Promotional bonuses are fundamental to customer acquisition in iGaming, but the same welcome offers, free spins and deposit matches that attract genuine players also attract fraudsters looking to extract value without ever behaving like a real customer. Stopping this abuse requires more than identity verification at signup — it requires correlating device, network, behavioral and payment signals in real time.
Short Answers: Ways to Detect Bonus Abuse in iGaming
Bonus abuse in iGaming can be detected by combining multiple risk signals rather than relying on KYC alone. Key detection methods include:
- Device fingerprinting — identify multiple accounts created from the same device or device cluster.
- IP and proxy intelligence — detect VPNs, residential proxies, TOR nodes and suspicious IP patterns.
- Identity correlation — link accounts using shared identity, phone, email, payment or address attributes.
- Behavioral analysis — detect repetitive registration, deposit, wagering and withdrawal patterns.
- Velocity monitoring — flag unusually rapid account creation, bonus claims or cashouts.
- Payment intelligence — identify shared cards, wallets or payment instruments across supposedly unrelated users.
- Risk scoring — combine identity, device, network, behavioral and transaction signals into a single risk score.
- Network analysis — detect coordinated groups of accounts behaving like a fraud ring rather than isolated players.
Introduction
Promotional bonuses are fundamental to customer acquisition in iGaming. Welcome bonuses, free spins, deposit matches and promotional bets can attract new players and increase engagement. However, the same incentives can become targets for organized fraud when attackers discover ways to repeatedly claim rewards without behaving like genuine customers.
Bonus abuse in iGaming occurs when players, fraudsters or coordinated groups manipulate promotional programs for financial gain. Common techniques include multi-accounting, synthetic identities, account sharing, stolen credentials, bonus farming, payment-instrument sharing and automated registration.
The problem is becoming increasingly significant. A March 2026 survey from LexisNexis Risk Solutions found that 78% of online gaming decision-makers in North America identified bonus abuse as a top fraud threat, making it the most prevalent fraud type cited in the study.
For operators, preventing bonus abuse is therefore not simply about protecting a promotional budget. It is about protecting acquisition economics, payment integrity, player trust and the overall health of the gaming ecosystem.
Understanding How Bonus Abuse Works
A typical bonus-abuse scheme starts with the creation of multiple accounts. A fraudster may use different email addresses, phone numbers or identity combinations to appear as separate players.
More sophisticated attackers go further, using:
- Synthetic or stolen identities
- Residential proxies and VPNs
- Anti-detect browsers
- Multiple devices or emulators
- Shared payment methods
- Purchased player accounts
- Automated registration tools
- Coordinated betting behavior
Organized groups can make this activity particularly difficult to detect because individual accounts may appear legitimate when examined independently.
The real signal often appears across the relationship between accounts. For example, five accounts might use different names and email addresses but share similar device characteristics, login behavior, IP infrastructure, payment instruments and bonus-claim timing. Individually, each account may pass conventional KYC. Collectively, they may represent a coordinated bonus-abuse network — a pattern industry analysis increasingly points to as evidence that bonus abuse is run by organized syndicates rather than opportunistic individuals.
Why Traditional KYC Is Not Enough
KYC verifies whether an individual can establish an identity. Bonus-abuse detection asks a different question:
"Is this user legitimately entitled to this promotion?"
A fraudster can potentially pass identity verification while still operating multiple accounts or participating in a coordinated abuse network.
This is why operators need to combine identity verification with digital footprinting, device intelligence, behavioral analytics and transaction-level risk signals.
A Technical Framework to Prevent Bonus Abuse in iGaming
1. Establish a Unique Player Identity
Operators should create a persistent risk profile for every player instead of treating each registration as an isolated event. The profile can incorporate:
- Verified identity
- Email and phone intelligence
- Device fingerprint
- IP reputation
- Geographic consistency
- Payment identifiers
- Account history
- Previous promotional activity
This creates a broader view of the player beyond basic KYC information, similar to how digital footprinting is used to corroborate identity across other high-fraud onboarding flows.
2. Use Device Fingerprinting
Device intelligence is particularly valuable against multi-accounting. Even when fraudsters change email addresses or IP addresses, device characteristics can reveal relationships between accounts.
Signals may include operating-system attributes, browser characteristics, device configuration and other technical identifiers.
A repeated device-account relationship should not automatically result in a block. Instead, it can contribute to a dynamic risk score and trigger additional investigation when combined with other suspicious signals.
3. Monitor IP, VPN and Proxy Intelligence
Fraudsters frequently use VPNs, proxies and other infrastructure to disguise their actual location.
Real-time IP intelligence can help identify:
- Datacenter IPs
- Residential proxies
- TOR traffic
- Known malicious infrastructure
- Geographic inconsistencies
- Rapid IP changes
Atna's fraud-prevention framework combines IP intelligence, device fingerprinting, behavioral biometrics, threat databases, velocity triggers and trust scoring to identify suspicious activity.
4. Detect Behavioral Anomalies
Behavior can be more difficult to fake consistently than identity information. Operators can analyze:
- Registration speed
- Deposit timing
- Bonus activation
- Betting patterns
- Session duration
- Withdrawal behavior
- Login frequency
- Account-to-account similarities
For example, a group of accounts that registers within minutes, deposits similar amounts, claims the same promotion and follows nearly identical betting patterns deserves greater scrutiny.
5. Analyze Payment Relationships
Payment intelligence can uncover connections that traditional identity checks miss. Multiple supposedly unrelated accounts using the same card, bank account, wallet or other payment instrument can indicate account farming or coordinated abuse.
Payment signals become even more powerful when correlated with device, IP and behavioral information.
6. Apply Dynamic Risk Scoring
Rather than relying on binary rules such as "one device = fraud," operators can assign a risk score based on multiple signals. Atna's scoring engine, for example, combines rules, machine-learning insights, device and OS signals, IP intelligence, email and phone networks and customizable parameters into a structured decision such as approve, review or decline.
This approach enables operators to differentiate between:
- Low risk → Allow
- Medium risk → Additional verification or review
- High risk → Restrict promotion or block
This reduces unnecessary friction for legitimate players while increasing scrutiny of suspicious users.
Key Benefits of Eliminating Bonus Abuse in iGaming
1. Protect Promotional Revenue
Promotions are designed to acquire valuable customers. Preventing abuse ensures marketing budgets are spent on genuine players rather than organized bonus hunters.
2. Improve Player Lifetime Value
A player who repeatedly extracts promotional value without generating sustainable revenue can distort acquisition metrics. Removing abusive accounts allows operators to focus on genuine high-value customers.
3. Reduce Fraud Losses
Bonus abuse can overlap with payment fraud, identity fraud, account takeover and money-movement risks. A unified fraud strategy can address multiple threats simultaneously.
4. Strengthen Player Trust
Legitimate players expect gaming platforms to maintain a fair environment. Reducing coordinated abuse helps protect promotional fairness and platform integrity.
5. Improve Operational Efficiency
Automated risk scoring reduces the need for fraud teams to manually investigate every suspicious registration or promotion claim.
6. Detect Organized Fraud Networks
The biggest advantage of multi-signal intelligence is the ability to identify relationships between accounts rather than investigating them independently.
This is particularly important as bonus abuse becomes increasingly organized. Sumsub's 2026 research estimates that bonus abuse accounts for 63.8% of iGaming fraud, while European operators are estimated to lose 10–20% of marketing turnover to the problem.
Conclusion
Bonus abuse in iGaming is no longer simply a problem of players opening duplicate accounts to claim an additional welcome offer. It has evolved into a sophisticated fraud category involving identity manipulation, device spoofing, proxies, automation, payment relationships and organized account networks.
The most effective defense is therefore not a single KYC check or static rule. iGaming operators need a layered risk architecture that combines identity intelligence, device fingerprinting, IP intelligence, behavioral analytics, payment relationships, velocity monitoring and dynamic risk scoring.
By connecting these signals in real time, operators can distinguish genuine players from coordinated bonus-abuse networks, protect promotional budgets and maintain a safer gaming ecosystem. Platforms such as Atna AI can help operators build this intelligence layer by combining real-time fraud signals, adaptive scoring and automated decisioning — get a demo to see it applied to your promotional flows.
The objective is simple: reward genuine players while making promotional fraud economically and operationally difficult to scale.
Frequently Asked Questions
Bonus abuse is the exploitation of promotional offers through tactics such as multi-accounting, synthetic identities, account sharing or coordinated betting.
It increases promotional losses, distorts customer acquisition metrics and can expose operators to broader fraud risks.
KYC helps establish identity but cannot independently detect every form of multi-accounting or coordinated promotional abuse.
It identifies technical relationships between devices and accounts, helping operators detect multiple accounts associated with the same device or device cluster.
Yes. Fraudsters may use VPNs and proxies to disguise their location or create apparent separation between related accounts.
Multi-accounting occurs when one individual or group creates multiple player accounts to circumvent promotional or platform restrictions.
AI can correlate large volumes of identity, device, behavioral, network and transaction signals to identify patterns that static rules may miss.
No. Risk-based decisioning can classify players into approve, review and decline categories, reducing false positives while controlling fraud.
