Curriculum
- 4 Sections
- 19 Lessons
- 1 Day
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- AI/ML Foundations for Regulated Financial Decisioning5
- 1.1ML vs rule-based decisioning: AI enhances risk accuracy and efficiency
- 1.2Core ML model categories: Classification, anomaly detection, supervised vs unsupervised learning
- 1.3Feature engineering, model training, testing, validation, and drift
- 1.4Key evaluation metrics: accuracy, recall, ROC-AUC, precision, false positives/negatives
- 1.5Explainability requirements (XAI), fairness, bias mitigation, and alignment with supervisory expectations
- Practical Application for Credit Scoring & Underwriting5
- 2.1Traditional vs ML-based scoring frameworks: FICO-style vs behavioral and alternate-data models
- 2.2Feature inputs: Bureau data, financial statements, behavioral patterns, device signature signals, employment and segmentation attributes
- 2.3Probability of Default (PD), Loss Given Default (LGD), and risk segmentation modeling
- 2.4Loan pricing and decision policy optimization using model outputs
- 2.5Portfolio recalibration, population stability analysis, and ongoing model risk management
- Real-World Fraud Detection and Behavioral Risk Modelling5
- 3.1Real-time payment fraud detection using anomaly detection and NLP features
- 3.2Synthetic identities, mule accounts, and high-risk behavioral markers
- 3.3Card transaction risk scoring, merchant risk analysis, and cross-channel fraud prevention
- 3.4AI-enabled AML transaction monitoring: pattern analysis and hidden signal discovery
- 3.5Supervisory and audit expectations: control testing, governance, and failure case remediation
- Simulation Labs & Decisioning Experiments4
- 4.1Model Interpretation Lab: Evaluate model outputs and adjust thresholds for accuracy–risk balance
- 4.2Credit Portfolio Simulation: Re-score a portfolio to assess changes in approval, pricing, and risk
- 4.3Fraud Decision Exercise: Prioritize alerts using severity and likelihood scoring
- 4.4Bias & Explainability Review: Detect fairness gaps and prepare a regulator-ready explanation