Group-IB Highlights iGaming Fraud Distorting Growth Metrics
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Key Takeaways:
- iGaming fraud has industrialized, becoming networked and data-driven.
- Bonus abuse accounts for over 60% of iGaming fraud.
- Fraud can consume up to 15% of an operator’s annual revenue.
- Operators often mistake fraud for genuine growth spikes.
- Modern fraud prevention requires continuous behavioral monitoring.
- Fraudsters use AI-generated identities and behavioral simulation.
- Affiliate fraud inflates acquisition metrics and distorts KPIs.
- Group-IB offers a "No Play, No Bill" protection model.
Group-IB's Head of Gaming, Sarah Psaila, has highlighted how fraud is increasingly distorting iGaming growth metrics, with a significant portion of reported activity not being genuine. Psaila, with over two decades in iGaming fraud prevention, notes that modern attacks often mimic legitimate player behavior, making detection challenging for operators. She emphasizes that dashboards showing consistent green metrics can be a red flag, indicating undetected fraud. Operators are racing to onboard players faster, but a growing share of this activity is not real.
The Evolution of iGaming Fraud
Sarah Psaila identifies the shift from opportunistic individual abuse to organized crime as the most significant change in iGaming fraud over the past twenty years. Fraud has become networked, data-driven, and operates with its own tooling and infrastructure. Different markets present unique attack surfaces, such as collusion networks in less regulated regions or intricate AML and KYC evasion where iGaming licensing frameworks are strict. Psaila states that modern fraud exploits "the gaps" rather than just following the money. Many operators mistakenly believe their fraud rates are acceptable, often only monitoring surface-level issues like stable chargebacks. Fraud is structural, leading to losses that do not appear as direct fraud, such as inflated acquisition metrics from fake users or distorted campaign ROI from online casino bonus offers. Psaila notes that "bonus abuse alone now accounts for over 60% of iGaming fraud, quietly eating away up to 15% of an operator’s annual revenue not through theft, but through bad data, fake retention, and wasted marketing spend." Operators can mistake growth spikes for strong quarters, only to later discover that "new users" were fraud rings manipulating bonuses or VIP schemes. This façade of success leads to strategic mistakes, including overspending on acquisition and misallocating promo budgets. Psaila concludes, "You can’t manage what you can’t measure. And too many operators are still measuring the symptom, not the disease."
Gaps in Current Prevention and Future Threats
Psaila points out that most operators treat fraud prevention as a one-time verification event, rather than a continuous, intelligence-driven process. Current static checks, like KYC at registration or device fingerprinting, become outdated quickly as attackers test thresholds, automate account creation, and evolve patterns within hours. A critical gap is the lack of continuous behavioral monitoring, which tracks sessions, transaction habits, and gameplay anomalies in real time, affecting how online casino payment methods are processed. This dynamic approach, combined with machine learning, allows for fraud detection as it develops, rather than after the fact. Psaila believes the core gap is a mindset issue, noting that while adjacent domains like payments and cybersecurity have moved to dynamic intelligence, iGaming is still grappling. Manual review is identified as a bottleneck that does not scale with the millions of events operators process across global online gambling markets and payment methods, becoming a hard ceiling on growth. Fraud today operates in networks, with coordinated rings sharing devices, identities, and behavioral patterns to appear legitimate, which point-based detection systems often miss. Solving this requires a shift to relationship intelligence, connecting user, device, and behavioral data to uncover hidden links. In 2026, fraud is evolving to blend into systems, with coordinated networks using automation, shared data, and behavioral simulation. AI-generated identities can clear verification and mimic legitimate users for extended periods before fraud becomes visible. Fraudsters study detection logic, mimic normal behavior, and adapt when patterns are flagged. Affiliate and acquisition fraud are also rising, involving bot clicks, fake sign-ups, and low-intent users that inflate numbers, trigger payouts, and distort performance metrics. This leads operators to misallocate budgets and overlook high-value players, making behavioral analytics from the start of the funnel essential.
Intelligence-Driven Solutions and Commercial Models
Intelligence-driven fraud detection integrates multiple signals, including device fingerprinting, cross-channel correlation, persistent IDs, behavioral biometrics, and threat intelligence. These components form a continuous learning system, creating an essential intelligence layer. Fraud prevention that enables growth in 2026 is described as infrastructure that learns continuously and adapts in real time. Risk scoring evolves with every action across device, payment, and behavioral signals, becoming an intelligent filter that removes bad actors while allowing legitimate players to move freely. Group-IB’s "No Play, No Bill" commercial model aligns protection spend with real player activity, ensuring operators do not pay to detect fraud in traffic that never converts. Psaila emphasizes that effective fraud prevention should enable growth, rather than slowing it, by connecting fraud decisions directly to growth, acquisition cost, marketing ROI, player experience, and regulatory exposure.
