GAMWIT's Bonus Abuse model identifies the players likely to abuse a bonus before the offer goes out, so promotional budget reaches players who will play.
Player Decision
Example rows, not live data shape as in the GAMWIT platform
Why bonus abuse matters
It also makes promotions less fair for honest players. GAMWIT's Bonus Abuse Prevention model flags the players likely to abuse a bonus before the offer goes out.
See what your team gets50%
of fraud in iGaming, online casinos and sports betting involves bonus abuse.
About 15%
of annual gross revenue is typically lost to bonus abuse.
Up to 10%
of revenue can go on fraud management alone.
Source: figures as cited on GAMWIT's Bonus Abuse Prevention 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 bonus abuse call and the probability behind it, next to the deposit, bet and NGR figures the model read. Exportable, or delivered to the fraud desk.
982 players scored on 8 Sep 2026. Sample data.
These are sample players. In a walkthrough, we run the Bonus Abuse Prevention model on yours.
Book a walkthroughWhat changes
The difference is mostly when you find out, and how many people it takes.
Who does the finding
Without GAMWITMarketing, Promotions, Customer Service, Risk and Compliance, Legal and BI, coordinating across silos.
With GAMWITOne GAMWIT model. Those teams act on its list instead of building it.
When you find out
Without GAMWITAfter the payout, during reconciliation or a review.
With GAMWITBefore the offer goes out, so the player is left off the campaign.
Genuine players
Without GAMWITCaught by blunt rules, with trust and experience damaged in the process.
With GAMWITProtected: multiple checks run before anything is flagged as abuse.
What it needs about the player
Without GAMWITIdentity checks, email history and social media, which abusers avoid leaving behind.
With GAMWITBehavior only. No personal data needed.
How it works
Why abusers are hard to catch
They are careful
Over 90% of bonus abusers have never appeared in a data breach, most use free email providers, and they tend to leave no social media presence attached to that address.
It takes too many teams
Identification spans Marketing, Promotions, Customer Service, Risk and Compliance, Legal and BI, which brings coordination delays, knowledge silos, inconsistent data handling and accountability gaps.
The cost of a false positive is high
Labeling a real player an abuser damages their experience and their trust, and that costs reputation and revenue too.
GAMWIT's Bonus Abuse model detects the behavioral patterns that indicate abuse, instead of fixed rules that abusers learn to work around.
GAMWIT runs repeated checks so that only genuine cases are flagged and real players are not caught by mistake.
GAMWIT reads withdrawal and deposit variation, betting patterns and activity levels at once, using proprietary features built from decades of iGaming work.
Because the signal is behavioral and game-specific, predictions do not depend on personal data, which keeps player privacy intact.
In practice
Examples from GAMWIT's use-case guide. They show how the model is used, not a customer's results.
The gnoming ring
The situation
A group of accounts with different IDs and identical betting patterns clears bonuses together.
What GAMWIT does
GAMWIT reads the shared behavioral fingerprint across the accounts. They are held for investigation before any payout.
Minimal-risk wagering
The situation
A player bets the safest possible way to meet a wagering requirement.
What GAMWIT does
GAMWIT classifies the player as a bonus abuser. Wagering contribution is paused and a warning goes out, under your rules.
Zero-deposit scrapers
The situation
Sign-ups that only ever collect free spins, with no intention to deposit.
What GAMWIT does
GAMWIT tags the scraper pattern. Those accounts receive deposit-only offers from then on.
How to use the results
Quick winFeed the risk score into your registration flow, so serial abusers are stopped at the gate.
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 Bonus Abuse Prevention 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 Bonus Abuse Prevention model is already built. Connecting your data takes 2 to 4 weeks.
