16 Feb 2022
Practical Application of Clustering in Insurance
Actuarial analytics found its way into several areas of the insurance value chain, mostly through the use of tools from supervised learning such as linear or tree-based regression. On the other hand, unsupervised learning, such as partitional clustering, seems to be used rather less despite its potential to gain insights into high-dimensional insurance data sets.
Cluster analysis is the task of grouping a set of objects (often data points) in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. In contrast to simple segmentation (e.g. by geographical location only), Clustering uses several features to differentiate among those groups. Potential applications are manifold and centred around questions such as, for example:
The course provides an introduction into clustering that does not require any previous knowledge in this area and shall give the participant a jump start to work on his/her own problems. Thus we put a focus on typical stumbling blocks arising when clustering techniques are applied in practice such as interpretability, missing values and mixed data types.
The web session is open to all interested persons. Previous knowledge about partitional clustering is not required, however, basic statistical knowledge is recommended. Familiarity with the R programming language would be helpful to follow the practical example.
Technical RequirementsPlease check with your IT department if your firewall and computer settings support web session participation (the programme Zoom is used for this online training). Please also make sure that you are joining the web session with a stable internet connection.
Purpose and Nature
The following topics will be covered:
The theoretical explanations will be accompanied by a practical example in R on a public data set showcasing a typical insurance application.