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Web Session "Advanced Concepts of Clustering in Insurance" on 6
May 2022
Cluster analysis is the task of grouping a set of objects (e.g.,
observations, policies, claims) 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?
- How can we identify outliers in our underwriting or claims
process?
The course shows how different algorithms can be used to obtain
a segmentation of insurance data. The methods covered range from
centroid-based (k-means, k-prototypes) to probabilistic (Gaussian
Mixture Models) and density-based (DBSCAN) approaches. We
demonstrate how the clustering results can be visualized and
evaluated. Moreover, it will be shown how the clustering results
can be used to identify outliers in the data set. We also cover
techniques that reduce the dimension of the data so that the
segments can be computed either on aggregated information or using
only a subset of the available information. The course puts an
emphasis on the practical application and therefore showcases all
concepts on an insurance data set.
further details
Web Session "Recent Actuarial Machine Learning Applications: An
Overview" on 31 May 2022
Parallel to the very dynamic development of machine learning
methods in the recent years, the number of applications of the
"learning from data" paradigm in the actuarial area has been
steadily increasing. In this web session we plan to present the
latest implementations of the machine learning methods - primarily
decision tree based models and neural networks - for finding
innovative solutions for the old actuarial problems.
An important criterium for the machine learning applications which
will be presented in the web session is that an actual model with a
company's data has been built.
In the beginning we will give a short introduction of machine
learning and its best known and currently most widely used
algorithms - decision trees derivatives and deep neural networks.
Then we will introduce a few applications by presenting the context
of the actuarial problem and the results. Our intention in this web
session is not to present code or to actually go through the
modelling process, but rather to offer as broad an overview as
possible of the recent applications in the actuarial
departments.
further details
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