# Building a system that documents, validates and monitors models, and drafts the artefacts examiners read inside a bank

Source: https://www.bankingnewsai.com/agentic-banking/build/model-risk
Last updated: Sep 17, 2026

Model risk teams are a natural early adopter because their output is documents: development records, validation reports, monitoring summaries and the inventory itself. A chained workflow that drafts each artefact from the underlying evidence and checks it against the standard the team already applies removes weeks of writing. The line to hold is that the language model drafts and checks; the validator's judgment and signature remain the validator's.

## The brief (default answers)

| Choice | Answer |
| --- | --- |
| Who is affected | Staff only |
| Reversibility | Fully |
| Stakes | Material |
| Knowledge | Stable documents |
| Verifiability | By judgment |
| Steps | Known |
| Jurisdiction | United States |
| Delivery route | Provider API |

- **Pattern:** Workflow — Several model calls orchestrated by your code, in a sequence or a graph you designed.
- **Tier:** 3, Tier 3: act within an envelope — Low stakes, reversible, internal. The model may act within a defined envelope, with sampling, monitoring and a kill switch instead of per-action review.
- **Workflow shapes:** Prompt chaining, Evaluator and optimizer
- **Human involvement:** The model may act within a defined envelope; people sample, monitor and can stop it.
- **Knowledge:** Retrieval over the governed document set, with a citation on every answer and a refusal when nothing relevant is found.

## Decomposition

| Step | Owner | Note |
| --- | --- | --- |
| Assemble the evidence | system | Model code, data lineage, test results from the model repository. |
| Draft the validation report sections | model | Conceptual soundness, monitoring, outcomes analysis, each cited. |
| Check against the validation standard | model | An evaluator pass against the bank's own checklist. |
| Validate and sign | human | Effective challenge is a person's job. |
| Update the inventory | system | Structured fields written by code from the signed report. |

## Control layer weights (0–4)

| Layer | Weight |
| --- | --- |
| governance | 1 |
| identity | 0.55 |
| actions | 0.4 |
| data | 1 |
| models | 0.55 |
| runtime | 0.8 |
| observability | 0.7 |
| oversight | 0.6 |

## Documents that apply

- [SR 26-2](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-26-2) — The 2026 US framework: narrower model definition, materiality, generative and agentic AI carved out.
- [OCC Bulletin 2026-13](https://www.bankingnewsai.com/ai-regulation/documents/occ-bulletin-2026-13) — Same text for national banks; the promised interagency RFI on AI.
- [PRA SS1/23](https://www.bankingnewsai.com/ai-regulation/documents/pra-ss1-23-model-risk-management) — UK: AI and machine learning stay inside model risk management.
- [ECB Guide to internal models (July 2025, ML section)](https://www.bankingnewsai.com/ai-regulation/documents/ecb-guide-to-internal-models-2025-machine-learning) — EU capital models: explainability and justified complexity.
- [SR 23-4](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-23-4) — US third-party risk management, including the model provider.
- [SB 26-189](https://www.bankingnewsai.com/ai-regulation/documents/co-sb26-189) — Colorado: notice, explanation and human review for consequential automated decisions from Jan 1, 2027.
- [FSB AI monitoring report (Oct 2025)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-monitoring-ai-adoption-vulnerabilities-2025) — Concentration on a small number of model suppliers.
- [NIST AI RMF 1.0](https://www.bankingnewsai.com/ai-regulation/documents/nist-ai-100-1) — The voluntary Govern, Map, Measure, Manage frame for everything model-risk guidance leaves out.

## Evals

- A golden dataset of at least 50 real cases with expected outputs, including adversarial inputs: wrong documents, unusual formats, prompts that try to change the task.
- A judge model scoring against a written rubric (accuracy, completeness, tone, citation present), calibrated against a human-scored sample every month. Threshold set from the human sample, not guessed.
- Citation checks: every factual claim resolves to a passage in the governed set; unsupported claims below 2% of answers.
- Trace evals per step, not only end to end: which step fails, how often, at what cost, so a prompt or model change can be judged step by step.
- The same suite reruns on every prompt change, model version change and retrieval change; a regression blocks the release. That is what ongoing monitoring and outcomes analysis mean in model-risk terms.

## Human gates

- An action envelope: which tools, which systems, what value, what volume, what the model may never do.
- Sampled human review of outputs and a weekly look at the exception log.
- Turn and cost budgets, logged, with an automatic stop when exceeded.
- A kill switch that any owner can pull.

## What an examiner will ask

- Where is this system in your inventory, what tier did you assign, and who signed it off?
- What counts as a model here, and what does your validation cover for the parts that are not?
- Show me the data lineage behind the retrieval set and the training or tuning data.
- What can the system do without a person, and where is that written down?
- How do you know it is still working: which evals run, how often, and what happened the last time one failed?
- What did you do about the vendor: due diligence, contract, exit plan, concentration?
- Walk me through one wrong output from production and what the customer, if any, saw.
- Who can switch it off, and has that been tested?

## What the board should hear

- What it does: documents, validates and monitors models, and drafts the artefacts examiners read; the model may act within a defined envelope; people sample, monitor and can stop it.
- Pattern: workflow (prompt chaining, evaluator and optimizer); tier 3: act within an envelope.
- Rules it answers to: 4 documents across US, each linked in the brief.
- How we know it works: a golden dataset, automated checks on every release, and human review at the level the tier demands.
- What could go wrong and who answers: the accountable owner, the kill switch, the escalation route.

## Pitfalls

- Drafts that read as validation without the tests having run.
- Using the tool on generative systems and calling the result model validation; the 2026 guidance says it is not.
- No version control on the prompts that produce examinable documents.

Change any answer on the interactive map: https://www.bankingnewsai.com/agentic-banking/build/model-risk

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Part of [BankingNewsAI](https://www.bankingnewsai.com/) — a free daily brief on AI in banking, an AI regulation tracker (19 authorities, 166 documents) and AI-strategy profiles of the 100 largest US banks. Markdown versions of every reference page: append `.md` to the page URL; index at https://www.bankingnewsai.com/llms.txt.