📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Fraud Detection Using AI in Finance, from foundations to a certified capstone project.
Problem Shape
• Extreme class imbalance and why accuracy is a meaningless metric here
• Adversarial drift: fraudsters respond to your model in a way churners do not
• Label delay and the chargeback window that means today's labels are incomplete
Features
• Velocity, recency and aggregation features over sliding windows
• Entity resolution and device or identity graphs
• Graph features: shared attributes, community detection and ring identification
• Preventing leakage from features computed after the event
Models
• Gradient-boosted trees as the practical baseline and why they are hard to beat
• Anomaly detection for novel fraud with no labels yet
• Graph neural networks for organised fraud rings
• Ensembling with rules engines rather than replacing them
Decisioning
• Cost-sensitive thresholds: false-positive friction against fraud loss
• Precision at low alert volumes, because analyst capacity is finite
• Champion/challenger deployment and measuring real uplift
Governance
• SHAP-based reason codes for adverse decisions
• Fairness testing where a false positive freezes a customer's money
• Model documentation and audit trails for financial supervisors
Earn government-registered certification in Fraud Detection Using AI in Finance
e-Certificate and e-Marksheet issued on successful completion.
