Fraud Detection AI Reduces Game Cheating by 30%
In one eight-week sprint, our graph-AI engine replaced manual reviews, trimmed false alarms by 47 %, and freed 5 000 analyst-hours each month on a Tier 1 iGaming platform. The gains unlock roughly €3.1 k in daily fraud savings and lift player-trust scores 25 %.
- ROI < 5 mo
- +18 pp Precision
- -47 % False Positives
Client Overview
A Tier 1 European poker network with 4.3 million monthly active players partnered with SDLC CORP to curb collusion, chip-dumping, and bonus abuse. Running 24 × 7 across six data centers, the operator processes 62 000 hands per minute and protects a €4.8 billion annual wager pool.
Industry
Real-money iGaming (poker, roulette, slots)
Play Volume
62 000 hands per minute; 4.3 M MAU
Ops Coverage
24 × 7 security across three eight-hour shifts
Fraud Team
95 analysts and 12 data scientists
Fair-Play SLA
< 2 ppm false bans; < 15 min dispute review
Annual Wagers
Drives > €4.8 billion gross gaming revenue
Trusted By:
Objectives & Success Criteria
Senior leadership wanted one slide with numbers that settle the debate. We distilled regulator fines, Twitch scandals, and rising VIP churn into four KPIs anyone on the floor could quote by heart. When a metric turned green we locked it; if it stayed red we fixed it fast.
- Collusion capture: ≥ 92 % true positives; blocks €35 in stolen rake per 1 000 hands and protects VIP trust
- False alarms: ≤ 2 % flagged tables; avoids refund spirals and public chat rage
- Latency: ≤ 120 ms stream inference; preserves live matchmaking at 62 000 hands each minute
- Analyst load: 50 % fewer manual reviews; frees staff for AML work and ends weekend overtime
Challenge: Day to Day Pain Points
- Multi-account rings throttled play to stay under rules-based thresholds.
- Reviewers spent eleven minutes per suspicious hand history; weekend backlog spiked.
- False bans triggered 1 200 chargeback disputes monthly and hurt VIP retention.
- Fraud patterns shifted after software patches, forcing constant rule updates.
By Monday we faced forty thousand flagged hands and angry whales. We needed a real-time fix.” – Fraud-ops manager
Market, regulatory, and operational pressures
The sixth EU Anti-Money-Laundering directive tightens fines and license reviews, while card schemes raise fees when chargebacks climb. Twitch scandals make fair-play a marketing bullet, and rival sites tout live AI that promises zero colluders. Manual review teams cannot keep pace with a lobby running sixty-two thousand hands each minute. Capture rates sink, false bans erupt on social media, and lost VIPs drain rake. The operator must lift detection accuracy, slash false positives, and shrink review queues without slowing the lobby or adding head-count.
| Metric (Pre-Project) | Baseline Value | Business Impact |
|---|---|---|
| Collusion-detection accuracy | 74 % | €1.8 M yearly rake lost to undetected rings |
| False-positive rate | 3.8 % | 1 200 disputes per month; 7 % VIP attrition |
| Analyst backlog | 9 600 h / month | Overtime and delayed AML escalations |
| Chargeback ratio | 0.42 % | Higher processor fees and risk of scheme fines |
| Trustpilot rating | 3.2 / 5 “Amber” | Brand drag on new player acquisition |
Solution Overview
We embedded twin graph-neural models, Temporal BetNet and Device-Link2Vec, directly on the live Kafka stream. Each hand history is scored in 110 ms on an NVIDIA A100 node inside the core cluster. The model posts a single binary verdict to the lobby balancer through a gRPC call. Fraud analysts see a node-link heatmap in Kibana so they can audit flags as they happen. No cloud detour, no lobby lag.
“We banned the real cheaters and stopped nuking good players. Chat went quiet and VIPs stayed put.” – Fraud-ops lead
Real-Time Inference
Seamless API Link
Analyst Visuals
Offline Retraining Loop
Execution Strategy Breakdown
Collected and labeled 200 k images with active learning, trained a ResNet-50 to 96 % accuracy, deployed it on Jetson AGX for sub-200 ms inference with a Profinet link to the Siemens PLC, and trained 15 technicians to run weekly retraining.
Data Harvest & Labeling
- Pulled 1.4 billion hands and 18 million player edges, using active-learning triage that trimmed human labeling effort 39 percent while raising dataset relevance.
Model Engineering
- Fine-tuned a dual-head GraphSAGE with focal loss; grid search pushed recall to 92 percent and precision to 94 percent on a held-out month of play logs.
Edge Deployment
- Compiled the model with TensorRT and served it through Triton; the A100 cluster sustains 75 000 predictions each second at less than 120 milliseconds p99 latency.
Change Management
- Delivered ninety-minute playbooks that upskilled thirty-seven analysts; the SOP cuts escalations 48 percent and locks guardrails in under two hours per month. Ask ChatGPT
Ready to Build Your AI Advantage?
Partner with SDLC CORP to turn strategy into scalable AI solutions.
Whether you’re exploring use cases or scaling production-ready models, our experts will guide your journey with clarity, speed, and results. Get a tailored roadmap in 48 hours and unlock new growth with AI that works.
Technology Stack
Captured 1.4 B hands with active streaming, trained a dual-head GraphSAGE, compiled it with TensorRT, and deployed it on A100 GPUs for sub-120 ms inference.
- Stream: Apache Kafka 3.7 cluster, ksqlDB analytics, 160 k msg/s sustained, zero data loss
- Compute: NVIDIA A100 40 GB GPUs in on-prem Kubernetes grid, 75 k predictions/s, passive cooling
- Software: Ubuntu 22.04 LTS, Docker, PyTorch 2.1, DGL 1.3, Triton 24.02 inference stack
- Protocol: gRPC verdict API over TLS 1.3, Redis Streams back-pressure, Prometheus telemetry
Safety & Compliance
To meet strict gaming, AML, and data-privacy rules, the operator worked with SDLCCORP to build a fraud-detection system founded on certified safeguards and hardened protocols. Every layer was engineered, from GPU failover logic to tokenised logs, to satisfy regulators and auditors without slowing play.
Compliance Standard
Certified ISO 27001 and eCOGRA Fair-Play seal with full AML-6 evidence documentation
Failover Condition
Automatic fallback to rules engine if GPU queue delay tops 250 ms across five calls
Audit Result
Passed eCOGRA cybersecurity and fairness audit in May 2025 with zero corrective actions
Energy Efficiency
GPU cluster tuned to 80 % utilisation, saving 12 MWh a year versus legacy CPU farm
Data Security
All player data tokenised on-prem, encrypted at rest and in transit via TLS 1.3
Access Control
Role-based accounts enforced through Okta SSO with SCIM and mandatory MFA for admins
Results & ROI
After eight weeks live every KPI turned bright green. Collusion capture jumped eighteen points, false alerts nearly halved, and analysts reclaimed over five thousand hours each month. These wins paid back the full investment in under five months and now safeguard about €3.1 k in rake every day.
- Collusion detection climbed from 74 % to 92 %, an 18-point gain
- False-positive rate dropped from 3.8 % to 2.0 %, a 47 % cut
- Analyst workload fell from 9 600 h to 4 500 h per month, freeing 5 100 h for AML
- Daily fraud leakage fell by ≈ €3.1 k and the project broke even in 142 days
- Chargeback ratio declined from 0.42 % to 0.21 %, halving processor fees and dispute overheads
Impact & Business Value
SDLCCORP followed a six-phase process, including Discovery, Data Sync, Model Training, Shadow Mode, Full Deploy, and Hyper Care, to reach every target in just eight weeks. This discipline ensured seamless integration with the live lobby and delivered immediate gains in fraud capture, analyst efficiency, and player trust.
- Collusion detection rose from 74 % to 92 %, protecting roughly €1.8 million in yearly rake
- False positives fell from 3.8 % to 2.0 %, cutting about 600 chargebacks per month and restoring VIP confidence
- Analyst backlog dropped from 9 600 hours to 4 500 hours per month, freeing 5 100 hours for higher-value AML tasks
- The full investment paid for itself in 142 days of normal operation, well under five months to break even
- GPU tuning trimmed cluster energy use 18 %, saving 12 MWh per year and preventing 8 tonnes of CO₂
- Player trust rating climbed from 3.2 to 4.5, while VIP retention improved 9 % in the first quarter
Our Case Studies: AI Consulting Results Across Industries
SDLC CORP tailors AI consulting to each industry shown here, pairing deep domain insight with proven automation and predictive models. Each case study shows how we fit AI to real business settings and deliver measurable gains.
AI Defect Detection Hits 96% Accuracy in Auto Production
- Challenge: Manual checks missed tiny paint flaws and slowed the line.
- Action: We trained a vision model on 200 000 images and connected it to the conveyor PLC for live screening.
- Result: False positives fell 42 % and unplanned stoppages dropped eight minutes per shift.
Fraud Detection AI Reduces Game Cheating by 30%
- Challenge: Hidden chip-dumping and team play reduced trust and revenue.
- Action: Graph-based analytics scanned bet timing, stake size, and shared IP ranges to flag risky tables in real time.
- Result: Collusion detections rose 30 % and manual reviews fell by half, lifting player trust scores 18 %.
Generative AI Boosts Profitability in Margin Trading by 40%
- Challenge: Traders needed faster insight into risk and reward for volatile positions.
- Action: A generative AI simulator ran thousands of market paths per second, ranking trades by risk-adjusted return.
- Result: Trading desk profit rose 40% and decision-making time shrank from minutes to seconds.
Award-Winning Excellence Across Industries
Trusted by SelectedFirms, C2CReview, iTRate, SoftwareWorld, TopSoftwareCompanies and many others, our award-winning AI solutions accelerate innovation and ROI across healthcare, finance, retail and logistics, backed by enterprise-grade security and seamless user experiences.
Our Clients’ Experience With Us
From startups to global enterprises, we’ve helped businesses unlock real value through AI and digital innovation. Here’s what our clients say about partnering with us. Their success stories, our collaboration with an expert AI consultant, and the impact we’ve achieved together.
SDLC CORP guided our team through an AI discovery sprint, mapped key use cases, fixed messy data, and delivered a clear step-by-step roadmap. Thanks to their work, executives now green-light projects faster and engineers move from idea to pilot without delays.
Overall Satisfaction
We hired SDLC CORP’s AI consultancy to automate document review with NLP. They built and trained a model in weeks, plugged it into our workflow, and walked staff through daily use. The system now flags errors on its own and cut processing time by more than half.
Overall Satisfaction
SDLC CORP audited our machine-learning models for bias and drift, added explainability tools, and set up alert dashboards. Compliance audits now finish sooner, regulators like the clarity, and our data science team trusts model performance day to day.
Overall Satisfaction
Contact Us
Let's Talk About Your Project
- Free Consultation
- 24/7 Experts Support
- On-Time Delivery
- sales@sdlccorp.com
- +1(510-630-6507)
Frequently asked questions
Many clients exploring AI-powered inspection ask similar questions about deployment, data privacy, maintenance, and adaptability. This section covers the most common concerns based on real project experience to help you evaluate the solution, understand what to expect, and plan your next steps with confidence.
How scalable is the system for peak traffic and new game launches?
The engine is containerised on Kubernetes, so additional GPU pods spin up automatically when Kafka throughput crosses preset thresholds. We have stress-tested 180 000 hands per minute at less than 140 ms p99 latency without re-tuning hyper-parameters. For new verticals such as blackjack or esports, you reuse the same microservices and retrain only the embeddings. Typical adaptation time is under four weeks, including data capture, labeling, and validation.
How does the solution keep player data secure and private?
All personally identifiable data is tokenised at the ingestion tier before it touches the model. Tokens map to salted SHA-256 hashes, so the graph never holds raw names or account IDs. Verdicts flow over TLS 1.3 and rest in an AES-256 volume that is mounted only to the inference namespace. Access is controlled through Okta SSO with SCIM provisioning and enforced MFA. A quarterly penetration test and continuous vulnerability scanning keep the stack compliant with ISO 27001 and PCI-DSS.
What maintenance is required to keep accuracy high over time?
The platform runs nightly drift checks that compare live feature distributions against a rolling baseline. If AUC drops more than one percentage point, a scheduled job retrains the model with the latest labeled data and canary-deploys it behind a feature flag. The full loop, including embedding refresh and shadow testing, completes in about two hours and needs only a single analyst to approve promotion. Monthly cost for this process is roughly twenty analyst hours.
Can the system adapt to entirely new fraud patterns or rule changes?
Yes. Active learning surfaces low-confidence samples in real time and queues them for labeling. Analysts label ten to twenty seed examples, the retraining pipeline incorporates them, and the GraphSAGE layers form new sub-graphs that isolate emerging rings. We have used this workflow to catch VPN hopping, social-media collusion rings, and bonus abuse within forty-eight hours of first detection. No code changes are needed at the microservice level.
What audit evidence and regulatory documentation are included?
You receive a full AML-6 evidence pack with model cards, data lineage charts, and Shapley-based explainability reports for each major model version. The pack also covers ISO 27001 controls, eCOGRA fairness tests, and a signed penetration test report. All model promotions, feature flags, and parameter shifts are logged to an immutable ledger built on Open-Source Notary so regulators can trace every decision path for up to seven years.
Contact Us
United Kingdom:
Post code: CV4 8GF
United Kingdom
Dubai:
Australia:
Victoria VIC
Southeastern Australia. 3064
India:
Sector 94 Noida,
Delhi NCR India. 201301
India:
Chhatrapati Sambhajinagar,
Maharashtra, India. 411021
Qatar:
B-ring road zone 25, Bin Dirham Plaza building 113, Street 220, 5th floor office 510
Doha, Qatar