Operators spend millions on engineering incentives to attract players, then act surprised when those same offers bring in fraud. Account takeovers (ATO), bonus abuse and loyalty fraud, the top three categories in the industry, account for 68% of betting and gaming losses according to SEON’s 2026 Fraud & AML Leaders Survey. The common thread between these fraud tactics is alarming: they all feed on incentives, exploiting the business model itself.
For 57% of operators, fraud losses are already outpacing revenue growth. And with 99% planning to launch a new product or enter a new market in 2026, the attack surface is only getting wider. The fix isn’t better detection after the fact. It starts with anticipating fraud from the moment incentives are designed, ensuring only legitimate players can participate.
The Welcome Mat Is Open
The conditions that make ATO so effective are the same conditions operators engineered for growth: frictionless onboarding, instant account access and high-value balances built up through deposit bonuses and loyalty rewards.
Frictionless onboarding means fewer identity challenges at the front door — exactly what a credential-stuffing operation needs. Instant account access means there’s no cooling-off period between a successful login and a withdrawal request. And the balances themselves, accumulated through deposit matches and loyalty points, give attackers a ready-made payout the moment they’re in. Each of these features was designed to compress the time between a player’s intent and their first bet — a fast lane on a highway to fraud.
Every UX decision that reduces friction for a real player removes a barrier for an attacker. The industry spent years perfecting the “who are you?” question at signup, yet almost nobody asks “are you still the same person?” after day one.
Bonus Economics, Meet Bonus Arbitrage
Welcome bonuses, deposit matches, free bets and referral incentives exist for one reason: to lower the barrier to a player’s first wager. Fraudsters read that brief differently. With synthetic identities, device spoofing, automated scripts and coordinated multi-accounting rings, they have the tools to exploit every promotional offer at an industrial scale without leaving any money on the table.
What operators design as a generous first impression becomes a systematic extraction operation. The question isn’t whether promotions attract abuse. It’s how much abuse you’re willing to fund before you redesign the incentive.
Loyalty Programs Reward the Wrong Players
VIP tiers, cashback structures, points multipliers and retention bonuses are calibrated to reward volume and consistency. That same logic rewards manufactured play — churning through low-margin wagers to hit tier thresholds, farming points for resale or exploiting cashback mechanics with minimal real risk.
The players gaming the system look nearly identical to high-value legitimate players on every metric operators track. That’s what makes loyalty fraud so expensive to absorb: the abuse pattern mirrors the ideal customer profile.
A growing subset of these attacks falls under what can be called retention bonus abuse — fraud that specifically targets the retention layer rather than the welcome offer. Dormant account takeovers, sleeper accounts aged past loyalty thresholds, and coordinated exploitation timed to major promotional events all exploit the incentives operators use to keep players active. Retention bonuses are often more lucrative than welcome offers, and fraud has followed the money.
Designing Incentives That Resist Abuse
The pattern across all three fraud types is the same: operators optimize for acquisition speed, and fraudsters exploit it. Fraud prevention must get smarter and faster, and the only way to achieve this is by designing incentives with the knowledge that they can and will be exploited.
Most promotional structures today treat every player identically from the moment they sign up. The same welcome bonus, the same unlock conditions, the same withdrawal path — regardless of whether the account behind it has a two-year deposit history or was created ten minutes ago. That uniformity is the vulnerability. When every account gets the same experience, fraudsters don’t need to be clever. They just need to be fast.
The shift is from static incentives to adaptive ones. Instead of releasing a bonus at sign-up, tie it to behavioral milestones — verified identity, a minimum play threshold, a time-gated unlock. The headline offer doesn’t change, but the path to extracting value from it now requires the kind of sustained, organic activity that fraud operations can’t economically replicate. A legitimate player barely notices the difference while a multi-accounting ring loses its entire business model.
The same logic applies further down the lifecycle. Loyalty programs that reward raw wagering volume are trivially easy to game. Programs that reward engagement patterns (such as session diversity, game variety and deposit consistency) are not. The harder it is to manufacture the profile of an ideal customer, the fewer fraudsters will bother trying.
None of this requires operators to make the experience worse for real players. It requires them to stop assuming that every player is real.
Detection Should Be More Than An Afterthought
The pattern across all three fraud types is the same: operators design an incentive, optimize it for conversion, then bolt on fraud controls after the abuse surfaces. The sequence is backward.
Fraud resilience has to be a design input, not a post-launch patch. That means stress-testing bonus structures the way product teams stress-test UX flows — modeling the abuse case alongside the happy path. It means building risk signals into the player lifecycle at the incentive layer, not just at the transaction layer.
The operators who get this right gain something their competitors can’t easily replicate. When controls are native to the experience, operators can offer bigger welcome bonuses, richer loyalty rewards and more aggressive retention campaigns — all without bleeding margin. The incentive structures aren’t the problem; designing the problem is without thinking like an attacker.
