AML & Fraud Analytics Path

Ten $9,500 deposits in two weeks.
Same device, three "different" customers.

The SQL patterns AML analysts, fraud strategists, and transaction monitoring investigators actually write — velocity windows, self-joins for counterparty rings, fuzzy entity resolution across customer records, structuring detection, sanctions false-positive tuning, and rule backtests — against a consumer-banking dataset with planted rings, structuring, device reuse, and dormant-then-active patterns. Written for career changers with any bachelor’s degree — AML RightSource, Chainalysis, fintech compliance teams, and AML consultancies all say “SQL + CAMS” and hire on portfolio.

See All Missions

30 missions (10 free · 20 Pro incl. 5 Master) · 5 tables · planted rings, structuring, and device reuse

How It Works

1

Read the briefing

A Slack message from your manager

2

Explore the schema

5 tables in a star schema

3

Write your query

Full SQL editor with autocomplete

4

Get expert feedback

Graduated hints, not just pass/fail

Why This Path

Real AML Typologies

Structuring below the $10K CTR threshold, smurfing across multiple accounts, round-dollar wire cycles, dormant-then-active money mules, and device/IP/email reuse across “separate” customers — every FinCEN / FATF / Wolfsberg pattern maps to a mission.

Velocity + Self-Join SQL

COUNT() OVER with date-range window frames, correlated subqueries for same-counterparty pairs, SOUNDEX / LEVENSHTEIN for entity resolution, and gap-and-island logic for dormant account reactivation.

Rule Tuning + Backtesting

Backtest a proposed threshold against historical alerts, compute rule precision / recall on true-positive labels, and measure false-positive reduction — the analysis every compliance team asks of new hires.

Interview + Job-Ready

Every mission maps to a published AML analyst prompt (Capital One, Chainalysis, Chime, Block, Ramp, Mercury) and the work real investigators do. Pair with CAMS (~$1,400, 3 months) and you have a hiring cheat code.

The Missions

Each mission is a real request from someone at the company. Difficulty increases as you go.

Starter5 missions
1
Meet the customer bookFrom: AML Ops Lead
What channels do we see?From: Chief Compliance Officer
Active customers by stateFrom: AML Ops Lead
Drop the reversalsFrom: Transaction Monitoring Lead
Cash deposits over $10KFrom: BSA Officer
Easy5 missions
Sub-$10K cash deposits by customerFrom: BSA Officer
Daily deposit count per accountFrom: Transaction Monitoring Lead
Dormant accounts that just woke upFrom: Fraud Ops Lead
Customers with no KYC refresh in 3 yearsFrom: Chief Compliance Officer
Wire vs cash — volume by channel groupFrom: AML Ops Lead
Medium5 missions
Round-dollar wires — flag a patternProFrom: Fraud Ops Lead
Velocity — 5+ cash deposits in 24 hoursProFrom: Transaction Monitoring Lead
Structuring candidates — $9K–$9,999 over 14 daysProFrom: BSA Officer
Counterparty ring — same two customers, repeatedProFrom: Fraud Ops Lead
Device reuse across "separate" customersProFrom: Fraud Ops Lead
Hard5 missions
Email reuse — fuzzy entity resolutionProFrom: AML Ops Lead
SOUNDEX-style name dedup across customer_idsProFrom: Chief Compliance Officer
Alert precision — how good is our rule?ProFrom: Model Risk Manager
False positives by rule — tuning targetsProFrom: Model Risk Manager
Mule network — one receives, many sendProFrom: Fraud Ops Lead
Expert5 missions
Structuring ring capstone — everyone in the networkProFrom: BSA Officer
CTR / SAR filing candidate listProFrom: Chief Compliance Officer
Sanctions-list fuzzy match scorecardProFrom: Chief Compliance Officer
Rule-tuning backtest — precision quadrantsProFrom: Model Risk Manager
The investigator's dashboardProFrom: Chief Compliance Officer
Master5 missions
Multi-signal customer risk scoreProFrom: BSA Officer
Cash-intensive customer profileProFrom: Fraud Ops Lead
Rule precision scorecard with rankProFrom: Model Risk Manager
Sanctions fuzzy match scorecardProFrom: Chief Compliance Officer
Investigator capstone — case-load boardProFrom: Chief Compliance Officer

The Database

A consumer-banking AML schema with planted rings, structuring patterns, and device reuse. Sub-$10K cash deposits that stack to CTR-threshold evasion over 14 days, device fingerprints and IPs shared across supposedly separate customers, dormant-then-active money mules, round-dollar wire cycles, and alert dispositions with honest false-positive noise. The exact data shape investigators see on day one.

aml_customers (32)aml_accounts (43)aml_transactions (84)aml_device_sessions (20)aml_alerts (19)aml_sanctions_list (5)

●dimension tables  ● fact tables

The 30+ DPD ticked up — and so did the SARs

Write the velocity rules, the entity-resolution joins, and the rule-tuning backtests that actually clear the alert queue.

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