iGaming Fraud Prevention

Online gambling operators face a fraud challenge that’s broader and more operationally demanding than many other digital businesses. The combination of real-money transactions, high-value promotional programs, strict regulatory requirements, and a global player base creates a fraud surface that spans the full player journey, from registration through deposit, gameplay, withdrawal, and beyond. 

iGaming is the umbrella term for online wagering and gaming activity where a player bets on the outcome of a game or event: online casinos, sports betting, poker, online lotteries, skill gaming, and fantasy sports all fall under it. It’s the digital counterpart to gambling and gaming that has traditionally happened in brick and mortar establishments, and its rapid growth has made it as attractive to fraudsters as it is to legitimate operators.

In this blog, we’ll be going over iGaming fraud prevention, addressing each of these touch points and how to protect net gaming revenue and player trust at the same time.

Different types of iGaming fraud

There are many different tactics that fraudsters can implement to scam others in the iGaming industry. Here are all of the main ones:

• Account creation fraud and multi-accounting: Fraudsters register fake accounts on a large scale to claim welcome bonuses, free bets, and promotional credits intended for new players. The economics of the scam are direct. If a welcome bonus is worth $50, creating 100 accounts that each claim the bonus yields $5,000 in extracted value. Multi-accounting at this scale can erode player acquisition economics significantly across an operator’s promotional calendar. Two specific variants show up often: gnoming, where a single player runs a network of “friends and family” accounts they secretly control to multiply bonus claims and bets, and chip dumping, common in poker, where a player intentionally loses chips to a colluding account to transfer funds, a technique also used for money laundering.
• Account takeover: ATO targets existing player accounts with deposited funds or accumulated loyalty rewards. Fraudsters use credential stuffing, phishing, and SIM-swapping to compromise the existing accounts and initiate withdrawals before the legitimate player becomes aware their account’s been compromised. For VIP players with substantial account balances, a single ATO event can represent a five- or six-figure loss.
• Payment fraud: Fraudsters use stolen payment credentials to deposit funds, gamble (sometimes deliberately losing to other fraudster accounts), and withdraw the proceeds. This form of money laundering through gambling platforms results in chargebacks and exposes the operators to regulatory action. One specific variant worth watching for is phone top-up abuse, where fraudsters use phone credit or prepaid cards, often obtained through social engineering or phishing, to fund gambling accounts while circumventing regulatory controls on payment sources.
• Bonus abuse: Beyond the multi-accounting variant, bonus abuse includes arbitrage (placing simultaneous bets across platforms to guarantee a positive outcome), collusive play (coordinated accounts cooperating to extract bonus value), and advantage play that exploits promotional mechanics beyond their intended use. This combined with multi-accounting account for the largest share of fraud losses in iGaming, with one industry study putting the figure at roughly 64% of all fraud in the sector. Because promotions calendars move fast by design, every new offer is also a new attack surface: fraud rings typically probe a promotion within hours of launch, which means detection has to operate at the same speed marketing does rather than catching abuse after the fact.
• Affiliate fraud and typosquatting: Third parties can exploit pay-per-lead or pay-per-click marketing programs for personal gain, sometimes using stolen data or payment details to generate fake leads or “sales.” A related tactic is typosquatting, where cybercriminals register domains that closely resemble an operator’s brand name to redirect illegitimate traffic and siphon off referral commissions that should never have been earned.
• Responsible gambling circumvention: In regulated markets, players who have set deposit limits, loss limits, or self-exclusions are legally protected from further gambling harm. But with the use of multi-accounting, fraudsters will bypass these protections, which creates both regulatory exposure and harm to players the operator is required to protect.

Making the case for bonus abuse prevention

Fraud teams are often asked to justify prevention spend by the number of incidents blocked, but bonus abuse gives you a cleaner way to make that case: by tying it directly to revenue protected.

Start with three numbers most teams already track: the average promotional value per new account, the percentage of new signups that review queues flag as suspicious, and the current false-positive rate on account rejections. Multiply flagged volume by average bonus value to estimate gross exposure, then subtract the cost of over-blocking, since every legitimate player wrongly declined erodes net gaming revenue too, just on the acquisition side instead of the loss side.

This reframes the conversation from “how much fraud did we stop” to “how much revenue did we protect, and how much did we generate by approving more real players faster.” Both halves matter to a CFO. Only one is typically measured.

iGaming fraud detection strategies

Detecting iGaming fraud effectively requires signal coverage across the full player journey, not just at deposit or withdrawal.

• Registration-time controls: Device intelligence and behavioral analytics at registration catch automated account creation, bot-driven multi-accounting, and identity fraud at the earliest possible point. While catching a bonus abuser after they have claimed the bonus and initiated results in a loss, catching them at the point of registration costs the operator nothing.
• Continuous session monitoring: Player risk profiles should update throughout each session. A player who registers normally but exhibits unusual bet patterns, moves toward withdrawal immediately after bonus activation, or shows behavioral characteristics inconsistent with their history is exhibiting post-registration risk signals that warrant review.
• Cross-account linking: Sift assesses thousands of signals throughout the user journey to produce a Sift Score, or an overall risk score between 0 and 100, that’s informed by cross-network activity. Linking accounts through shared device infrastructure, IP ranges, behavioral similarities, and payment method relationships reveals multi-accounting networks that appear independent in per-account analysis.
• Withdrawal risk scoring: Withdrawals are the monetization event in player journey fraud. Real-time risk scoring at withdrawal, with Sift Score and behavioral signals evaluated in the context of the player’s full account history, allows operators to flag and review high-risk withdrawals before funds leave the platform.
• Ongoing measurement: Detection strategies need upkeep once they’re in place. Track approval rates for legitimate new depositors alongside the bonus abuse block rate; if one improves while the other stalls, the underlying detection model needs retuning rather than a new rule. It’s also worth reviewing where abuse attempts cluster, by geography, device type, payment method, and time of registration, since coordinated rings tend to leave patterns even when individual accounts look clean on their own.
• Staff training and regular audits: Signal-based detection catches most fraud, but not all of it. Support and review teams benefit from ongoing training on common fraudster tactics and red flags, and periodic audits of KYC, AML, and other compliance processes help catch gaps in coverage before fraud rings find and exploit them.

Regulatory context for iGaming fraud prevention

iGaming fraud prevention can be tricky, due to the fact that regulated markets face compliance obligations that intersect directly with fraud prevention. Know Your Customer (KYC) requirements, Anti-Money Laundering (AML) monitoring, and responsible gambling protections all require the same underlying player risk intelligence that fraud prevention depends on.

An operator that cannot reliably link a new registration to a previously self-excluded player faces both regulatory sanction and the harm of enabling compulsive gambling. An operator that can’t detect structuring (breaking large deposits into smaller amounts to avoid AML thresholds) faces compliance exposure similar to a financial institution.

The most operationally efficient approach treats fraud prevention, KYC compliance, and responsible gambling monitoring as overlapping programs built on shared player risk intelligence, rather than as separate systems requiring separate data.

The stakes here go beyond fines. A secure, trustworthy platform gives players the confidence to keep playing, which directly supports retention and long-term growth. A major fraud incident can just as quickly damage an operator’s standing with players, regulators, and investors alike, in a sector where reputation is one of the most valuable and most fragile assets a business has.

If you’re an iGaming operator currently dealing with fraud challenges, then don’t worry. Sift provides machine learning technology that scans for fraudsters in your system during every point of the customer journey, and stops it in its tracks before it causes significant financial damage to your business. 

Frequently Asked Questions