> ## Documentation Index > Fetch the complete documentation index at: https://docs.dataspike.io/llms.txt > Use this file to discover all available pages before exploring further. # Anatomy of an AML Risk Score > How Dataspike turns a screening hit into a percentage and a band — walked through three worked examples. Every screening hit comes back with a **percentage** and a **band**. Here is how we get from “what we found” to that number — walked through three cases. > 🎯 The band is the decision signal; the percentage tells you how much evidence sits behind it. ## How the score works The **band** — Low, Medium or High — is the decision signal. It drives alerts, and a **High** fails an automated identity check outright. The **percentage** is the detail behind it: how much corroborating evidence sits inside that band. | Band | Range | | ------------- | --------- | | 🟢 **Low** | 0 – 29% | | 🟡 **Medium** | 30 – 69% | | 🔴 **High** | 70 – 100% | **Rule one — the band is the single worst finding.** One sanctions listing makes a case High, and nothing in the record talks it back down. Equally, twenty minor findings stay Low — volume never escalates a band. **Rule two — the percentage moves only inside that band.** Extra findings of the same severity raise the number with sharply diminishing returns, and it is capped so it can never spill into the band above. ## Case 1 · Country risk alone *A clean individual whose only exposure is where he is from.* **John Doe** `Person` `System default profile` One hit, on a national company register — he is listed as a director. No sanctions, no adverse media, no enforcement history. His citizenship is a jurisdiction on the **FATF grey list**. | Finding | Factors | Severity | | --------------------------------------------------------------------------------------- | :-----: | --------- | | Company register entry *(the register itself is a low-risk source)* | 1 | 🟢 Low | | “Company registry” label | 1 | 🟢 Low | | Citizenship — FATF grey list *(default rulebook: grey list = Medium for an individual)* | 1 | 🟡 Medium | | **Total** | **3** | | `Medium 30% · Low 5.5% (2 factors) → 1 − (1 − 0.30) × (1 − 0.055) = 0.34` > 🟡 **34% · Medium** — Nothing in his own history is serious. The grey-listed jurisdiction alone lifts him out of Low, which is exactly what a grey listing is meant to signal. > ⚡ We read the FATF lists live, so a country moving on or off the grey list changes this score on the next screening — no configuration change needed. ## Case 2 · The client’s own country rule *The same clean record, but the client has told us which jurisdictions they will not accept.* **John Doe Inc.** `Company` `Custom profile` A Venezuelan-registered company with one hit, on a national company register. Nothing adverse at all. This client’s rulebook sets Venezuela to **High for companies** and Medium for individuals. | Finding | Factors | Severity | | ---------------------------------------------------------------------------------------------------------- | :-----: | -------- | | Company register entry | 1 | 🟢 Low | | “Company registry” label | 1 | 🟢 Low | | Country — Venezuela *(client rulebook, company rate — a rated country replaces the FATF reading entirely)* | 1 | 🔴 High | | **Total** | **3** | | `High 70% · Low 5.5% (2 factors) → 1 − (1 − 0.70) × (1 − 0.055) = 0.72` > 🔴 **72% · High** — A spotless company scores High purely on the client’s own country policy. Run the identical record as an individual and the same rulebook returns Medium instead. > 🛠️ This is the strongest lever a compliance team has. Country policy is set per client, separately for individuals and companies, and it overrides the FATF default for that country. ## Case 3 · A saturated record *A real entity from our index, screened on the default profile — what the ceiling looks like.* **Roman Arkadyevich Abramovich** `Person` `System default profile` Matched at full-name confidence against 13 lists. Citizenships RU, IL and PT. We never score a record whole — it is broken into individually rateable **risk factors**, and this one yields 37. | Finding | Factors | Severity | | -------------------------------------------------------------------------------------------------------------------- | :-----: | --------- | | Government sanctions listings *(EU, UK, Swiss, Canadian, Australian, NZ, French, Belgian, Monégasque, Ukrainian)* | 12 | 🔴 High | | “Sanctions” label on each of those | 12 | 🔴 High | | UK disqualified directors register | 1 | 🟡 Medium | | “Finance” and “Legal” labels | 9 | 🟢 Low | | Citizenships RU, IL, PT *(Russia partially compliant; Israel and Portugal full members — all Low for an individual)* | 3 | 🟢 Low | | **Total** | **37** | | `High 70% × 24 factors → 103%, clipped to 100% · Medium 30% · Low 6.9% → combined 1.00` > 🔴 **100% · High** — Twelve independent government listings, each High on its own. The score saturates: as unambiguous as a screening result gets. Two things are worth noticing. Twenty-four High factors would have scored **103% on volume alone** — the cap pulls it back to 100%, because the tenth sanctions list cannot count as much as the second. And geography contributed almost nothing here: the default profile sets no country policy of its own, so RU, IL and PT were read straight from FATF and all came back Low. Case 2 is what happens when a client changes that. ## The rulebook behind the numbers None of these severities are hardcoded. Each client has a **risk profile** deciding what counts as serious. The default ships with these positions: | Rulebook entry | Default | | ------------------------------------------------ | ---------------------------------------- | | Sanctions, Terrorism, Criminal labels | 🔴 High | | PEP, Leaks, Debarment, Regulatory warning labels | 🟡 Medium | | Finance, Legal, Media, Company registry labels | 🟢 Low | | FATF blacklist country | 🟡 Medium (person) · 🔴 High (company) | | FATF grey-list country | 🟡 Medium (both) | | Country policy | Not set — FATF reading is used | | Per-list severity | Not set — each list’s own rating is used | > 📌 Profiles are versioned, and every result records which profile and version produced it — so a score from six months ago can always be explained. ## Worth knowing * **Volume never escalates a band.** Fifty Medium findings still read Medium. Deliberate — it stops a noisy record manufacturing a High — but it means the band alone doesn’t convey how much evidence sits behind it. That is the percentage’s job. * **Only strong name matches raise the case.** Every matched record gets its own score, but it only lifts the overall search result if the name matched at high confidence. A High-risk record on a weak name stays visible to the analyst without escalating the case. * **A clean search reports Low, not blank.** Zero matches produces Low rather than an empty result, so “nothing found” and “something minor found” look alike at a glance. Read the hit count alongside the band. * **Labels are read per listing.** A record carries a rolled-up label summary, but only the labels attached to each individual listing are scored. A label that appears in the summary and nowhere on a listing adds nothing. > Cases 1 and 2 are constructed illustrations; case 3 is traced from production scoring logic against entity `46840029-b485-34f6-ae82-280d00becb15` under System Default Profile v1. Percentages are rounded; case 3’s stored value is `1.0`. ## Next steps * [**AML Search**](/aml-screening/search) — run one-time AML searches and read the same score. * [**Daily Monitoring**](/aml-screening/daily-monitoring) — get alerted when a score changes. * [**API Reference**](/api) — all parameters and response formats.