Until recently, the logic of fighting fraud in iGaming looked relatively simple. An operator would ask the player to upload a document, compare the photo against a selfie, verify age and personal details, and consider the bulk of the job done.
If the procedure got stricter, security was supposedly better too. In 2026, that equation no longer holds.
Artificial intelligence has driven down the cost of forging documents, manufacturing synthetic identities, and scaling fraud operations. Organized groups rely on account farms, residential proxies, money mules, and tools that bypass liveness checks. Meanwhile, plenty of defense systems still fixate on one moment: registration.
The upshot is an unpleasant paradox. A legitimate player finds onboarding harder than before, while professional fraud either adapts to the new rules or pushes its activity downstream: to deposit, bonus abuse, gameplay, or withdrawal.
The real question is no longer whether KYC is strict enough. The problem is that a single identity check cannot map the full risk attached to an account.
3S.INFO breaks down why additional verification does not fix the problem, which fraud schemes are especially dangerous in 2026–2027, and what fraud actually costs, from lost LTV to licensing risk. The article also explores how operators can rebuild their defenses, from orchestrating signals and dynamic risk scoring to honestly tracking false positives and making anti-fraud decisions explainable.
More Fraud, Better Quality: Quantity Is Not the Point
According to Sumsub, the share of verification attempts in iGaming flagged as fraudulent rose from 1.10% in 2024 to 1.30% in 2025, reaching 1.53% in the first quarter of 2026. Compared with 2024, the figure is up by nearly 40%.
The post-registration trend is even more striking. Between Q1 2025 and Q1 2026, the volume of suspicious transactions grew 4.5-fold, while their average amount climbed from $3,960 to $6,500. The report drew on more than 3 million identified fraud attempts and over 30 interviews with industry specialists.
These figures call for careful interpretation. They are internal data from a single verification provider, not full statistics for the global market. What is more, the rise in detected fraud may stem not only from more fraudulent attempts, but also from better detection systems.
Still, the trajectory is unmistakable: fraudsters are not just trying to pass KYC more frequently. They operate more systematically, probe defenses longer, and increasingly leverage already registered accounts.
According to Sumsub's observations, a suspicious user now spends 4.6 times longer on a verification attempt than an ordinary player. This likely signals not clumsy attackers, but more consistent behavior: cycling through documents, repeated attempts, testing different data combinations, and bypassing individual verification steps.
AI has not made every fake profile flawless. It has made the production of such profiles cheap, fast, and practically continuous.
How AI Rewrote the Economics of Fraud in iGaming
In the past, crafting a convincing fake identity took time, technical know-how, and access to documents. Today, part of that work can be automated. Generative tools churn out face images, edit documents, assemble bundles of personal data, and help reshape a single template to fit the requirements of different platforms.
Even if most such fakes remain mediocre, their sheer volume creates a problem of its own. Thousands of cheap attempts strain automated checks, swell the manual review queue, and force operators to scale up their fraud operations. A handful of successful pass-throughs can pay for a large number of blocked applications.
At the same time, fraud-as-a-service is flourishing. A fraudster no longer needs to build the whole infrastructure from scratch. The gray market sells accounts, SIM cards, payment instruments, proxies, device emulators, automation scripts, and verification-bypass services. A complex fraud scheme turns into a kit of ready-made parts.
So operators face two tiers of threat at once. The lower tier is mass AI-generated content that creates operational overload. The upper tier is professional crews armed with genuine documents, money mules, and interconnected account networks. Tightening one check may catch part of the first wave but hardly dents the second.
- More specifically, the affiliate market's core challenge is that fraud now shapes not only the call of whether to admit a player, but also the question of who owns the conversion and who is entitled to the commission. We take a closer look at how this scheme operates, what signals expose it, and what can help safeguard reputation, payouts, and working funnels in 2026–2027.
Fraud Schemes That Pose the Biggest Threat to iGaming in 2026 / 2027
One of the most complex scenarios is synthetic identity fraud. A synthetic identity can blend real and fabricated data: an existing address, someone else's identification number, a generated face, and a fresh contact profile. Such a construct does not always look like an obvious fake, especially if the fraudster gradually builds a history of activity.
Multi-accounting and bonus abuse form another major segment. It relies on profile farms, purchased accounts, relatives, money mules, VPNs, residential proxies, and emulators. The goal is not only to claim a welcome bonus. Linked accounts can take part in arbitrage schemes, opposing bets, coordinated play, or the masking of fund movements.
According to a separate Sumsub study, bonus abuse may account for 63.8% of detected iGaming fraud. This estimate, too, should be treated as vendor statistics, yet it illustrates the shift well: promo fraud stopped being a minor drain on the marketing budget long ago. At industrial scale, it affects the unit economics of acquisition and can turn an outwardly successful campaign into a loss-making one.
Account takeover occupies a special place. If an attacker gains access to an already verified profile, the original KYC does not help: the document was genuine, and a real person went through registration. Suspicious signs surface later, with a change of device, IP address, payment details, or usual behavior.
There are also schemes at the intersection of fraud, payments, and AML. Stolen cards, chargebacks, using third parties to top up an account, quick withdrawals after minimal gameplay, and coordination across multiple accounts can look different, yet they share one trait: the user's identity is only one element of the risk.
Why Tightening KYC Does Not Solve the Problem
KYC answers a narrow question: does the declared identity match the data provided at the moment of verification. It does not guarantee that the same person will keep using the account, that the payment instrument belongs to them, or that their subsequent behavior will remain legitimate.
Even a genuine document does not always mean an honest player. It can be used by a money mule. A verified account can be bought, rented, or taken over. A user can clear every check and then hand access to an organized group.
That is why trying to answer each new risk with yet another verification screen quickly hits a ceiling of effectiveness. An extra document request lengthens registration. A repeat selfie breeds irritation. Proof of address hits conversion especially hard in regions where part of the audience lacks convenient digital documents. Source of funds may be necessary for AML, but it fits poorly as a mass check for all customers.
For a fraudster, such a procedure is just one more technical hurdle. For an ordinary player, it is a reason to close the page and pick a competitor.
The result is an asymmetry: a professional attacker is prepared in advance for a complex scenario, while a legitimate customer sees no reason to prove their good faith before they have even used the product.
What Is the Real Cost of Fraud? (Not Just Stolen Money)
Direct financial losses are the most visible part of the problem, but far from the only one. They are compounded by spent bonuses, chargebacks, payment system fees, investigation costs, and hours of manual review.
There are also less obvious losses. A false positive can block a solvent customer's deposit or delay their withdrawal. If verification takes too long, the operator loses not just one transaction, but the player's future LTV. A negative experience turns into a public complaint, damages brand reputation, and erodes trust in the licensed market.
Anti-fraud errors also ripple into the affiliate model. If an operator flags anomalous traffic but cannot show clear criteria, the dispute shifts onto the affiliate. Accusations of bonus abuse, incentivized registrations, or low-quality players start to fly. Without pre-defined rules, the partner cannot verify the calculation, and the operator cannot convincingly justify withholding the commission.
Finally, fraud can turn into a regulatory breach. A missed fraud case can touch on AML, age restrictions, protection of vulnerable users, or payment acceptance rules. In that case, the cost of the mistake is no longer measured by a single transaction, but by a fine, an audit, and risk to the license.
When Security Starts to Hurt Conversion
A one-size-fits-all KYC scenario is convenient to organize, but rarely optimal economically. A user with a local device, a familiar payment method, and a clean history should not have to go through the same path as a profile with a fresh emulator, a proxy, multiple cards, and overlaps with a known account network.
This is where the classic conflict between false negatives and false positives kicks in. If the rules are too loose, the system lets fraudsters through. If they are too strict, it blocks legitimate customers. Simply lowering one metric almost always raises the other.
Therefore, anti-fraud performance cannot be judged by the number of rejected registrations alone. A system can post an impressive block rate while simultaneously crushing conversion and overwhelming support.
What matters more to an operator is the overall economics of control: prevented losses, the cost of manual review, the share of erroneous decisions, onboarding speed, conversion to first deposit, and user retention after additional checks.
Cutting fraud by a few basis points may prove unprofitable if the operator lost a noticeable share of quality players in the process. But high conversion is worth nothing either if a significant chunk of new accounts is organized bonus abuse.
Protection Must Cover the Whole Player Journey
A modern anti-fraud model begins before a document is even uploaded. From the very first visit, it can assess the device, IP address, use of a VPN or proxy, geographic mismatches, and the quality of the traffic source. These signals should not trigger an automatic block, but they help define the next verification scenario.
During KYC, document and face checks are joined by liveness, deepfake detection, a search for reused data, and matching the account against known connections. Yet a successful verification should be an initial risk assessment, not a final clearance.
At the deposit stage, new data comes into play: who owns the payment instrument, the number of attempts, the speed of operations, one card being used across multiple profiles, and the device's links to other accounts.
During gameplay, behavior can be analyzed. Automation, synchronized actions by connected users, atypical bonus use, opposing bets, and a sharp deviation from the usual pattern often yield more insight than yet another photo of a document.
Withdrawal becomes a high-risk point, yet even here a blanket repeat KYC is not always the best answer. A more precise approach factors in whether the device, location, password, contact details, and payment credentials have changed, whether new ties to suspicious profiles have emerged, and whether the withdrawal matches the player's prior activity.
In other words, what needs checking is not just identity, but the continuity of control over the account.
Signal Orchestration Instead of Extra Barriers
An effective anti-fraud setup is not a collection of disconnected tools, but a decision-making system. Device intelligence, KYC, payment data, behavior, affiliate source, gameplay, and transaction monitoring should all feed into a single risk profile.
The key principle of such a model is dynamic risk scoring. The risk level shifts with the user's actions. A low-risk player follows a short path. When unusual signals appear, the system requests extra verification, or step-up verification. If the suspicious signals fade or get a reasonable explanation, the account does not stay forever in a flagged category.
Graph analysis plays a major role here. On their own, several accounts may look fine. The ties between them, a shared device, card, IP cluster, betting pattern, or sequence of operations, reveal an organized network. To catch this kind of fraud, what matters is analyzing not the user as an isolated record, but their surroundings.
The organizational side is no less critical. CRM handles bonuses, the KYC team reviews documents, the payments team processes transactions, and the affiliate manager tracks only the registration source. Each team holds an incomplete picture, and fraudsters exploit precisely these gaps between systems and areas of responsibility.
What Operators Should Change Right Now
- The first step is to separate onboarding fraud from post-KYC fraud. Clearing registration successfully should not automatically mean low risk for the rest of the customer lifecycle.
- The second is to drop the same depth of checking for everyone. GEO, device type, payment method, traffic source, transaction size, and behavioral history should all shape the control scenario.
- The third is to unify signals from KYC, CRM, payments, the affiliate platform, and the gaming engine. Without that, an operator will catch individual suspicious actions but never see the structure of a fraud network.
- The fourth is to measure false positives separately. A wrongful block is not a neutral side effect of security. It is lost revenue, added support load, and a potential reputational conflict.
Finally, anti-fraud decisions must be explainable. An operator needs to understand why a profile was rated high risk, what data drove the block, and how the user can appeal the error. "The model decided so" is becoming less and less acceptable, both to the customer and to the regulator.
Who Wins: Takeaways, Conclusions, and Outlook
AI gave fraudsters primarily an economic edge. It lowered the cost of preparing fraud schemes, made it possible to scale forgeries, and sped up the testing of defense systems. The answer to that cannot be endlessly complicating registration.
Every additional check costs the operator money and the player time. At the same time, it offers no protection against account takeover, money mules, coordinated networks, and fraudulent activity that begins after onboarding.
The key shift of 2026 is not that documents are being forged better. Fraud has finally moved beyond KYC. It has migrated into the links between accounts, payments, devices, bonuses, and gameplay.
So the winner will not be the operator with the longest verification flow, but the one that better connects weak signals and adds extra checks only at the moment risk genuinely rises.
The goal of modern protection is not to make registration as hard as possible. It should make fraud economically unprofitable while keeping a fast, clear path for a normal player.
