Day 1: Introduction, clustering methods and CART
Part 1: What can Predictive Analytics methods and Machine Learning be used for in Insurance?
1.1 Introduction
1.2 Software
1.3 Applications
Part 2: Clustering methods, classification and regression trees
2.1 Clustering and other unsupervised learning methods
2.2 Classification and Regression Trees (CART)
2.3 Applications: credit scoring, auto insurance, fraud detection, credibility
Day 2: The Random Forest and Stochastic Gradient Boosting algorithms
Part 3: Random Forest
3.1 The Random Forest algorithm
3.2 Interpretation of the results.
Part 4: Stochastic Gradient Boosting
4.1 The benefits of the Stochastic Gradient Boosting algorithms
4.2 How Stochastic Gradient Boosting algorithms work
4.3 Interpretation of Stochastic Gradient Boosting output.
Day 3: Multivariate Adaptive Regression Splines
Part 5: Multivariate Adaptive Regression Splines (MARS)
5.1 Introduction to non-linear regression with splines
5.2 The MARS algorithm
5.3 How to use MARS models.
Part 6: Application to credit scoring and insurance problem
Day 4: Risk management for variable annuities
Part 7: Motivational example: variable annuities
7.1 Cash flows
7.2 Risks associated with the guarantees
Part 8: Models for risks underlying variable annuities
8.1 Equity risk models
8.2 Interest rate models
8.3 Mortality risk
8.4 Policyholder behavior risk
8.5 Basis risk
Day 5: Pricing and hedging for variable annuities II
Part 9: Pricing and reserving methods for variable annuities
Part 10: Hedging methods for variable annuities
10.1 Hedging instruments
10.2 Review of various hedging approaches
Part 11: Reinforcement Learning in Finance
11.1 Introduction to Reinforcement Learning
11.2 Applications to variable annuities
