AML Judgment Gap Report · Full findings
Gambling and Gaming
This is a specimen. The cases, the weights, the methods and the sources are real, and are what your team would sit and what your own report would cite. The scores are invented, for a hypothetical cohort of 14 people. A real report carries your own results and nothing else.
Your team detects 5 of the 9 money laundering methods that apply to this sector. 1 need action.
One of these is urgent. Your team was tested on it and missed it.
Your assessment is 6 cases, not one. Each covers a different part of your exposure, and the appendix shows how many of your 9 methods each one carries. Measured against the methods that apply to this sector, not against other firms.
Since the last sitting
September cohort against March cohort. Your team detected 2 methods then and 5 now. 2 went backwards.
What to do
For each one, ask a single question. Does the team not know the method, or do they know it and have no rule that would surface it? The first needs a briefing. The second is a control gap, and it is the more serious answer.
- High
- Tested and missed, on a case covering a large part of your exposure.
- Medium
- Tested and missed on a smaller case, or never tested on one that matters.
- Low
- Never tested, and a small part of your exposure.
Ordered by priority. The reason for each is printed with it, so you can disagree with the ranking.
- 01
Prepaid Cards, Gift Cards and Stored Value
HighYour team was tested on this and missed it. It sits in The Betting Account, which covers 7 of the 9 methods that apply to you.
- Result
- Caught 45% of the laundering customer's alerts.
- Tested by
- The Betting Account — covers 7 of your 9 methods
Now · BriefSend the team the register entry. It has the mechanism, the signal and the sources.
30 days · ControlWhich rule, report or alert would surface this?
Bulk purchases of stored-value products at retail, card-load patterns inconsistent with any consumer use, and programme manager settlement accounts with volumes exceeding the plausible cardholder base.
90 days · Re-testSit The Betting Account again, on the version the team has not seen.
OwnerTarget date
On your own report
Each line carries your team’s result, an owner and a date. The re-test at 90 days shows what has moved.
Appendix
Evidence
Everything the finding above rests on, for anyone who wants to check it.
By case
The 6 cases that make up your assessment. Detection rate is the share of the laundering customer’s alerts the team caught. The last column counts people who escalated everything, which catches the laundering customer without deciding anything and scores badly for that reason.
| Case | Your methods | Share of assessment | Runs | Median mark | Detection rate | Escalated everything |
|---|---|---|---|---|---|---|
| The Betting Account | 7 | 41% | 14 | 49 | 45% | 3 of 14 |
| The Verification Desk | 5 | 29% | 14 | 87 | 84% | 3 of 14 |
| The Collection Network | 2 | 12% | 14 | 61 | 57% | 2 of 14 |
| The Exchange Desk | 1 | 6% | 14 | 69 | 66% | 3 of 14 |
| The Incoming Payment | 1 | 6% | 14 | 69 | 65% | 3 of 14 |
| The Payment Trail | 1 | 6% | 14 | 39 | 36% | 2 of 14 |
Every method
All 9 methods that apply to this sector, and where each one is documented. Full citations are at amlbenchmark.com/coverage.
| Method | Case | Rate | Result |
|---|---|---|---|
| Prepaid Cards, Gift Cards and Stored ValueHM Treasury and Home Office, National Risk Assessment of Money Laundering and Terrorist Financing 2025 (July 2025), paragraph… | The Betting Account | 45% | Missed |
| Gambling and Betting Channel MisuseGambling Commission, Money laundering and terrorist financing risks within the British gambling industry (2026 assessment… | The Collection Network | 57% | Partial |
| Money Mule Networks and RecruitmentFCA multi-firm review of firms' use of the National Fraud Database and mule detection tools | The Collection Network | 57% | Partial |
| NFT and In-Game Asset Wash TradingFATF and national FIU material on virtual asset market abuse | The Incoming Payment | 65% | Partial |
| Biometric Injection and Liveness Bypassrarely trainedGroup-IB, Weaponized AI (January 2026), documenting 8,065 biometric injection attempts against the digital loan onboarding of a… | The Verification Desk | 84% | Detected |
| Laundering and Fraud as a Servicerarely trainedFATF, Professional Money Laundering (26 July 2018), describing professional launderers, organisations and networks that launder… | The Verification Desk | 84% | Detected |
| Synthetic and AI-Generated Onboarding Documentsrarely trainedFinCEN Alert FIN-2024-Alert004 (13 November 2024) on fraud schemes using generative AI to circumvent identity verification… | The Verification Desk | 84% | Detected |
| Synthetic Identities at Scalerarely trainedUS Federal Reserve payments-improvement material on the transformation of synthetic identity fraud by generative AI; industry… | The Verification Desk | 84% | Detected |
| Control Probing and Detection-Threshold DiscoveryAnalytic pattern rather than a single reported case | The Verification Desk | 84% | Detected |
How this was measured
Each case is built around one customer who is laundering money. The rate is the share of that customer’s alerts the team caught.
- Detected
- Four fifths of them or more.
- Partial
- Between a half and four fifths.
- Missed
- Half or fewer.
- Untested
- No one has sat a case covering it.
Nothing is reported as detected on the strength of a good overall mark. Findings are suppressed below 3 runs on a case, because a smaller number describes one analyst rather than a team. Methods are declared on the case, not the individual alert, so this reports whether the team detected the method a case is built around rather than scoring each method separately.