For validation & audit firms

Your constraint isn't demand. It's reviewer hours.

Every ALM validation starts the same way: chase the document request, read a corpus you've read a hundred variations of, transcribe the same forty figures into the same workpaper template. VetALM puts an AI reader on that pass, with every extracted figure cited back to its source. Your senior people start where the judgment begins.

Request a demo See the economics

Multi-tenant by engagement: one workspace per client
Your reviewers approve every finding
Workpapers export to Word, PDF, and Excel

The Documents tab of a VetALM engagement, showing expected document slots grouped by category with collected or missing status.

The economics

The same team, more engagements.

Validation work is priced by scope and delivered by hours. Most of those hours go to evidence handling rather than analysis: reading, transcribing, cross-referencing, formatting. That's the portion VetALM's AI absorbs.

40–80h → 10–25hTarget effort per engagement

44Documents on the request list, tracked whether or not they arrive

12Deterministic rules, plus an AI pass

100%Findings traceable to source evidence

Effort figures are targets for a typical community-bank IRR validation, not a guarantee. Scope, corpus quality, and your own review depth all move the number.

Where the hours go back

Four places a validation loses time.

Chasing the document request

The PBC list lives in someone's spreadsheet and the follow-up lives in email. VetALM makes the request list part of the engagement: forty-four expected documents across seven categories, each slot showing collected or missing, so the gap list is always current and always shareable. AI classifies each upload into the slot it belongs in as it arrives. Few clients can fill every slot, and they aren't meant to. What the list gives you is an accurate picture of what you have to work with, early enough to scope around it.

Transcribing the same forty figures

Betas, decay rates, average lives, EVE and NII by scenario, policy limits, approval dates. A language model pulls each one out with a confidence score and a locator to the exact cell or paragraph. The workpaper tie-out becomes a review rather than a re-typing.

Reading documents against each other

The deposit study says one beta, the assumption register says another, the config export says a third, and nobody documented which one won. That contradiction only appears if someone holds all three in their head at once. A language model reads across the whole corpus and holds all ten, and it has to cite the passages behind any contradiction it raises.

Benchmarking, honestly

Guidance asks for comparison to an independent alternative. Building a second ALM model per client isn't billable, so the step often thins to peer data. VetALM ships its own engine and produces EVE and NII across the shock set from disclosed rollups.

The differentiator

Benchmarking you can actually put in the report.

This is the section of a validation that's hardest to do well and easiest for an examiner to probe. It's also the one your competitors are least likely to have automated.

An independent model, per engagement, in minutes.

AI lifts the account-category rollups that ALM report appendices already disclose. From there it is ordinary financial mathematics, not AI: the challenger projects monthly cash flows and values them on a shocked curve. You get economic value of equity and twelve-month net interest income for every parallel shock, side by side with what the institution reported.

Seeded from a template scaled to the engagement's total assets, then overwritten with the institution's actual figures
Variance past your tolerance raises a finding automatically
A disagreement on the direction of exposure escalates to high severity regardless of magnitude
The benchmark table and its method note render straight into the workpaper

7Parallel shocks, ±100 to ±400 bps

EVE + NIIBoth benchmarked per scenario

Append-onlyRuns snapshot their inputs and stay reproducible

The challenger model workbench comparing challenger EVE and NII against reported results for each rate shock, with variances and direction disagreements flagged. — Reported NII improving while the challenger shows it falling: a sign flip, escalated on sight.

Fitting your practice

It slots into how your firm already works.

One workspace per client, isolated by construction.

Each engagement scopes a single institution: its documents, extractions, findings, benchmark, and audit trail. Every record carries a tenant and every query is tenant-scoped in the service layer, so one client's corpus cannot surface in another's engagement.

Roles map to how review actually runs: engagement lead, senior reviewer, reviewer, read-only
Who may approve a finding or sign off is a server-side capability check
Sign-off seals the engagement's audit trail

The audit trail: timestamped actions with acting entity and before and after content hashes. — Attributable, time-stamped, append-only: the record if a conclusion is ever questioned.

Deliverables in the formats you already send.

An executive summary, the validation workpaper, and an issues register, in Word, PDF, and Excel. A language model drafts the prose, but generation runs only on findings a reviewer has approved, so nothing AI-drafted reaches a client unread.

Edit findings in place before generation; edits are recorded, not silent
Rejections require a reason, which becomes part of the record
Regenerate at any time, and the trail records that too

The Outputs tab listing a generated issues register, validation workpaper, and executive summary in Word, PDF, and Excel.

Questions firms ask

Independence, defensibility, and what you sign.

Does using AI compromise our independence?

Independence is about who forms the conclusion, and that stays entirely with your reviewers. VetALM is a tool your firm operates, much like the model you'd build in Excel to benchmark a client, except you don't rebuild it each engagement. The AI reads, extracts, and drafts; it never concludes. No conclusion is reached, and no deliverable is produced, without a named reviewer approving it.

What do we tell an examiner or a peer reviewer about the tooling?

That every finding cites the source it came from, that the twelve detection rules are deterministic comparison logic rather than model output, with AI supplying only the cited figures those rules compare, that the checks are individually documented and testable, that the detection engine is scored against a golden dataset before each release, and that the engagement's audit trail records who approved what and when. Those are the questions that get asked, and the platform is designed to answer them with records rather than assurances.

Our clients' documents are confidential. How is that handled?

Documents are encrypted in transit and at rest, scoped to a single tenant and engagement, and never used to train a model. The AI provider is configured for zero data retention, so content isn't kept after a response returns. Access is by engagement membership, enforced server-side.

Can we keep our own workpaper template and methodology?

The generated workpaper follows a structure built around the evidence and findings, and the findings themselves are yours to edit before generation. If your template differs materially, that's worth a conversation during a pilot. The output layer is the most adaptable part of the system.

What does a pilot look like?

A demo on a sample bank dataset first, about thirty minutes. If the fit looks right, a pilot on one live engagement of your choosing, run alongside your normal process so you can compare the output to what your team would have produced.

Run one engagement through it.

We'll start with a walkthrough on a sample bank dataset, then talk about a pilot on a live engagement of yours, run in parallel with your normal process so the comparison is real.

30-minute guided walkthrough, no slideware
Tailored to the documents your clients actually send
We review reported outputs, with no model re-computation required