The purpose of this training is to introduce participants to neural networks for actuarial pricing with a strong emphasis on the practical implementation in a R library.
The training proceed step by step, recalling the fundamental statistical concepts at the heart of tree-based methods. Their relative merits are illustrated by means of several case studies with insurance data.
The aim of this course is to introduce the local and global methods analyzing relations between output and input of complex ML algorithms.
Through the exploration of the modeling of interest rate and inflation, a particular attention is granted to the econometric estimation of the models and to their use in risk management.
This training focuses on recent developments in quantitative finance applied to risk management.
The aim of this session is to cover unsupervised learning techniques for visualizing and analyzing a dataset. This course focuses on actuarial applications of clustering methods.
This training gives you a general knowledge of insurance analytics. You will be able to select the appropriate approach for your own data, run the R code and interpret the results.
Discover the subtleties behind the well-known methods you use to compute the reserves of your company. Understand the Chain Ladder and GLMs you always heard about.
This course focuses on the statistical estimation of static and dynamic mortality models with R.
A concise and comprehensive presentation of stock market ratios and of techniques for analysing short term stock market move.
Get a general understanding on the main problematics linked to cyber-risk evaluation. We will discuss the quality of cyber data, and to develop models to evaluate the risk associated with a cyber contract, and decision tools to determine the perimeter of the guarantee.
