Date(s) - 11/12/2020
8 h 30 min - 10 h 00 min
Catégories Pas de Catégories
The DIALog Chair invites you to its first
on Friday December 11th 2020 at 8:30am
On the theme: “Boosting insights in insurance tariff plans with machine learning methods”
This talk puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, present visualization tools to obtain insights from the resulting models and evaluate the economic value of the resulting tariff plans with proper tools. We also shed light on latest research, introducing the R package and procedure called maidrr: a procedure to develop a Model-Agnostic Interpretable Datadriven suRRogate model for highly regulated industries. Insights are extracted from a black box model via partial dependence effects. These are used to perform smart feature engineering, resulting in a segmentation of the feature space with automatic feature selection. A transparent GLM is fit using the features in a categorical format and their relevant interactions.
Link to the presentation of Katrien Antonio: link
Registration deadline : Thursday December 10th 5:00pm
Do not hesitate to contact us if you have any questions: firstname.lastname@example.org.
Les réservations sont closes pour cet évènement.