Programme

WEB SESSION

4 - 7 Oct 2021

Non-Life Pricing&Profitability Analysis Using ML Techniques with R

Monday, 04 October 2021

09:00 - 10:30 Data Selection, Pre-Analysis and Feature Selection (data quality, pre-treatment, missing values, feature engineering and feature selection)
Overfitting and cross-validation
Example: Data Analysis and filtering
10:30 - 10:45 Break
10:45 - 12:30 Reminders and Q&A about regression tree models
How to fit a machine learning model
Example: Fitting a regression tree and random forest on frequency

Tuesday, 05 October 2021

09:00 - 10:30 Reminders and Q&A about bagging and random forest models
Case Study: Random forest model adjustment for cost
10:30 - 10:45 Break
10:45 - 12:30 Reminders and Q&A about GBM models
Case Study: GBM model adjustment for frequency
Example: Fitting a neural network on the average claim amount

Wednesday, 06 October 2021

09:00 - 10:30 Interpretability of Machine learning techniques
Case study: Features selection, partial dependence plot and Shapley Value
10:30 - 10:45 Break
10:45 - 12:30 Case study: Application of GBM method to highlight interactions
Reminders and example about unsupervised machine learning

Thursday, 07 October 2021

09:00 - 10:30 Profitability analysis: profitability and positioning assessment with ML techniques
Example: profitability analysis with regression trees
10:30 - 10:45 Break
10:45 - 12:30 Competition analysis: understanding competitors prices
Example: Reverse engineering of competitors prices
Client behaviour and Elasticity
(Time zone: CEST - Central European Summer Time)

Seminar Details

Programme

CPD Credits

Participant Feedback

4.1 of 5 Points

Sponsor

PREMIUM Sponsor of the Event