Scenario Studio manufactures the ground truth your monitoring has never had — the patterns that must alert, and the near-misses that must not. Effectiveness becomes a measured number on one scale, whether the control behind it is a rule, a model, or an agent.
Every typology is decomposed from a published source — FinCEN advisories, FATF typologies, RBI master directions — into what a pattern must include, may include and must not include. You review the decomposition, edit it, and adopt it as yours.
Nothing here asks you to rip out what runs today. A composite control stack keeps the rules that work and adds measured components around them — each one held to the same evidence standard, so the AI parts of the stack are never the ones you can't explain.
Coverage is attested. Someone maps transaction codes to typologies on a spreadsheet and signs it.
Coverage is tested. A named scenario either fires or it doesn't, per channel, before you sign anything.
Effectiveness is inferred from alert volume and the alert-to-SAR ratio — a measure of activity, not of what was missed.
Effectiveness is recall against ground truth — the one number a bank normally cannot compute, because you can't count what you never saw.
A new AI component stalls at model risk because there's nothing to evidence it against and no accepted standard to cite.
A new AI component ships with the same recall / precision / determinism / traceability record as the rule sitting next to it.
Independent testing starts from a blank scope and eight to twelve weeks of sampling.
Independent testing starts from measured coverage already in hand — scoped at what's actually weak, and reviewed with a record instead of a memo.
We ship a baseline, then work with your team to make it yours: your schema, your adopted typologies, your thresholds. Each stage below is a real capability, not a slide — and we say plainly which ones are live today, which are running in pilot, and which are the road ahead.
Pick typologies from the library, or bring an advisory and we decompose it with you.
A reviewable must / may / must-not specification for each typology, traceable to its source paragraph.
Nothing to integrate. Runs entirely on synthetic data in a segregated environment.
Map your data schema once, generate a scenario pack in it, run it against your own monitoring.
A dated coverage record for a specific product, channel or config change — before you sign off on it.
One schema mapping, done once. Your environment, your data — nothing leaves it.
Point an agentic or model-based control at the same scenario packs used to test your rules.
The composite record: detection, decision quality, explainability and cost, on one scale as the rules beside it.
A model endpoint we can invoke, and your model risk team's sign-off on the threshold — we measure, you set the pass mark.
Nothing manual — read-only connectivity to a non-production instance, reviewed once.
Coverage that re-runs on a schedule, so a threshold change shows its effect before it reaches production, every time.
Your security review. This is the version that satisfies validation before and after deployment — it belongs past a pilot, not before one.
Polarisk measures. The bank sets the pass mark — because a vendor that also sets the threshold has graded its own homework, and a reviewer will say so.
| Measure | The question it answers | Why a reviewer accepts it |
|---|---|---|
| Recall | What does the control miss? | The Wolfsberg Group names false-negative assessment as an effectiveness measure and cautions against alert-to-SAR ratios alone. Ground truth is what makes it computable at all. |
| Scenario coverage | What was never tested at all? | FFIEC independent testing requires that the systems supporting BSA/AML be shown complete and accurate. This is that requirement, quantified per channel. |
| Decision determinism & reasoning stability | Same input, same decision — and the same reason — on replay? | Published work on LLM-judge auditing finds verdict agreement above 99% coexisting with reasoning stability far lower on some tasks. Reporting only the verdict would flatter the model; both numbers is what makes this an assurance record. |
| Clause attribution | Does the rationale cite the obligation it came from? | Closes the chain from obligation to control: a finding arrives as a paragraph reference, not a bare number. |
Illustrative example Every figure on this page is illustrative example data, labelled as such — a real coverage number belongs to your run, not ours.
Expanded here, static, no clicking required — a reader who studies this one block understands the whole product.
Four ACH credits over five days from three unrelated originators into a nine-week-old account, consolidated out by wire on day six.
Four ACH credits of similar size and timing into the same account profile — but all four originate from one payroll bureau against a registered employer relationship.
Deliberately generating what must not alert is the part incumbent tuning tools structurally can't do — they reason from your own alert history and can't test uncovered space. The interpretation is Polarisk's proposal; the adopted version is yours, versioned and traceable, because accountability for a third-party model does not transfer to the vendor.
Pick a typology. See the specification it becomes, and the scenarios generated from it — the ones that must alert, and the near-misses that must not.
FinCEN advisory, red-flag indicators ¶14–17
Four ACH credits over five days from three unrelated originators into a nine-week-old account, consolidated out by wire on day six.
Ground truth: Satisfies every must-clause plus two may-clauses.
Four ACH credits of the same size and timing into the same account profile — all four from one payroll bureau against a registered employer relationship.
Ground truth: Trips a must-not clause. An alert here is a tuning defect.
We don't hand over a fixed tool and leave. Scenario Studio ships as a working baseline — a typology library, a specification format, a measurement standard — and the first weeks of an engagement are spent making it match your institution, not the other way round.
A product that finds control gaps creates a real question for its buyer at the exact moment it works: who else can see this, and what happens next. Three things answer that up front, by design rather than as talking points.
The first run is explicitly pre-decisional — not an audit, not an independent test. You decide what happens with the result.
Polarisk does not retain findings from your run. We can say so in writing, because that's the first question the second line will ask.
Every finding arrives with the specific scenario or threshold change that closes it — a fix and a finding in the same breath.