One of the most difficult issues facing gaming and gambling platforms today is multi-account fraud. In which criminals set up and manage several accounts to misuse bonuses, launder money, tamper with leaderboards, or launch coordinated attacks. The methods of fraud have shifted from opportunistic to industrialized. Attackers can now scale account creation and abuse at times when human teams are least prepared, thanks to fraud farms, bot networks, and AI-assisted toolkits. Operators have to combat this during sign-up, cash-out, and play, and they are becoming compelled to do so in real time.
Why Real-Time Detection Matters
Multi-accounting is not a one-off incident. It is a persistent risk that can cost operators money, consumer confidence, and regulatory headaches (particularly in cases where money-laundering risk and bonus abuse collide). According to recent industry surveys, fraud has increased annually for the majority of iGaming operators. Attackers are taking advantage of early morning hours when teams are less active. Therefore, always-on, automated defenses are extremely important.
Techniques Attackers Use
Fraudsters deploy a variety of strategies, including mule networks that transfer real money through ostensibly authentic accounts, device farms that cycle device IDs and IPs, automated bots for mass sign-ups, and synthetic identities created from stolen PII. Additionally, they schedule attacks to fall during promotional periods or downtime. For example, providers report that sign-up attacks increase during major events and the holidays. Defenders can prioritize detection rules thanks to these routines, but detection also needs to change quickly.
What Real-Time Anti-Fraud Looks Like (Technical Playbook)
Signal fusion at the edge: At the time of registration and transaction, integrate device telemetry, IP reputation, phone and email checks, BIN and payment signals, geolocation anomalies, and behavioral fingerprints into a single, scored decision. By identifying linked accounts that share subtle signals (such as the same device fingerprint across multiple emails, overlapping payment rails, reused phone numbers, etc.), this lowers false positives.
Adaptive risk scoring: Make use of a multi-layer evaluation (feedback loop + ML model + fast rules). High-risk accounts are blocked, medium-risk accounts need verification, and low-risk accounts might experience friction (2FA). Platforms can downgrade or upgrade suspicion in response to emerging signals by means of continuous scoring.
Session & behavior analytics: Tracking in-game activities in real time, such as betting speed, stake patterns, and odd win/loss patterns, in order to identify coordinated play or “gnoming” (account rotation). Over time, behavioral models identify bots that mimic humans poorly.
Automated orchestration & response: Playbooks that escalate when human review is required and automatically apply mitigations (rate limits, device quarantine, progressive challenges) without human intervention. This is crucial because attackers specifically take advantage of a sluggish manual review window.
How Goso Identity Helps — Built For Gaming & Gambling
Goso Identity’s approach maps exactly to the layered, real-time needs above:
Fraud Engine (single API): Allows you to block or challenge requests in milliseconds during sign-up and cashier time by combining email, IP, phone, BIN, address, and device signals into a single fast risk score. This lowers decision latency and relieves teams of the burden of stitching together multiple vendors.
Multi-account fraud detection: Instead of considering each registration as a separate, persistent identity, graphing connects accounts across signals (device, payment, and contact points) to expose networks of related accounts.
Adaptive playbooks: Prebuilt and customizable response rules let you automate progressive friction (OTP → biometric check → manual review) based on live risk.
Focus on UX + compliance: Identity checks are adjusted to minimize false positives, which is essential in gaming markets that are sensitive to retention and where inadequate onboarding harms LTV.
Operational Tips For Teams
- Run intensive automation around-the-clock and keep an eye on attack patterns by time of day. Automate for staff offline times, as Goso Identity and others report that fraud peaks during those times.
- Prior to cash-out, stop mule chains by combining device intelligence and payment BIN analysis.
- Employ continuous learning to strengthen detection without making it more difficult for legitimate players by feeding verified fraud cases back into models and rules.
Bottom line
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