Bonus abuse in iGaming: the hidden costs operators ignore

Bonus abuse in iGaming accounts for nearly 64% of all fraud in the sector, more than card fraud or identity theft. That figure comes from Sumsub’s 2025 iGaming Fraud Report.

It’s the exploitation of free spins, deposit matches, and welcome perks that operators design for genuine players, turned into a systematic revenue drain by fraudsters

And it’s getting worse. Research shows promotional abuse is now the single most common type of gaming fraud globally.  

Most operators know bonus abuse exists. Far fewer understand what it actually costs them or how much the technology to stop it has improved.

This guide covers both. You will leave with a precise definition of bonus abuse, a breakdown of tactics bonus abusers use, a clear picture of the financial and regulatory exposure it creates, and an understanding of how modern AI detection closes the gap that traditional systems leave wide open.

What is bonus abuse in iGaming?

Bonus abuse in iGaming, also called promo abuse, promotional fraud, or bonus hunting, is the systematic exploitation of promotional offers that operators design for genuine players.

Instead of engaging with a platform as an ordinary customer, fraudsters extract the monetary value of a promotion (a welcome bonus, a free spin offer, a cashback credit) without ever becoming the revenue-generating player that bonus was meant to aquire.

Unlike most fraud types, bonus abuse sits in an awkward grey zone. The tactics range from a single opportunistic player creating a second account to claim a sign-up offer twice, all the way to organised fraud rings running hundreds of synthetic identities through scripted registration pipelines. Both are bonus abuse, Only one shows up on your fraud dashboard.

The industry uses several overlapping terms. Multi-accounting refers to creating multiple accounts to claim first-time bonuses repeatedly. Gnoming describes long-running schemes where fraudsters maintain accounts over months, harvesting loyalty and reload bonuses. Bonus hunting is the serial claiming of welcome offers across many different platforms. All three are forms of bonus abuse. All three are growing.

How bonus abuse works in iGaming: the main tactics

Bonuses are designed to spark excitement, but the same mechanics make them catnip for fraudsters. And they don’t work in isolation anymore. The most damaging bonus abuse today comes from coordinated campaigns using automation, identity manipulation, and data sharing between actors. 

Here are the tactics operators encounter most often.

Multi-accounting

The cornerstone of almost every bonus abuse scheme. A bonus abuser creates multiple accounts, sometimes dozens or even hundreds, using variations of real or fabricated identities.

Each account claims a sign-up bonus. Device spoofing and residential proxies make each account appear to originate from a distinct, legitimate user.

Identity spoofing

Stolen credentials and synthetic identities let abusers bypass weak KYC checks. AI tools have made this easier: generating a photorealistic selfie to pass a liveness check now takes seconds.

The fraudster does not need a stolen passport. They need a convincing one.

Collusion rings

Groups of players coordinate their activity, sharing information in poker, backing specific outcomes on the same event, or systematically feeding chips to a designated account. Collusion is invisible from individual account data. It only becomes visible when you look at behaviour patterns across the full network.

On a dashboard, the promo looks like a success, new accounts are up, redemption rates are high. But scratch the surface, and you may find that half your ‘new players’ vanish the moment the bonus dries up.  

The hidden costs operators underestimate 

Some operators treat bonus abuse as ‘the cost of doing business’, without realising how dangerous this truly is. That reasoning tends to survive only until someone runs the numbers. 

Direct financial drain 

For example, you put $500,000 into a bonus campaign. If just 10-15% is lost to abuse, that’s $50,000-$75,000 gone. This is money that could fuel real player engagement. 

Multiply this across all the campaigns you’re running and it becomes an even bigger problem. Operators with aggressive acquisition strategies in competitive markets regularly see abuse rates that make those estimates look conservative.

Operational drag 

Fraud and compliance teams get buried in manual reviews and can spend entire weeks chasing spreadsheets of suspicious accounts, only to realise the fraudsters are already ten steps ahead.  

That’s wasted time and money that could have been used to increase your revenue. How bonus abuse distorts your KPIs and poisons board-level strategy is a problem that compounds further up the organisation than most operators realise.

Brand trust erosion 

Genuine players notice when promotions feel gamed. If a loyalty programme stops feeling rewarding — because the economics have been wrecked by abuse, real players disengage.

That is a long-term hit to retention that will not show up in this quarter’s P&L but will define next year’s.

Regulatory risk 

Here’s what most operators don’t admit out loud: regulators hate sloppy fraud prevention. If you can’t show robust controls, you’re inviting not just abuse, but also fines and reputational damage. Regulators in mature markets (the UK Gambling Commission, Malta Gaming Authority, and state-level bodies across the US) are increasingly treating weak fraud controls as a compliance failure, not just an operational one. 

That means operators who can’t demonstrate proactive bonus fraud prevention aren’t just losing promo budget, they’re accumulating regulatory exposure.

Why traditional defenses fail 

Most operators still lean on rule-based monitoring or armies of reviewers. Both approaches used to work, but now not so much. That’s because: 

• Rules are predictable. A rule that flags accounts with matching IP addresses is useful, until fraudsters start using proxies to blend in with legitimate players. A rule that catches duplicate emails is useful, until they start generating unique addresses per registrations. Any rule you can write, bonus abusers figure them out and simply route around them. 

• Manual reviews are too slow. Bonus abuse operates at machine speed. By the time a compliance analyst reviews a suspicious registration pattern, the bonus has been claimed, the wagering requirement met, and the withdrawal submitted. The fraudster moved on before the reviewer opened the spreadsheet.

• Data is fragmented. A collusion ring deliberately spreads its activity across devices, IP addresses, and time windows. No single data point triggers a rule. The abuse is only visible when you connect the dots across the full player network, and that requires a system that can process this.

The result is a confidence gap. Operators think they’re protected, but in reality, they’re running a leaky ship. 

How AI detection stops bonus abuse 

AI fraud detection is not a faster version of a rules engine. It is a fundamentally different approach: instead of defining what fraud looks like and watching for it, machine learning models learn what legitimate player behaviour looks like and flag what deviates.

In practice, this means three capabilities that rule-based systems cannot replicate.

• Pattern recognition at scale:An AI system does not flag one suspicious account. It identifies that 200 accounts share an underlying betting pattern no genuine player group would produce. The signal is not in any single account, it is in the relationship between them. Collusion rings that are invisible to per-account rules become obvious at network level.

• Genuine player protection: Player friction doesn’t need to come as a cost to protection. An AI model that understands what a real player looks like applies tight controls to high-risk registrations while letting genuine players onboard cleanly. The operators who reduce bonus abuse without driving genuine players away are using AI to make exactly this distinction. Rules-based systems apply the same criteria to everyone.

• Regulatory proof: AI systems automatically generate audit trails making it easier to show regulators you’re not asleep at the wheel. 

To be clear, this isn’t about replacing fraud teams. It’s about giving them tools to stop playing whack-a-mole and start playing as effectively as possible.  

How EveryMatrix’s Bonus Guardian applies this

This is how our Bonus Guardian tool works; helping to bulletproof your defense against bonus abusers, and your team prevent such cases.  

Unlike traditional rule-based systems, Bonus Guardian’s AI fraud detection approach is built specifically for iGaming operators’ current needs, connecting the dots across player behaviour, device fingerprints, and betting patterns that manual reviews routinely miss. 

Each bonus abuse pattern above has a distinct behavioural signature. Bonus Guardian’s models are trained to detect exactly these signatures, often at the point of registration, before a single bonus is credited. 

Bonus Guardian helps detect bonus abuse through: 

• AI-driven analysis: Cross-referencing device data, IP addresses, registration patterns, and play behaviour simultaneously, catching multi-accounters that rule-based systems miss entirely. 

• Behavioural modeling: Identifying collusion rings through shared betting patterns and coordinated withdrawal behaviour, even when accounts use separate identities and devices. 

• Constant learning: Models are trained as fraud tactics evolve, so the system stays ahead of abusers who have already reverse-engineered your old rules. 

The system constantly monitors player activity and the detected abusers are displayed in a widget for the operator to instantly view. After that, you can set restrictions on the detected abusers.  

Bonus Guardian also constantly evolves by identifying and learning new bonus abuse patterns. As an operator you play a key role in training the model by providing verdicts such as: 

• Confirmed Bonus Abuser; 

• Confirmed Suspicious User; 

• Not a Bonus Abuser.  

Ready to stop the bonus abuse leak? See how Bonus Guardian works and start saving both time and budget. 

The bottom line

Remember that opening number: almost 64% of iGaming fraud is bonus abuse. That’s not an industry footnote. Bonus abuse is the single biggest fraud vector your operation faces. And most operators are still managing it with tools built for a simpler era.

The operators pulling ahead aren’t just cutting losses. They’re redirecting the budget that was quietly funding fraudsters back into genuine player acquisition.

Bonus abuse is a solvable problem. The question is whether you solve it before your next campaign goes live or after.

Frequently asked questions

What is bonus abuse in iGaming?

Bonus abuse in iGaming is the exploitation of promotional offers, welcome bonuses, free spins, deposit matches — by fraudsters who have no intention of becoming genuine players. Using tactics like multi-accounting, synthetic identities, and coordinated betting patterns, abusers extract the monetary value of a promotion and withdraw, leaving operators with the cost and none of the revenue.

What is the difference between bonus abuse and bonus hunting?

Bonus hunting typically describes opportunistic behaviour: a single player signing up to multiple platforms to collect welcome offers before moving on. Bonus abuse is the broader category, covering everything from casual hunting through to organised fraud rings running hundreds of accounts. All bonus hunting is a form of bonus abuse; not all bonus abuse is simple hunting.

How do operators detect bonus abuse?

Effective detection combines device fingerprinting, IP analysis, registration velocity checks, and behavioural modelling. The most powerful modern approach uses AI to analyse patterns across the full player network, flagging accounts that share underlying behavioural signatures even when they use different devices, email addresses, and IP addresses. Detection at the point of registration, before a bonus is credited, is the gold standard.

What is multi-accounting and how does it relate to bonus abuse?

Multi-accounting means creating multiple accounts on a single platform to claim promotions intended for first-time users more than once. It is the most common mechanism behind bonus abuse, and the hardest to catch with simple rules, because each account can be made to look distinct. AI-based detection identifies multi-accounters through shared behavioural patterns rather than matching personal details.