as a work. The concept is real; this registry record is a parsing defect. This entry is kept so the citations that pointed at it still resolve, and so the correction is visible rather than silent.
Cited by¶
9 citations across 9 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Out Of Distribution Detection
- Statistics and survey research: explicit refusal to generalise beyond the sampled population or outside the convex hull of training data. Finance: credit models that decline to score applicants from populations they were not trained on, rather than producing scores that look like predictions.
This sourceIdentifies applying a model outside its intended scope or population as a primary source of model risk and requires institutions to understand model limitations and assumptions — the model-risk-management basis for credit/risk models declining to score applicants outside their development population rather than emitting out-of-scope scores.
- Statistics and survey research: explicit refusal to generalise beyond the sampled population or outside the convex hull of training data. Finance: credit models that decline to score applicants from populations they were not trained on, rather than producing scores that look like predictions.
Mechanisms¶
- Adverse Selection Pool Segmentation
- The discipline that guards against this is validating proxies against realized outcomes, constraining the permitted factors, and watching for drift rather than trusting a one-time fit.
This sourceRequires model validation against realized outcomes and ongoing monitoring to detect whether a model remains accurate, reliable, and fit for purpose.
- The discipline that guards against this is validating proxies against realized outcomes, constraining the permitted factors, and watching for drift rather than trusting a one-time fit.
- Independent Assumption-Challenge Gate
- Its strength is that it breaks the optimism of self-assessment and gives "no" an enforceable form — the discipline banking supervisors call effective challenge: critical review by parties who are competent, independent, and have the influence to compel change.
This sourceDefines effective challenge as critical analysis by objective, informed parties whose competence and influence can secure corrective action.
- Its strength is that it breaks the optimism of self-assessment and gives "no" an enforceable form — the discipline banking supervisors call effective challenge: critical review by parties who are competent, independent, and have the influence to compel change.
- Linear Programming Solver
- Its failure mode is solver theater: a sophisticated engine returns a precise answer from a weak objective, bad data, or illegitimate constraints, and the crispness of the output lends false authority to garbage inputs.
This sourceIdentifies incorrect or poor-quality input data as a source of inaccurate model output.
- Its failure mode is solver theater: a sophisticated engine returns a precise answer from a weak objective, bad data, or illegitimate constraints, and the crispness of the output lends false authority to garbage inputs.
- Model-Failure Red Team
- This is the "effective challenge" that model-risk governance calls for.
This sourceDefines effective challenge as objective, informed critical analysis that identifies model assumptions and limitations and produces appropriate changes.
- This is the "effective challenge" that model-risk governance calls for.
- Model Registry
- Its strength is that it turns a sprawling, silently-changing population of models into something traceable, accountable, and reversible — the substrate that model-risk governance (in banking, the discipline codified by supervisory guidance such as the Federal Reserve's SR 11-7
This sourceRequires a comprehensive, responsible firm-wide model inventory that supports traceability, accountability, and aggregate model-risk management.
- Its strength is that it turns a sprawling, silently-changing population of models into something traceable, accountable, and reversible — the substrate that model-risk governance (in banking, the discipline codified by supervisory guidance such as the Federal Reserve's SR 11-7
- Model Simplification Audit
- Its strength is that it finds the biases a headline accuracy number hides — the subgroup harm, the out-of-domain drift — and, by binding each to a fix, it turns a model critique into governance rather than a list of caveats.
This sourceFederal Reserve/OCC guidance calls for monitoring changes and out-of-scope use, responding to identified problems, and integrating validation into model-risk governance.
- Its strength is that it finds the biases a headline accuracy number hides — the subgroup harm, the out-of-domain drift — and, by binding each to a fix, it turns a model critique into governance rather than a list of caveats.
- Risk Score Threshold Recalibration
- Crucially, the middle manual-review band is preserved and its width tuned, so underwriters still see borderline cases — and an override channel lets them reverse an edge decision
This sourceRecognizes expert-judgment overrides in which model users ignore, alter, or reverse a model output.
- Crucially, the middle manual-review band is preserved and its width tuned, so underwriters still see borderline cases — and an override channel lets them reverse an edge decision
- Threshold Versioning Register
- The register didn't choose or ship 0.82 — but it is why the choice is now defensible and, if it goes wrong, cleanly reversible.
This sourceRequires documented evidence for model choices and records of the information used to select and validate a model.
- The register didn't choose or ship 0.82 — but it is why the choice is now defensible and, if it goes wrong, cleanly reversible.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. Neither this nor any other of the 9 citations of this work carries a recorded support check.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 5 other ways.
- https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107a1.pdf ×5
- https://www.federalreserve.gov/bankinforeg/srletters/sr1107a1.pdf ×1
- https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm ×1
- https://www.federalreserve.gov/supervisionreg/srletters/sr1107.pdf ×1
- https://www.federalreserve.gov/supervisionreg/srletters/sr1107a1.pdf ×1
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