For financial institutions
A finding raised by your validator becomes a remediation item, a board conversation, and a line in next year's exam. The same condition, found three months earlier by your own team, is just a task. VetALM puts an AI reader on your documentation and runs the validator's evidence pass on your schedule.
Validation readiness
None of these are exotic. They're the ordinary consequences of a model that's been running for a few years while the people and priorities around it changed. They are also what an independent reviewer is trained to look for first, and what VetALM's AI reader is pointed at across your whole document set.
A deposit study from 2022 still driving betas and decay rates in 2026, with a policy that requires a refresh every twenty-four months. AI pulls both dates out of the documents. A deterministic rule compares them. The result is unarguable and high severity, and it was trivially avoidable if someone had checked.
An assumption set to something other than what the study recommended, because someone had a reason at the time. A language model reads across the study, the config, and the committee minutes looking for that reason. If it isn't written down anywhere, the validator can only record that it isn't.
The figure in the ALCO package doesn't tie to the model output it came from. AI extracts both numbers with a citation to the exact cell or paragraph, and the comparison is arithmetic from there. Usually it's a version mismatch, occasionally something worse. Either way it's a governance finding.
The policy states limits, the model doesn't have them loaded, and nothing is breaching because nothing is checking. A control gap rather than a risk result.
A prior-period item marked for remediation that the current evidence shows unchanged. Repeat findings escalate. They read as a remediation-tracking failure, not just a technical one.
Forty-four documents a thorough validation expects. AI classifies what you upload against that list, so you see the gaps without sorting the folder yourself. Knowing which ones you'd struggle to hand over is a useful morning's work, and the checklist is the first screen of the product.
Independent benchmark
Your vendor's model produces a number. Your validator will ask whether an independent model produces a similar one. You can know the answer before they ask.
AI reads the account-category rollups out of your own ALM report appendix. From there it is ordinary financial mathematics, not AI: the challenger engine independently produces economic value of equity and twelve-month net interest income across the parallel shock set, then puts its answer next to yours.
A divergence isn't automatically an error in your model. It can just as easily point at an assumption worth documenting. Knowing which, before someone else asks, is the point.
When to use it
Run it sixty to ninety days before your validator starts. Close what you can, document what you can't, and walk into the kickoff knowing what they'll find.
Interest-rate risk and model governance are standing exam topics. An evidence-backed self-assessment, with citations, is a better answer than a binder.
A new version, a re-parameterization, or a conversion is where assumptions quietly drift from their documentation. Re-run and compare against the prior engagement's record.
Questions institutions ask
No, and we'd steer you away from anyone who says otherwise. Independence requires a party outside the model's ownership and use, which a tool your own team runs cannot supply. That is true no matter how good the AI is. VetALM is for self-assessment and readiness. If your validator chooses to use it as well, that's their engagement and their sign-off.
No. AI reads the documents you already produce: policy, shock reports, deposit studies, assumption documentation, config exports, board packages, and back-testing evidence. Nothing connects to your model or core.
That's information, not a verdict. Two models built on different conventions will differ, and the useful question is whether the gap is explainable. The workbench lets you flex betas, decay rates, and the curve to see which assumption drives it. That is usually the conversation worth having with your vendor anyway.
It's the normal case, and it's partly the point. The document checklist will show you exactly which of the forty-four expected items you can't currently produce, which is often the most actionable output of a first run.
AI does the reading. It classifies each document, extracts every figure with a citation to the cell or paragraph it came from, reads across the full corpus for contradictions no single rule anticipates, and drafts the workpaper prose. Deterministic code does everything else: the twelve detection rules are fixed comparison logic, the challenger model is ordinary financial mathematics, and the audit trail is recorded, not generated. Findings from the language model must cite their evidence or stay quiet, and every finding requires a human reviewer's sign-off before it appears in a deliverable.
Typically model risk or the ALCO/treasury function, with internal audit as a reader. Roles are enforced server-side, so read-only access for the people who need visibility without edit rights is straightforward.
We'll walk through a complete engagement on a sample bank dataset: the document checklist, the AI-extracted assumptions, the challenger benchmark, and the findings with their citations. Then we'll talk about running it on yours.
