GAMWIT's Anti-Money Laundering model flags the transaction and behavior patterns worth a compliance review, so your MLRO works from earlier and clearer signals.
Player Priority
Example rows, not live data shape as in the GAMWIT platform
Why AML matters
EU AML rules and the US Bank Secrecy Act require transaction monitoring, suspicious activity reporting and strict KYC. Getting it wrong can mean fines, a lost license or charges against executives. GAMWIT's AML model supports your compliance team by flagging activity for review.
See what your team gets£6M
fine for a major gaming operator over AML breaches.
€386K
penalty for failing to address money laundering risk.
€67K
fine from Malta's anti-money laundering unit.
Source: figures as cited on GAMWIT's Anti-Money Laundering model page.
A real screen from a licensed GAMWIT account, on sample data. Every player on the base is scored.
Every player, scored
Customer id, the risk segment, the money laundering call and the probability behind it, next to the withdrawal and deposit figures the model read. Exportable, or delivered to the MLRO.
982 players scored. Sample data. The call is a flag for review, not a finding.
These are sample players. In a walkthrough, we run the Anti-Money Laundering model on yours.
Book a walkthroughWhat changes
The filing, the escalation and the decision stay with your compliance team. The signal reaching them changes.
What triggers a look
Without GAMWITFixed thresholds, which launderers learn to stay under.
With GAMWITBehavior patterns across deposits, play and withdrawals, which are much harder to fake.
When you know
Without GAMWITIn the periodic review, or when a regulator asks.
With GAMWITAs activity happens, with time to freeze an account or block a transaction.
Volume handled
Without GAMWITSampled manually, because reading everything is not realistic.
With GAMWITFull transaction and behavior data read at once.
Who decides
Without GAMWITThe MLRO, working from a late and incomplete signal.
With GAMWITThe MLRO, working from an earlier signal with the evidence attached.
How it works
Why money laundering is hard to detect in gaming
Players can stay anonymous
Accounts often need minimal personal information, and platforms accepting cryptocurrency add another layer that makes tracing harder.
The rules are not standardized
AML enforcement differs by country, which creates inconsistency across an operator's footprint and gaps in less regulated regions.
The techniques keep evolving
Combined deposits, player-to-player collusion and structured withdrawals are refined specifically to stay ahead of detection.
Verification has a cost
Compliance can require collecting and analyzing a great deal of player data, and extended verification pushes legitimate players away.
GAMWIT's AML model finds the patterns a manual review misses, instead of relying on thresholds that launderers learn.
GAMWIT flags suspicious activity as it happens, so an account can be frozen or a transaction blocked while the money is still there.
GAMWIT reads player-specific betting patterns, gaming activity and non-gaming interactions together rather than in separate reviews.
GAMWIT supports your AML process. It does not make the compliance decision, it does not file a suspicious activity report, and it does not replace your KYC obligations or your MLRO's judgment.
In practice
Examples from GAMWIT's use-case guide. They show how the model is used, not a customer's results.
Deposit, one bet, withdraw
The situation
A player deposits 5,000, places one small safe bet, and requests a withdrawal.
What GAMWIT does
GAMWIT flags the low-turnover withdrawal. The request waits until your turnover rule is met, and the compliance team sees why.
Structured deposits
The situation
Ten deposits of 900 to stay under a 1,000 reporting threshold.
What GAMWIT does
GAMWIT aggregates the deposits over time and flags the structuring pattern. The compliance team is alerted with the sequence attached.
Unexpected wealth
The situation
A low-value player suddenly deposits 20,000.
What GAMWIT does
GAMWIT flags the deviation from the player's own history. The account is held pending your source-of-funds check.
How to use the results
Quick winOrder manual reviews by the model's confidence score, so the queue clears from the most likely cases down.
Try it yourself
Sign up and build your first model yourself, no setup call needed. Prefer a guided tour? Book a walkthrough instead.
Getting started
The Anti-Money Laundering model at a glance
What it asks of your team
Marketing, Finance, Risk and Compliance teams use it directly, without an analytics background.
Wherever and however your gaming data is stored, the GAMWIT team handles the connection. See how GAMWIT connects
Once connected, the model runs on its own and delivers fresh predictions on every run.
FinalistCompliance Innovator of the YearVixio · 2024
NominatedCompliance Technology ProviderEGR North America · 2024
NominatedData and AI PartnerEGR B2B · 2022 · 2023The Anti-Money Laundering model is already built. Connecting your data takes 2 to 4 weeks.
