AI Case Study · Fraud & Anomaly Detection

Fraud Detection AI Reduces Game Cheating by 30%

A Tier-1 European poker network processing 62,000 hands per minute needed to detect collusion, chip-dumping and bonus abuse without slowing live play or flooding analysts with false alerts.

Graph Machine LearningReal-Time Risk ScoringAnomaly DetectionGPU Inference

92%Collusion capture

-47%False positives

5,100hAnalyst hours saved / month

142 daysPayback period

Project Snapshot

Real-time fraud intelligence for a high-volume gaming network

The implementation had to improve fair-play controls without increasing player latency or expanding the fraud-operations team.

Client EnvironmentTier-1 European poker network

Monthly Active Players4.3M

Live Throughput62,000 hands/min

Delivery Window8 weeks

Primary ML PatternGraph ML + anomaly detection

Primary Outcome92% collusion capture

Client Context

Fair-play controls had to work at live-game speed

The operator served 4.3 million monthly active players across six data centers and protected a €4.8 billion annual wager pool. Rules-based controls could not keep pace with coordinated multi-account behavior and changing fraud patterns.

Scale

62,000 hands per minute across a 24×7 environment.

Fraud Operations

95 analysts and 12 data scientists reviewing suspicious play.

Business Constraint

Detection had to improve without adding lobby latency or headcount.

Baseline

The operating model was already overloaded

Before the project, fraud detection accuracy, analyst workload and player trust were all moving in the wrong direction.

74%Collusion-detection accuracy

3.8%False-positive rate

9,600hAnalyst backlog / month

0.42%Chargeback ratio

3.2 / 5Player trust rating

“By Monday we faced forty thousand flagged hands and angry whales. We needed a real-time fix.”

Success Criteria

The model had to improve detection without hurting live play

Targets were defined before deployment so the team could judge the system on operational outcomes, not model accuracy alone.

≥ 92%Collusion capture

≤ 2%False-positive rate

≤ 120 msStream inference

50% fewerManual reviews

Solution Architecture

Graph intelligence moved directly into the live event stream

The system scored every hand, device relationship and account interaction in the core gaming environment rather than pushing decisions into a slow manual queue.

01 · StreamKafka ingests bets, device signals, IP changes and seating events.

02 · GraphTemporal BetNet and Device-Link2Vec model relationships between players and devices.

03 · ScoreGPU inference returns a risk verdict in about 110 ms.

04 · ActgRPC sends the result to the lobby balancer for eject, shadow-ban or step-up KYC.

05 · ReviewKibana graph views let analysts trace linked accounts and chip flow.

Implementation

Eight weeks from discovery to live protection

Data & Model Engineering

1.4 billion hands were captured through active streaming and used to train a dual-head GraphSAGE model.

Production Inference

The model was compiled with TensorRT and deployed on NVIDIA A100 GPUs for sub-120 ms inference.

Drift & Retraining

A refresh pipeline ingests disputes plus recent play logs, rebuilds embeddings and redeploys weights before the next operating cycle.

Services & Capabilities

Capabilities used in the engagement

The project combined model engineering, production integration and operational monitoring rather than treating fraud detection as a standalone model.

Machine LearningGraph ML, anomaly detection and risk scoringExplore service →IntegrationKafka, gRPC and live-lobby workflow integrationExplore service →MLOpsModel refresh, deployment and production monitoringExplore service →AI EngineeringEnd-to-end production AI deliveryExplore service →

Results

The project improved detection without slowing the lobby

74% → 92%Collusion detection

3.8% → 2.0%False-positive rate

9,600h → 4,500hMonthly analyst workload

≈ €3.1k/dayFraud leakage avoided

3.2 → 4.5Player trust rating

Technology

Production stack

Apache Kafka 3.7ksqlDBPyTorch 2.1DGL 1.3TensorRTNVIDIA TritonA100 GPUsgRPCRedis StreamsPrometheusKibana

Controls

Auditability stayed inside the design

Rules-engine fallback if GPU queue delay exceeds the operating threshold.
Tokenised logs and role-based access through Okta SSO and MFA.
On-prem processing to keep live event data inside the operator environment.
Published case study reports an eCOGRA cybersecurity and fairness audit with zero corrective actions.

Machine Learning & Fraud Detection

Build a risk-scoring system around your real event stream

Connect graph ML, anomaly detection and real-time scoring to the workflow where investigators or applications need the decision.

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