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:
- In which customer segments do we mainly generate new business?
- Which typical customer should we have in mind while designing new insurance products?
- How can we make use of granular information, such as diagnose or treatment codes, for example, while dealing with a limited number of observations or claims?
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.
