AI & Machine Learning Platform
Betfred's data platform has been migrated from SQL Server to Amazon Redshift, creating the foundation for advanced analytics. This initiative builds an enterprise ML platform on top of that foundation, transforming rule-based processes into intelligent, self-learning systems across five high-impact use cases.
Where Betfred Stands Today: Rules, Not Intelligence
Betfred's current operations rely on rule-based systems, manual processes, and batch analytics. While the Redshift migration provides the data infrastructure, the analytical capabilities remain limited to retrospective reporting rather than predictive intelligence.
Capability Gap Analysis
Data Platform Transformation: Toggle to Compare
Switch between Betfred's current analytics stack and the proposed AI/ML platform to see the transformation.
Interactive Architecture View
Total Cost of Ownership: Current vs. AI Platform
Annual Operational Cost Comparison
| Cost Category | Rule-Based (Current) | AI/ML Platform (Future) | Annual Saving |
|---|---|---|---|
| Fraud LossesRule-based detection → real-time ML scoring | £2,400,000 | £960,000 | £1,440,000 |
| KYC Manual Review Team6 FTE reviewers → 1 FTE for edge cases only | £360,000 | £60,000 | £300,000 |
| Regulatory Fine RiskReactive compliance → proactive ML monitoring | £825,000 | £50,000 | £775,000 |
| Marketing WasteBroad segments → individual-level targeting | £1,200,000 | £720,000 | £480,000 |
| AWS SageMaker InfrastructureTraining + inference compute, Feature Store | £0 | £420,000 | -£420,000 |
| ML Engineering Team4 ML engineers + 1 MLOps engineer | £0 | £480,000 | -£480,000 |
| Manual Trader AdjustmentsManual odds management → ML-assisted | £280,000 | £80,000 | £200,000 |
| Compliance ReportingManual SAR filing → automated generation | £180,000 | £40,000 | £140,000 |
| Total Annual Cost | £5,245,000 | £2,810,000 | £2,435,00046% reduction |
Net Annual Value
Including £4.2M revenue uplift from personalisation and odds optimisation, the total annual value exceeds £6.6M.
Five Use Cases: Current State → Future State
Each use case shows where Betfred is today, what the ML-powered future looks like, the specific models involved, and the business value delivered.
Responsible Gambling & Player Protection
Critical PrioritySelf-exclusion lists and basic deposit limits. Compliance team manually reviews flagged accounts. Reactive intervention only.
Real-time behavioural anomaly detection using LSTM autoencoders on session data. Proactive intervention before harm occurs.
Behavioural Anomaly Detector
Risk Score Predictor
Churn-to-Harm Classifier
| Risk Score | Risk Level | Automated Action |
|---|---|---|
| 0–30 | Low | Standard monitoring, no intervention |
| 31–60 | Medium | Soft nudge: 'Take a break?' messaging, cooling-off suggestion |
| 61–80 | High | Mandatory cooling-off period, reduced deposit limits, compliance alert |
| 81–100 | Critical | Account suspension, compliance review, UKGC notification |
Hyper-Personalisation & CLV Optimisation
High Priority5 broad customer segments with generic promotional offers. Same bonus for all customers in a segment. Low conversion rates.
Individual-level propensity models predicting next-best-action for each customer. Dynamic offer generation based on real-time behaviour.
CLV Predictor
Next-Best-Action Engine
Churn Predictor
Fraud Detection & Prevention
Critical PriorityRule-based fraud system with ~200 static rules. Manual investigation queue. 2–4 hour average detection time for new fraud patterns.
Real-time ML scoring on every transaction. Graph neural networks for multi-accounting detection. Automated case management.
Transaction Scorer
Multi-Account Detector
Bonus Abuse Classifier
Automated KYC & AML Compliance
High PriorityManual document review averaging 48 hours. 35% customer dropout during verification. Periodic batch AML screening.
AI-powered 12-second verification pipeline. OCR + facial matching + sanctions screening in a single automated flow.
Document OCR Engine
Facial Comparison
AML Risk Scorer
Dynamic Odds Optimisation
Medium PriorityStatic margin models with manual trader adjustments. Traders react to market movements rather than anticipating them.
ML models predicting market movements and automatically adjusting odds within trader-defined guardrails.
Market Movement Predictor
Margin Optimiser
Redshift → SageMaker: Leveraging Your Investment
The SQL Server to Redshift migration created the data foundation. This pipeline extends it into a full ML platform, with Redshift as the feature engineering source feeding SageMaker for model training and real-time inference.
Training Infrastructure
Inference Infrastructure
Future Solution: What Betfred Will See
End-State Vision
AI Platform Dashboard
Centralised command centre showing all five ML models, their health status, prediction volumes, drift metrics, and business KPIs. Operators can view model performance trends, trigger retraining, and manage A/B experiments from a single interface.