Data Analytics for Insurance and Actuarial Science: A Corporate Training Curriculum on Loss Reserving, GLM Pricing, and IFRS 17 Compliance

This project comprised the design and execution of an intensive 5-day executive corporate training program delivered in Lilongwe/Blantyre, Malawi, targeted at banking, financial services, and insurance (BFSI) professionals. The curriculum bridged fundamental insurance domain knowledge with quantitative actuarial modeling, predictive analytics, and executive visual reporting using computational workflows in Python, R, and Excel/Power BI. The 10-module pedagogical architecture covered the complete insurance analytics lifecycle: Actuarial Reserving & Loss Development: Implemented deterministic loss triangles, Chain Ladder methods, and Bornhuetter-Ferguson estimators to calculate Incurred But Not Reported (IBNR) loss reserves and perform stress-testing scenarios. Generalized Linear Models (GLMs) & Risk Pricing: Constructed rate-making models using Poisson (claim frequency) and Gamma (claim severity) link functions, evaluating model lift, Gini coefficients, and cross-validated out-of-sample performance to mitigate data leakage. Predictive Machine Learning & Telemetry: Built classification and ensemble models (Logistic Regression, Decision Trees, Random Forests) in R/Python to forecast policyholder churn, detect fraudulent claims clusters, and quantify large-loss risks. Regulatory Frameworks & Executive Dashboards: Addressed IFRS 17 / Solvency II compliance requirements, algorithmic pricing ethics, and data privacy regulations, culminating in interactive Power BI/Tableau reporting boards for executive decision-making.