Dataset S1: AML / MLRO candidates.
The first supply-side dataset flips the problem around: not Brighty's customers, but candidates for their hardest open role. From report №5: Brighty reopened the MLRO / AML Officer role six times — the old channel simply couldn't close it. We traversed the full graph of AML specialists at proven donor companies (the same ones Brighty's team already came from), found a real talent pool, built a two-tier sample, enriched it with the paid API, scored it on AML signals, split it into segments, and packaged it into a ready-to-use dataset.
1,148
in the pool (AML at donors)
804
in the sample (enriched, scored)
264
switch-ready (open to a move)
delivery-grade · prepared 2026-06-28 · MEASURED · Outricher data · supply-side from report №5
01Who we looked for and why
An MLRO is not "just another compliance hire". For a licensed crypto-neobank this is a person personally approved by the regulator, without whom the license does not function. Every month the seat stays open is regulatory risk.
SUPPLY-SIDE S1 · HARDEST OPEN ROLE (MLRO ×6 REOPENINGS)
AML / MLRO candidate
TWO TIERS
AML specialist at a licensed EMI/PI or VASP/CASP, priority on Baltic residency
WhyBrighty is already licensed — you can't "grow into" an MLRO along the way, you need someone who passes fit-and-proper here and now. Past searches ran a narrow keyword channel over overheated names; we traverse the entire graph of AML specialists by title, certification, and licensed-entity employer.
BloodlinesDonors are the same companies Brighty's team already came from. The Revolut→Brighty corridor is proven: 7 people on the current team came from there (report №4). The priority donor for the hardest roles is already mapped.
Donors (set)Revolut · Wise · Paysera · Nexo · Crypto.com · Bitpanda · TransferGo · kevin.
Geo priorityLT / LV / EE (residency in the license jurisdiction) → PL → rest of the graph
02How many we found in total
The pool matches a strict AML/MLRO title dictionary at the named donors, keyed on a verified company ID (not on an ambiguous domain name).
1,148
current AML specialists at 8 donors
197
of them in the Baltics + Poland
1,024
switch candidates (left a donor, AML 2021+)
| Donor | AML specialists | In sample | Channel |
|---|
| Revolut | 910 | 592 | ★ warm (corridor proven) |
| Crypto.com | 61 | 54 | direct |
| Bitpanda | 50 | 37 | direct |
| TransferGo | 47 | 43 | direct |
| Paysera | 50 | 50 | direct (Baltic, taken in full) |
| Nexo | 27 | 20 | direct |
| kevin. | 8 | 8 | direct (in full) |
| Total | 1,148 | 804 | |
Why Revolut dominates. It's the largest AML factory among the donors (910 of 1,148) and at the same time the most valuable channel — the Revolut→Brighty corridor has already worked. So it's wider in the sample, while the smaller donors (Crypto.com, Bitpanda, Paysera, kevin.) are taken almost in full. Under the strict AML dictionary, Wise returned nothing on its canonical page — reported honestly.
03How we built the sample
804 people, two streams: current AML at donors + switch candidates who are open to a move.
- Current AML specialists at donors (540) — a quota per donor proportional to the size of its AML team; smaller donors taken in full.
- Switch candidates (264) — anyone who held an AML role at a donor in 2021+ but is now at a different company: ready to move right now. Baltic ones prioritized.
- Deduplication by profile: 929 rows → 804 unique people.
- Full canonical field set from the first query — compatible with enrichment and packaging.
Two candidate tiers. Senior (154) — sitting MLROs / Heads of AML / Deputies: proven, low onboarding risk, regulator-ready. Promising Middle (650) — AML analysts with headroom: cost-effective, fast-growing, high ceiling, not yet in big-tech.
04How we enriched it
A free layer from the database + a paid API top-up for full career history. Email coverage is lower for this audience for an objective reason — and we say so plainly.
467
completed via paid API (full career)
83
fields on average per person (max 111)
29%
with email (236 people)
On email — honestly. Email coverage is 29%, below the typical rate. This is a property of the audience: compliance/AML specialists professionally minimize their public contact details. Where an email exists, it's real, from our own sources; we never fabricate contacts (that's an iron rule). The with-email and switch-ready segments concentrate the layer reachable right now. Every profile carries a full career picture regardless of whether an email is present.
Fit-and-proper. The status "approved by the regulator as the responsible person" is a fact from public registers (Lietuvos bankas / Latvijas Banka), not a graph field. We find the candidate by licensed-entity employer and title; confirming it against the register is a separate, quick step.
05How we scored
The s1_score (0–22) and an A+/A/B/C label. The formula follows the AML-ICP from report №5.
| Signal | Points | Logic |
|---|
| Title: MLRO / Head of AML · Deputy / Manager / Lead | +6 · +4 | seniority and role weight |
| Title: Officer / Specialist · Analyst | +3 · +2 | mid tier (headroom) |
| Certification ACAMS / CAMS / ICA | +3 | relevant qualification (booster, not a gate) |
| Skills: MiCA / Travel Rule / SAR-STR / sanctions / transaction monitoring | +1 each (up to +4) | real AML expertise in the text |
| Geo: Baltics (LT/LV/EE) · PL | +3 · +2 | residency in the license jurisdiction |
| Revolut bloodline · switch-ready | +2 · +2 | proven corridor · readiness to move |
| Has email · phone · career depth (5+ positions) | +5 · +1 · +1 | reachable + verifiable history |
143
A+ — contact immediately (≥14)
318
A — high priority (≥10)
279 · 64
B · C — nurture · watchlist
461 "hot" (A+/A) — candidates with role weight, AML expertise, and/or a direct contact. Outreach starts with them.
06Segments
Ready-made slices — each a separate file in the dataset, sorted by score.
| Segment | People | Purpose |
|---|
| hot-a-plus (A+/A) | 461 | ★ start here — weight + expertise + contact |
| senior-mlro | 154 | regulator-ready leaders (MLRO/Head/Deputy) |
| promising-middle | 650 | cost-effective analysts with headroom |
| revolut-bloodline | 592 | proven Revolut→Brighty corridor |
| baltic-resident (LT/LV/EE) | 130 | residents of the license jurisdiction (fit-and-proper) |
| poland-secondary | 139 | strong secondary CEE pool |
| certified-acams-ica | 38 | relevant certification |
| switch-ready | 264 | left a donor 2021+ — open to a move |
| with-email | 236 | reachable by email now |
Where they are
| Country | People | Read |
|---|
| India | 211 | large Revolut FinCrime operations hub |
| Poland · Lithuania | 139 · 126 | ★ Baltic AML market — priority for the license |
| Portugal · United Kingdom | 58 · 54 | donor tech/ops hubs |
| Bulgaria · United States | 44 · 28 | distributed compliance teams |
| other countries | ~140 | AT, ES, DE, AE, IE… |
License priority — the Baltics. LT 126 + PL 139 + the Latvian-Estonian layer = the core for fit-and-proper. The India layer (211) is a valuable, cost-effective "Promising Middle" for Deputy/AML Officer roles, but not for a named-MLRO under a Baltic license (the regulator requires a local resident).
07Who's at the top (preview)
Top rows by score — hot A+. The full list is in the dataset.
| Role | Donor | Geo | Tier | Score |
|---|
| FinCrime Monitoring Manager | Revolut | PL | senior | 20 |
| Financial Crime Compliance TM SME | Revolut | PL | middle | 19 |
| Head of FinCrime Ops Europe | Revolut | PL | senior | 18 |
| Senior FinCrime Compliance Manager | Revolut | JP | middle | 18 |
| MLRO (Netherlands) | Revolut | NL | senior | 18 |
★ Showing 5 of 461 hot candidates. The full list with tier, segments, and contact is in the dataset (XLSX/CSV/JSONL).
★Get the dataset
All 804 candidates, in every format, with scoring, tiers, and segments.
Request this dataset: Request this dataset ↗ — inside:
•
leads.xlsx — Excel with every field across all 804 candidates
•
leads.csv / leads.jsonl / leads.json — the same data for CRM and programmatic use
•
segments/ — 9 ready slices (hot-a-plus, senior-mlro, promising-middle, revolut-bloodline, baltic-resident, certified, switch-ready, with-email)
•
README.md — description of fields, scoring, tiers, and coverage
- Each row is a candidate: name, role, donor, location, career, certifications, skills, contact sources, the
s1_score, the tier, and the lead_label.
- The
switch_candidate field flags people open to a move; geo_priority flags priority for the license.
- Data is delivered as a secured dataset; this is the analytical report that accompanies it.
08What this dataset proves
01
Hard-to-fill role → real pool — 1,148 AML specialists
Every current AML at proven donors + 1,024 switch candidates. The narrow keyword channel replaced by a full-graph traversal. MEASURED
02
Pool → two tiers — 804: 154 senior + 650 middle
Balanced, enriched via the paid API, scored on AML signals, sliced into 9 segments. ENRICHED
03
Tiers → ready-to-use file — 461 hot + 264 switch-ready
A ready-to-use dataset in every format and slice. From an "unfillable role" to a "list of candidates to reach tomorrow". ACTIONABLE
Next supply-side roles. Further down report №5 — CISO (InfoSec), CFO / Chief Accountant (finance), UX Lead, Customer Support, Data Analyst. We run them in order with the same method.
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