Fraud in the iGaming sector is no longer the work of lone opportunists. Today’s scammers operate in well-organized, tech-savvy networks – quietly exploiting systems that weren’t built to catch them. And as the digital economy grows, so too does the complexity of fraud schemes targeting gaming operators.
Amid this evolving threat landscape, Frogo has emerged as a company redefining how fraud prevention should work. We spoke with Volodymyr Todurov, CEO at Frogo, to get an inside look at how fraudsters are changing their tactics – and what operators can do to stay ahead.
Fraudsters evolve fast – how does your system stay one step ahead without overwhelming teams with false alarms?
Absolutely, the landscape of fraud is constantly shifting and staying ahead requires more than static rules. At Frogo, we’ve developed a dynamic system that adapts in real-time to user behavior and transaction contexts. Our platform learns from both fraudulent and legitimate activities, enabling it to distinguish between the two more effectively. This approach reduces false positives and ensures that our clients’ teams can focus on genuine threats without being bogged down by unnecessary alerts.
Can you walk us through a real-world case where your platform uncovered a fraud scheme traditional tools missed?
Absolutely. One notable case involved a large-scale bot attack targeting SMS-based fraud vectors. Initially, our standard device ID-based defenses helped neutralize the first wave of the attack. However, the adversaries quickly adapted, altering their emulation tactics to bypass traditional checks. At that point, conventional methods were no longer sufficient to detect the evolving fraud.
We responded by implementing a dynamic anomaly detection framework. This involved redefining detection signals in real-time using IP intelligence and deep device fingerprint attributes – areas where our proprietary data collection algorithms provided a significant edge. By anchoring detection logic to more granular and resilient signals, we were able to recalibrate thresholds dynamically, ensuring legitimate users weren’t impacted.
The results were decisive: bot attack efficiency dropped sharply from over 80% to just 3.5%.
What’s something about fraud detection that most businesses get wrong? And how does Frogo challenge that?
A common pitfall we see is operational rigidity – many businesses rely on static rules and general-purpose triggers that result in high false positive rates. This not only burdens anti-fraud teams with unnecessary manual reviews but also degrades the experience for legitimate users, especially loyal or VIP customers.
For example, it’s typical to see blanket rules like “manually verify all payouts over X euros.” While that may seem prudent, in reality it’s inefficient. It overlooks low-value, high-frequency fraud – such as bonus abuse – and disproportionately flags legitimate high-value players.
At Frogo, we take a different approach. Our system adapts rules dynamically based on customer behavior and segmentation. A trusted VIP user with a long-standing reputation shouldn’t be reviewed multiple times a day. But if a wave of new €5 accounts starts exhibiting bonus-hunting behavior, they should run immediate scrutiny – regardless of transaction size.
By aligning detection logic with behavioral context and player reputation, we reduce noise, increase fraud catch rates, and protect real users from unnecessary friction.
How does Frogo automate risk logic without sacrificing the flexibility businesses need to reflect their unique policies and traffic patterns?
At Frogo, we don’t see automation and customization as opposing forces – they operate in different dimensions. Our focus is on automating the customization of risk and scoring policies in a way that respects each client’s specific risk appetite and user behavior.
We achieve this through dynamic triggers. Rather than hardcoding arbitrary rules – like “five failed top-ups per minute equals fraud” – we apply adaptive scoring thresholds that align with real-world usage patterns.. For example, our system might detect that, for a certain payment method and user segment, more than 1.3 failed top-ups per minute is statistically anomalous – because it exceeds the 98th percentile of historical behavior.
But that same trigger adjusts automatically. If the next day a payment provider experiences a technical issue and normal users start retrying more often, the threshold might shift to 2.7. What was anomalous yesterday may no longer be today – and our system adapts accordingly to reflect evolving traffic patterns.
As a result: the clients retain full control over their risk strategy, while Frogo ensures their policies scale efficiently, adapt in real time, and minimize false positives – even in volatile traffic conditions.
Beyond detection – how does Frogo help companies investigate and understand fraud at a strategic level?
Detection is just the beginning. Frogo’s graph-based forensic tools and AI models provide a comprehensive view of the relationships between accounts, transactions and behaviors. This allows companies to identify patterns and vulnerabilities that might not be apparent through traditional analysis. Our analytics layer offers insights into trends and forecasts, enabling businesses to understand the broader context of fraudulent activities and make informed strategic decisions to mitigate future risks.
Fraud might be getting smarter, but so are the solutions built to fight it. Platforms like Frogo are helping operators move beyond reactive security measures and into a space of strategic, data-informed defense. In an industry where trust is everything, that shift might just be the difference between staying one step ahead – or falling behind.
Disclaimer: Frogo’s fraud prevention solutions are developed in full compliance with applicable data protection laws, including GDPR. All behavioural analysis is performed on anonymised or aggregated data, with full transparency and control provided to our clients.
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