General Insurance Spring Conference 2024: Measuring loss reserving uncertainty with machine learning models

Fri 26 Apr 2024 -
09:30 - 10:30

Interest in the use of machine learning (ML) in reserving has been increasing in recent years, though its use in practice is not yet widespread. ML methods must tick several boxes before actuaries will feel comfortable deploying them. These include model stability, interpretability, ease of use, and the ability to estimate reserve uncertainty.

This talk focuses on this last topic and considers:

  • components of loss reserving error
  • how ML allows us to quantitatively estimate a greater proportion of the total loss reserving error than for example the Mack model or traditional bootstrapping approaches
  • how to use bootstrapping with regularised regression models to obtain these estimates
  • issues faced in practice when bootstrapping ML models and how to deal with them

This is an online presentation aimed at reserving actuaries with an interest in the use of ML or actuaries interested in uncertainty estimation. Full R code for the analysis will be available online in the form of a detailed worked example on some real-life data.

Featured Speakers

An actuary for over 20 years, Gráinne has extensive experience with the use of statistical and machine learning techniques in reserving, both through her work and as a member of the Machine Learning in Reserving Working Party. She has a particular interest in the estimation of reserve variability, and ways to practically use stochastic reserving methods in day-to-day work. She has delivered a number of presentations on these topics over the course of her career. She currently works with Optum Ireland.

Pricing and booking information

Members Book for free
Non-members £45