This thesis aims to improve the current risk classification for (company) car insurance at Achmea, focusing on WAM and ARD coverages. By using cluster analysis, specifically K-prototypes and spectral clustering, policyholders are grouped based on zip codes and license plates to enhance the current claim frequency models (and thus premium pricing models). The application of spectral clustering (with U-SPEC as the observation reduction technique) led to significant improvement of the current claim frequency GLM for the ARD coverage. This thesis highlights the potential of cluster analysis in actuarial science, offering new methods for mixed data types and high-dimensional clustering, thus providing a foundation for more accurate claim frequency models. The time stabilities and stabilities with respect to the number of observations of the clusters are also investigated.

In this report a new approach to (forensic) handwriting analysis is presented; score-based likelihood ratio (SLR) systems are employed and their quality of performance is studied. These systems compare elements of handwriting based on their characteristics and give an insight into the degree of uncertainty of the statement that two writings have the same writer. They can be used in forensic and fraud investigations.