Lunch&Learn | Insurance Analytics: clustering techniques

Lunch & Learn

This webinar proposes to discuss the results of our Detranote Insurance analytics with k-means and extensions.

It will be given in French with English materials by our Scientific Director, Donatien Hainaut, Ph.D

The k-means algorithm and its variants are popular techniques of clustering. Their purpose is to uncover group structures in a dataset. In actuarial applications, these methods detect clusters of policies with similar features and allows to draw a map of dominant risks. This L&L starts with a review of the k-means algorithm and develops next two extensions to manage categorical features. We present a mini-batch version that keeps computation time under control when analysing a high-dimensional dataset. We next introduce the fuzzy k-means in which policies can belong to multiple clusters. Finally, we conclude by a detailed introduction to spectral clustering.

About the speaker

Donatien Hainaut is Scientific Director at Detralytics Donatien and professor at UCLouvain where he is Director of the new Master program in Data Science, statistical orientation. Prior to this he held several positions as associate professor at Rennes School of Business and the ENSAE in Paris. He also has several field experiences having worked as Risk Officer, Quantitative Analyst and ALM Officer.