Understanding Payment Fraud in iGaming

Payment fraud is one of the most costly and operationally disruptive types of fraud facing iGaming operators, and it does not start at the deposit button. This guide covers the most common iGaming payment fraud types, why gambling platforms face disproportionate exposure and how to detect and prevent fraud from registration through withdrawal.

Why Are iGaming Operators Especially Exposed to Payment Fraud

iGaming operators carry a risk profile that most other merchant categories do not — and according to SEON’s Global iGaming Risk & Fraud Report, the threat is actively getting harder to detect.

Card networks classify gambling platforms as high-risk merchants, affecting processing rates, chargeback thresholds and monitoring requirements. When an average merchant exceeds roughly 1% chargeback ratio, they face fees and scrutiny. For an operator already in the high-risk category, the same exposure arrives with a shorter runway and more severe consequences.

The business model compounds this. Player acquisition depends on fast, low-friction onboarding, which is the same opening fraudsters exploit to push stolen card deposits through before controls flag the activity. In Asia-Pacific markets, fraud-related declines can reach up to 40% of transactions. In UK and EU markets, approximately 25% of new signups are deemed high-risk at onboarding.

What makes the current environment particularly dangerous is the quality of the remaining threat. SEON’s network data shows a decisive retreat of amateur fraud, confirmed by sustained declines in basic indicators like cookie hash reuse and incomplete device profiles. What’s left is almost exclusively professional syndicates operating across the full player journey and exploiting the gaps between disconnected fraud and payments workflows.

How Do You Detect iGaming Payment Fraud?

Fraud in iGaming doesn’t concentrate at a single point, it enters at registration, escalates at deposit, and often only becomes visible at withdrawal. Effective prevention means applying the right controls at each stage, calibrated to the specific risk at that stage.

1. At Signup

The primary gambling-specific risk at registration is multi-account creation for bonus abuse — players opening duplicate or synthetic accounts to claim welcome offers repeatedly. This distinguishes onboarding fraud in this vertical from generic identity fraud: the motivating incentive is built into the operator’s own acquisition model. Self-exclusion bypass is a parallel compliance risk unique to this sector and should be addressed by the same signup controls.

2. At Deposit

First-session deposits are disproportionately high-risk. Bonus hunters and stolen-card depositors both target the initial deposit window, and the two behaviors often co-occur — promotional abuse layered on top of card testing is a pattern largely unique to this vertical. BIN-to-IP mismatch checks matter here not just as a fraud signal but as a licensing check: accepting a deposit from a player in an excluded jurisdiction is a regulatory breach, not just a chargeback risk.

3. Post-Deposit

This is where generic fraud detection frameworks fall short. Legitimate players exhibit recognizable betting behavior. Fraudsters who have taken over an account or deposited stolen funds often place rapid, low-variance bets, backing both sides of an outcome for instance, to convert balance to withdrawable funds as quickly as possible. This cash-out normalization pattern is a sector-specific signal that doesn’t appear in ecommerce or banking fraud detection and rules calibrated for those environments will miss it.

What Tools Do iGaming Operators Need for Payment Fraud Prevention?

No single tool covers the full attack surface in iGaming, the fraud patterns are too varied and too layered across the player journey for point solutions to keep up. Operators need a connected stack that shares signals across onboarding, transaction monitoring, and withdrawal monitoring, rather than treating each stage as a separate problem.

• Onboarding monitoring – identifies risky registrations before first deposit using digital footprint, device and IP signals.
• Device intelligence – detects spoofed devices, VPNs, emulators and linked accounts across sessions.
• IP analysis – evaluates IP reputation, proxy use, geolocation and network anomalies at the point of deposit.
• Transaction monitoring – reviews deposit behavior in real time for velocity abuse, cross-border mismatches and suspicious patterns
• Chargeback analytics – tracks which acquisition channels, payment methods or player segments are generating disproportionate dispute exposure
• KYC verification – confirms player identity to reduce fake accounts and synthetic identity fraud at onboarding
• AML tools – monitors transactions for laundering patterns and supports SAR filing and ongoing monitoring obligations
• Withdrawal monitoring – flags rapid cashouts, altered banking details and deposit-to-withdrawal ownership discrepancies
• AI-driven fraud detection –  surfaces cross-signal patterns that rules-based systems operating in isolation miss.

“Automation turns transaction monitoring from a compliance checkbox into a strategic control layer that quietly protects revenue, player experience and regulatory relationships in the background.”

Nauman Abuzar, Director of AML & Risk Solutions

Other Ways SEON Can Help iGaming Companies

At SEON, iGaming has always been a vertical where our anti-fraud tech has the most positive impact. This is because SEON covers the full player journey, from registration to withdrawal, on a single platform integrating digital footprint analysis, device intelligence, transaction monitoring and AML screening into a single decision layer.

At registration, 900+ signals evaluate email, phone, IP and device data to surface synthetic identities and depositors using stolen cards before the first deposit. At the deposit stage, configurable rules flag velocity abuse, BIN-to-IP mismatches and prepaid card patterns in real time with no engineering dependency to adjust thresholds. At withdrawal, the same system detects structuring behavior, rapid cashout sequences and account detail discrepancies that indicate a takeover or layering attempt.

Lottoland achieved a 32x ROI, protecting marketing spend from bonus abuse. Soft2Bet reduced chargebacks and cut manual fraud queries by 40%, freeing compliance teams to focus on policy rather than case volume. Operators using SEON automate 95% of fraud checks and reduce fraudulent registrations by 90%.

The fraud and AML layers share the same signal set. A deposit velocity anomaly that triggers a fraud rule simultaneously informs the AML alert. One review event, one audit trail, one escalation path. Operators running separate workflows for the same activity are paying twice for incomplete coverage.