A poker table can seem suspicious after a few hands, and a casino bonus can be used up before the risk team has finished its first review. In this situation, if fraud detection takes place after the session has ended, it is too late.
It’s hard to spot collusion because it can look like normal play. Two or more users can work together, share information, avoid direct confrontation, manipulate the odds, or move value between accounts. This is why real-time fraud detection is now so important in protecting operators, players who are not cheating, and making sure the platform is fair.
Why collusion is so dangerous in iGaming
Collusion is no longer a one-off indulgence. It can be seen almost everywhere, in poker, casino games, betting exchanges, esports marketplaces, live betting, marketing to bring in affiliates, and campaigns that involve a bonus.
Poker-wise, colluding players might play in a weaker manner than they should, reveal hands, or try to prevent others from winning. In betting, players colluding at sports have easy access to delayed information or can bet the same funds at the same time whilst moving them from connection to connection in their different accounts. In the allocation of bonuses, several users involved in corruption receive multiple accounts to cash in on real money.
The money lost is only one part of the problem. Collusion also breaks trust. Honest users start to feel the game is unfair, even if they cannot prove why. When this happens, the platform doesn’t just lose money. It makes people less likely to believe it.
Why batch reviews are not enough
Traditionally, fraud deterrence was often gleaned in batches: nightly reports, weekly audits, chargeback reviews, manual table analysis, or post-event interventions. These processes still matter. They help spot patterns and sharpen the rules. However, they can be unimpressive at foiling a quickly executed abuse.
High-stakes scenarios can make it hard to figure out the right time to act. If you suspect someone is cheating, it might be because they’re betting concomitantly or perhaps stacking up money without restraint. In that case, you might not be able to recoup your money. Following the money trail becomes much less straightforward so much of the time as users move from game to game, from wallet to wallet, and from market to market, or even from device to device.
The signals that expose coordinated behavior
Collusion usually appears as a cluster of smaller clues. One shared device might be innocent. Two players folding oddly against each other once may be normal. Repeated patterns across accounts, sessions, payments, and gameplay are more meaningful.
Key signals include:
- Accounts repeatedly joining the same tables, lobbies, tournaments, or markets;
- Unusual win-loss transfers between a small group of users;
- Soft play, chip dumping, or repeated low-risk decisions between linked accounts;
- Shared device fingerprints, IP ranges, wallets, payment methods, or affiliate sources;
- Synchronized logins, deposits, withdrawals, or bet timing;
- Similar stake sizes, bonus use, or gameplay routes across multiple accounts;
- Sudden behavior changes after accounts interact with the same user group;
- Rapid withdrawal attempts after coordinated wins.
People can share networks, live in the same region, or play at similar times. The point is connection. Real-time detection becomes useful when it links events that look harmless alone but suspicious together.
A practical view of real-time collusion monitoring
The table below shows how real-time monitoring can connect user behavior with fast intervention. It is not about blocking every anomaly. It is about choosing the right response before losses grow.
| Area monitored | Suspicious pattern | Possible real-time response |
| Game tables | same users repeatedly avoid conflict or transfer value | flag table and restrict withdrawals |
| Sports markets | linked accounts place synchronized bets on thin markets | delay settlement and review exposure |
| Bonuses | multiple accounts claim similar offers from shared devices | limit bonus use or require review |
| Payments | withdrawals follow coordinated wins or chip transfers | hold payout and escalate case |
| Devices | several accounts share fingerprints or emulator patterns | increase risk score |
| Affiliates | one traffic source produces clustered suspicious behavior | pause stream and alert fraud team |
The value is in speed. A suspicious cluster can be slowed, reviewed, or limited before the operator pays out.
Bots, automation, and coordinated abuse
Collusion is not always human. Automated tools can create accounts, simulate play, test markets, claim bonuses, or coordinate actions across multiple profiles. Better bots now mimic human pacing, rotate infrastructure, and target APIs directly.
Imperva’s 2025 Bad Bot Report found 37% of all internet traffic to be bad bots as automated traffic as a whole reached 51%. This larger Internet trend is significant for iGaming as most of the automated abuse occurs outside the game itself: account creation, log-in, bonus claims, all down to payment testing and market scraping.
Real-time detection and sports integrity
In sports betting, collusion can overlap with suspicious betting activity and integrity risks. Operators need to spot unusual patterns early, especially in lower-liquidity markets where coordinated stakes can have a bigger effect.
Reports by the International Betting Integrity Association mention that they monitor an excess of $300 billion in online and retail betting turnover annually through 90+ operations and 200+ brands. The IBIA further reported 70 suspicious alerts in Q1 2026. An indication of why rapid intelligence sharing and monitoring are critical across the betting ecosystem.
For an individual operator, internal monitoring is still essential. A global alert may identify a suspicious event, but the operator must still understand its own user base: who placed the bets, whether the accounts are linked, how they were funded, and whether similar behavior appeared before.
Why risk scoring must be dynamic
Static rules are easy to understand, but collusion adapts around them. If a platform blocks users after five shared tables, fraudsters use four. If withdrawals above a fixed amount trigger review, they split amounts. If one device is flagged, they rotate devices.
Dynamic risk scoring works better because it changes as behavior develops. A user may begin with a low score, then become riskier after interacting with linked accounts, joining repeated sessions, using similar stake patterns, and requesting a fast withdrawal.
This is where modern igaming fraud prevention becomes practical. The system should connect gameplay, account links, devices, payments, bonuses, affiliate sources, and behavioral history in one view. Collusion lives in the relationships between events. The detection layer has to see those relationships.
Compliance and responsible operations
Compliance is equally assisted by monitoring in real time. The UK Gambling Commission, which delineates bona fide risks related to money laundering and terrorist financing, expects operators to consider the respective risk factors when they shape their evaluations. In iGaming businesses, any dubious account linkages, unfamiliar payment structures, and coordinated acts could lead to both fraud and compliance concerns.
This does not mean every suspicious table becomes an AML case. It means fraud, risk, and compliance teams should not operate in isolation. Collusion can involve money movement, identity misuse, bonus abuse, and payment manipulation. The data has to be connected.
Conclusion
It’s hard to stop collusion in high-stakes iGaming because it’s hidden in normal activity. One account may look clean. One bet might seem fair. The pattern only appears when things happen in a certain order.
Real-time fraud detection helps operators take action quickly. It helps to make sure that payments are checked, protect players who are playing fairly, find groups of accounts that are connected, and make people trust the platform.