Betting and Gaming Operators Are Growing Fast, But Fraud Is Growing Faster

Betting and gaming is the fastest-growing sector by nearly every commercial measure. Yet, according to SEON’s research, 57% of operators report that fraud losses are growing faster than revenue. The industry is winning and losing at the same time, and most operators are responding to the losses the same way they chase growth: by doing more of what got them here.

That instinct, let’s call it the growth trap loop, is a self-reinforcing cycle where aggressive expansion generates more fraud, fragmented tools miss the signals, teams hire more analysts to compensate, costs climb past revenue and operators push harder on growth to make up the difference. Breaking the loop requires something operators haven’t tried yet: fixing the foundation before adding headcount, automation or AI.

The Incentives Driving Growth Are Driving Bonus Abuse, ATO and Loyalty Fraud

Nearly every operator (99%) plans to launch a new product or enter a new market in 2026. That ambition may fuel the industry’s momentum, but it also fuels its fraud problem.

Operators compete on many fronts: product experience, odds, live dealer quality, app performance and brand. But the lever that moves the needle fastest on acquisition and retention is incentives. Welcome bonuses, loyalty programs, reload offers and VIP perks are how operators win players from competitors and keep them coming back. Each of these tactics has a direct fraud exposure on its reverse side. 

Account takeover leads all fraud categories, accounting for 27% of losses, followed by promotion and bonus abuse at 23% and loyalty program abuse at 18%. Together, those three types account for 68% of all losses, and each directly exploits the incentive structures operators built to acquire and retain players. Growth and fraud are not separate problems in betting and gaming: they are fed by the same mechanics and scale on the same curve.

The Tools Can’t See the Whole Picture

Growth initiatives open multiple simultaneous doors to fraud. When fraud signals do appear, the tools meant to catch them are often blind to the pattern.

Operators typically run separate, siloed platforms for know-your-customer (KYC), fraud detection and anti-money laundering (AML) compliance. Each sees only a narrow slice of the player journey, and when you are orchestrating across five or more disconnected point solutions, meaningful automation becomes nearly impossible. KYC software tools flag suspicious documents during onboarding, but the fraud engine never receives that signal.

Three weeks later, the same player triggers a bonus abuse alert, but there is no pattern to catch as the data lives in a different system. Fraudsters have learned to exploit this fragmentation, probing systems simultaneously, knowing that the tools behind each one cannot share what they see.

Data confirms the scale of the problem: more than half (55%) of leaders say data visibility is extremely or very challenging, and operators are nearly four times more likely than peers in other industries to call unified data “extremely challenging” — 22% compared to 6%. In response, 28% plan to replace a vendor in 2026, nearly double the cross-industry rate. But a new vendor plugged into the same fragmented foundation inherits the same blind spots. The problem is architectural, not the vendor.

Hiring Doesn’t Scale Against Fraud

When tools can’t close the gap, the default response is headcount. Nearly half of operators (47%) are prioritizing headcount in their 2026 budgets — 12 percentage points higher than any other industry surveyed. Betting and gaming is the only sector where headcount investment leads AI and machine learning investment. Everywhere else, that priority is reversed.

This is not AI skepticism. Operators actually lead in practical, localized AI, such as auto-filling regulatory reports, generating alert summaries and flagging individual transactions. Where they fall short is in deploying AI across systems: models that correlate onboarding signals with transaction behavior weeks later, or connect a KYC flag to a bonus-abuse pattern that emerges mid-lifecycle. That kind of cross-lifecycle intelligence requires a unified data layer underneath it. Without that layer, AI remains a feature within a single tool rather than a capability across the full player journey. 

Meanwhile, the tools analysts depend on are actively generating noise. More than a third of operators (36%) report false-positive rates of 26%-50%. Every false positive is a legitimate player blocked, a deposit declined or a withdrawal delayed — turning away players that growth teams spent real money to acquire. So operators hire more analysts to clear the queue. Each new analyst simply inherits the same broken infrastructure and the same endless backlog.

The Loop Defined

What emerges from these conditions is beyond a collection of isolated problems. It is a single, self-reinforcing cycle:

1. Aggressive growth — expands the player base, multiplies incentives and accumulates dormant accounts across new markets
2. Fraud scales with it — targeting the very incentives growth demands, exploiting the trust that established accounts carry
3. Fragmented tools create blind spots — no single system sees across the full lifecycle, so signals never connect into patterns
4. Headcount fills the gaps — but every new hire inherits the same broken architecture and the same false-positive queue
5. Costs outpace revenue — fraud scales exponentially while teams scale linearly
6. Operators double down on growth — to compensate for squeezed margins, they push harder on expansion, which feeds stage one again

The clearest evidence that each stage is producing real damage: account takeover (27%) and loyalty or rewards abuse (18%) together account for 45% of all betting and gaming fraud losses. Account takeover is the mechanism; promotion abuse is the objective. These two problems are actually two stages of the same loop, and together they account for nearly half of the industry’s fraud losses.

What Happens If You Don’t Break the Loop

The loop does not pause while operators plan. It compounds. Every quarter of inaction, growth creates more accounts, incentives and surface areas for abuse. Fragmented tools continue to miss the patterns. False positives pile up and analysts burn out reviewing them. Fraud costs climb faster than revenue, squeezing margins further, pushing operators to chase even more aggressive growth to make up the difference. Operators who do not break the loop will find themselves scaling a business that is systematically bleeding revenue to fraud their tools cannot see, their analysts cannot triage and their AI cannot reach.

What Breaking the Loop Actually Looks Like

The instinct when facing high review volumes is automation. But automating on top of fragmentation is like pouring cement over an uneven foundation — it seals every crack in place. It turns a fixable problem into a systemic one that generates false positives at scale.

That sequence matters. The data architecture has to come first: connecting KYC, AML and fraud data into a single player view so signals correlate across the lifecycle. That unified foundation is the prerequisite for lower false positives, faster investigations and AI that genuinely transforms operations rather than marginally improving a single system. 

Before the next hire or vendor contract, every fraud leader should be asking three questions:

• Is our data architecture as mature as our growth strategy? The gap between growth ambition and data readiness is where the loop originates. Closing that gap is the only way to stop feeding it.  
• Are we investing in AI as a capability, or just consuming it as a feature? Fragmented data limits AI to incremental improvements within siloed tools. Unified data is what unlocks transformational results across the player lifecycle.
• Can our fraud function scale as fast as our business? People scale linearly. Fraud scales exponentially. The only lever that changes that equation is architecture — consolidating tools, unifying data and building automation on a secure foundation first.

What Fraud Prevention Looks Like When It Actually Works

The operators who escape the loop will not be the ones who hired the most analysts or bought the newest point solution. They will be the ones who fixed the architecture first.

What changes when the architecture is right is not just the fraud rate. It is what the fraud function is allowed to do. Right now, most operators run their fraud teams in a permanent state of catch-up — clearing queues, investigating false positives, chasing patterns across systems that were never designed to talk to each other. The work is reactive by necessity, not by choice.

When the data layer is unified, that changes. Operators who can see across the full player lifecycle can offer more generous retention bonuses, enter new markets faster and compete harder on player experience — because their controls can actually see the risk. The growth playbook stops being a liability and instead becomes something the fraud function actively enables.

“We thought we had everything covered because we understood the nuances of our business, platform and users. But we discovered layers of fraud, from friendly to extreme. We realized how exposed we were.” — Claudia Farrugia, Head of Operations at MrQ

That is the real shift. Not a rebrand of the fraud team or a new metric on a dashboard. The fraud function earns a seat at the table where growth decisions are made because, for the first time, it has the data to inform them.