AI Fraud Detection
Enable practitioners to understand how AI detects fraud, set and tune detection so that genuine customers are not burdened, monitor models for drift, investigate alerts properly and govern AI fraud controls so they can be reviewed and defended.
Upcoming course. LMS delivery and enrolment are not open. Release dates will be confirmed.
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Planned learning outcomes
- Explain how rules, anomaly detection and supervised fraud models work together in a layered defence
- Evaluate a fraud model's data, labels, features and explainability before it is relied on
- Measure and tune false positives and missed fraud using precision, recall and cost
- Design monitoring that detects data drift, concept drift and silent system failures
- Apply sound investigation practice to AI alerts, including reporting timeframes and tipping off
- Conduct a fraud detection control review with evidence-based findings and recommendations