Case study

How AB Georgia, part of Flutter CEE, Stopped Fraud Before Withdrawals by Sharing Win Data with SEON

Company

Industry

Use Cases

Payment Fraud

About AB Georgia

AB Georgia, part of Flutter CEE, is one of the largest online betting and gaming platforms in its market, offering casino, sports betting, poker and peer-to-peer games to approximately 200,000 active customers. How fast a player can access their funds is a competitive advantage. That makes every friction point a commercial decision as much as a security one.

The Challenge

The iGaming operator was stuck between two modes: high friction with low risk, or low friction with high risk. They moved between the two day by day and never landed in the middle.

“We were either high friction, low risk or low friction, high risk. We never achieved the middle stage. The optimal balance zone.”

Director of Service Operations

The goal was straightforward. Only risky users should experience friction. Legitimate players should move through the platform without interruption. But the internal tools couldn’t deliver that precision. Manual reviews were slow. Legitimate customers were getting caught up in checks they didn’t deserve. Fraud managers were spending their time reviewing alerts instead of building prevention strategies.

Three pressures were converging at the same time. Competitors were raising the bar on player experience. Regulators were tightening oversight. And fraud was growing in both volume and sophistication.

Starting Small and Growing

The implementation started small, by design. The operator began by sharing withdrawal and transaction data with SEON, then expanded as confidence in the system grew. The decision to share gaming win data came later and proved to be the most valuable addition.

Screening Wins Before Withdrawals

Most fraud prevention in iGaming focuses on transactions: deposits, withdrawals and payments. AB Georgia went a step further. When a customer wins in a peer-to-peer game, that event is reported to SEON in real time. SEON then applies a set of predefined velocity rules against the win data before any withdrawal is initiated.

The logic is deliberate. If a customer is winning in a P2P game repeatedly within a short window, SEON captures it and queues it for review. It’s not a decline. Meanwhile, the fraud team has time to assess the case and block the winnings before a withdrawal request ever arrives. By catching suspicious activity between the win and the withdrawal, the operator stops fraud without the player ever experiencing friction.

“Not many providers support something like this. They retrieve this data from us, like a real transaction, but it is a win. And then SEON applies the rules we predefined.”

Director of Service Operations
Protect Players and Revenue From Day One

From bonus abuse to coordinated bot rings, SEON gives operators the visibility to act on threats before they hit the bottom line.

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Catching a Fraud Ring Before the Game Provider Did

The most significant outcome was a single fraud event the operator prevented before it escalated. A coordinated fraud ring was exploiting a game malfunction at one of their casino providers. AB Georgia’s SEON-powered rules caught the activity before the provider’s own fraud team identified the issue.

It was the device data that exposed the ring. Fraudsters operating across multiple countries were connected through shared device identifiers, a link that would have gone undetected without the data enrichment SEON provides.

“We caught this case before the gaming fraud team did. Sharing win data with SEON gave us this opportunity.”

Director of Service Operations

Protecting the Player Experience

AB Georgia’s UX team was involved throughout. In a market where fast payouts are a differentiator, every transaction that goes into a pending state when it doesn’t need to is a problem. The team worked alongside SEON to minimize unnecessary pending states. Where a review is required, players receive clear messaging about what is happening and why, keeping trust intact even when a check is in progress.

From Detection to Prevention

The shift from detection to prevention took approximately one year. During that time, the operator accepted that certain edge cases would get through while the behavioral rules matured. That was a deliberate trade-off. The alternative was continuing to impose friction on every player.

Over time, the team added more behavioral and velocity rules, moving from reactive alert review to proactive fraud prevention. Today, the fraud team focuses on prevention strategy rather than manual case review.

Results

Since going live, AB Georgia has tracked consistent improvement across its key fraud and operations metrics

  • Prevented a coordinated fraud ring that would have been costly to gross gaming revenue
  • Two fraud officers per shift now handle the full manual review workload for approximately 200,000 active customers
  • Decline rate improved since implementation
  • False positive rate dropped, meaning fewer legitimate players are affected by unnecessary checks
  • Detection rate continues to climb as the team adds more behavioral rules
  • Fraud managers shifted from manual review to prevention strategy

Conclusion

AB Georgia didn’t solve its friction problem by adding more rules or more reviewers. They solved it by rethinking what data they shared and when. Sending win data to SEON, not just transaction data, gave them visibility into fraud that most operators don’t have. Acting on that data before a withdrawal is initiated means the player never feels the friction. The fraudster gets stopped. The legitimate player gets paid fast.


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