It seems that AI is a cure-all for iGaming risk management today. But here’s the catch: every sportsbook or casino product has its own setup, and AI has to be tuned to its specifics. Besides, just because a tool is AI-based doesn’t mean it’s surefire.Â
Despite this, many operators use the technology blindly, without a strategy. This is their biggest mistake, but not the only one. For over a decade, I have been countering iGaming threats, and here are the gaps I see this year. Â
1. Focus on Common RisksÂ
Fraud remains the most damaging threat in iGaming. And it’s only getting worse. Between 2022 and 2024, fraud cases jumped by an average of 64%. The deposit stage is now the prime target, but attacks hit every step, from onboarding and verification to gameplay and withdrawals.
At the same time, I see many anti-fraud tools that focus on just one or two areas. Even those powered by AI often leave critical blind spots. But partial protection isn’t protection at all. It damages not just the operator’s security but also their reputation.
To stay resilient, operators need full-spectrum solutions that secure every stage of the player journey. Doubt whether you have made the right choice? Look under the hood of your current fraud prevention software to assess what it blocks, what it misses, and how it reacts under pressure.Â
This step is more critical than it seems. If you don’t test your tools, the fraudsters will.Â
2. Risk Management That Is Reactive, Not Proactive
The era of reactive, post-factum fraud protection solutions is over. Outdated systems that can’t process data in real time don’t just slow the product down. They leave it exposed. The only way to stay afloat is to manage risk proactively, before it even happens.
That’s where AI makes the difference. It processes massive real-time data streams, scans player behavior and betting patterns, and predicts anomalies like unusual session pacing or erratic product switching.Â
What’s more, the technology doesn’t act in isolation. AI fraud detection solutions profile players across time in compliance with overall confidence levels and risk scoring. This minimizes false blocks, keeps legitimate players engaged, and helps operators hit their target GGR.
The power of AI/ML algorithms goes beyond risk detection. They also sharpen decision-making by recommending when to escalate manual reviews, pause suspicious payouts, or trigger adaptive verification.Â
3. Lack of Customization
Most operators already rely on artificial intelligence across key areas of anti-fraud and risk management. Some even overuse it. But here’s the truth: any powerful tool falls short without sufficient knowledge, setup, and resources behind it.
No matter how advanced your system is, it won’t perform to the fullest unless tailored to your business model, fraud patterns, player base, and operational flow. Personalization is the key. It shouldn’t stop with selecting a reliable vendor. It should continue throughout the entire lifecycle of an iGaming product.
Besides, AI isn’t a silver bullet. It’s still an expensive, complex, and, let’s be honest, imperfect technology. There is no need to apply it everywhere. A much wiser step is using AI fraud prevention tools as part of a broader, analytics-driven strategy to cover the aspects for which they have been trained.
4. Same Strategy for Casino and Sportsbook
Applying the same risk strategy to the online casino and the sportsbook is another mistake. Yes, these products may operate within the same ecosystems and face similar threats around KYC/AML compliance, payments, or player activity. But that’s where the overlap ends.
Sportsbook and casino platforms have different logic, attract different player behavior, and face different fraud schemes. A bonus abuse tactic that works in slots may never appear in live betting, and vice versa.
| iGaming Sector | Fraud Type | How AI/ML Models Combat It |
| Casino, Sportsbook | Bonus Abuse | Analyze players’ behavior and cross-account interactions, and send instant alerts to operators when detecting suspicious activity |
| Carding | Monitor payments and IP/device mismatch in real time to flag fraud-prone transactions |
| Chargeback | Detect patterns in chargeback frequency, player history, and behavior, and spot high-risk users for review |
| Casino | Jackpot Hunting | Analyze gameplay patterns and session timing to identify abnormal betting behavior and block users |
| Affiliate Fraud | Track traffic sources, click/sign-up correlations, and spot bot-like activity or tech glitches |
| Sportsbook | After-Goal Betting | Compare the bet timing to event timestamps, and detect improbable win rates immediately after goals/events |
| Match Fixing | Flag betting volume spikes, odds movement mismatches, and bets placed shortly before the event start |
| Arbitrage Betting | Identify unnatural bet patterns, cross-platform timing anomalies, and users with high sure-bet odds |
That’s why even proactive risk management strategies aren’t one-size-fits-all. Each product demands a specific approach aligned with player interaction patterns, payout mechanics, bonus models, and technical limitations.
5. Rare System Optimization
A set-it-and-forget-it attitude has never been an element of successful iGaming risk management. Fraudsters evolve fast. Tactics shift, loopholes emerge, and new attacks strike when you least expect them.Â
According to Global Growth Insights, in 2024 alone, nearly 33% of platforms experienced security breaches that compromised user data or financial integrity. Now, selfie mismatch and synthetic identity fraud are gaining momentum, and they are hitting hard. The consequences are damaged trust, stalled business growth, and hindered user acquisition, especially in emerging markets with low awareness of secure gambling practices.Â
No platform can build long-term player loyalty without strong anti-fraud protection. A comprehensive security strategy is a must. Moreover, it needs constant monitoring and optimization. Audit your system at least once or twice a year, run stress tests, check for blind spots, and make sure it outruns threats.Â
6. Underestimating Fraudsters
This one closely resembles #5. Some operators say AI is just hype and only modernizes attacks we’ve already seen. I disagree. Cybercriminals aren’t just using AI to enhance old tricks. They’re building absolutely new corner cases. Entire teams and organized groups work 24/7 to outfox iGaming systems.
Their goal is simple: to make fraud look clean. With anti-detectors and spoofing tools, they devise a chain of actions to bypass the AI/ML-based fraud prevention system. Such tactics prolong the cycle of fraudulent operations, sidestep blocks, and drain money from platforms before operators even spot the breach. The more time and tech resources fraudsters have, the more damage they cause.
Unless operators start taking the AI’s cybercrime potential seriously, the online gambling industry will remain susceptible to money laundering, terrorist financing, and many other forms of fraud.
7. Slow Data ProcessingÂ
Fraud tactics change every year, but the race for real-time speed never stops. And in this race, operators can’t afford to fall behind.Â
No matter how feature-rich a fraud management solution is, it is already at risk if it processes data slowly. An extra 100 milliseconds of delay may seem insignificant, but it is more than enough to trigger financial and reputational losses. That is why operators must be fast at every moment, in every region, and for every player.Â
But speed isn’t just about hitting high numbers. It’s about retaining the pace and reaction under pressure. Because fraudsters move fast, getting the upper hand immediately when peak loads or traffic spikes slow operators down.
8. Poor Data Quality
One of the biggest challenges in iGaming fraud management today is measuring the effectiveness of AI/ML models.
Predictive risk scoring only works with the models built on large volumes of clean, structured data. If the data quality is poor, the output gets distorted. So do the operators’ decisions. The models also need constant training, which takes time, budget, and development teams.
On the other hand, ML models are trained on historical, not real-time, data. They strive to catch up with the present but eventually become outdated.
However, this doesn’t mean we should step back. Quite the opposite. This is a signal to focus on data quality and consistency more than ever:
- Collect, structure, and cleanse the data;
- Adjust the training logic of ML models to the platform specifics;
- Train and integrate the models into the platform;
- Monitor their efficiency and optimize, if necessary;
- Repeat the cycle every 6–12 months.
When you neglect at least one of these steps, the target performance of fraud detection solutions drops.
9. Insufficient Investments in Fair Play
AI drives full-cycle automation across 90–95% of anti-fraud and risk management activities, processing massive amounts of data in real time. The technology is an ultimate breakthrough, and operators set high hopes on it. But fraud isn’t the only threat to long-run success.
Reputation relies not just on proactive risk mitigation but also on fair, balanced gaming. Today’s players don’t want just secure solutions. They want proof that the game is honest, winning is possible, and the odds aren’t stacked.
Operators who overlook this lose more than revenue. They lose trust. And it’s much harder to regain trust than any payout.
Fair play and responsible gambling should not be some buzzwords. They should become the business fundamentals. Yes, that takes investments, commitment, and transparency. But it seems the iGaming industry is ready to initiate the shift.
Final Thoughts
We are far from saying that AI has solved all risk and fraud management issues. There’s still plenty of space for optimization and upgrades, and the technology will keep evolving. So will fraudulent schemes.Â
Relying on a single solution and expecting it to work forever is no longer an option. AI models need constant retraining and strategic adjustments. The only way to stay ahead is to respond quickly and proactively.
