Sparse predictive modeling techniques with applications in insurance pricing and mortality forecasting KU Leuven
The insurance sector relies heavily on data for a variety of their operational processes such as product pricing, marketing and estimating future expenses. As today's society generates data more rapidly than ever before, the demand for new algorithms, able to infer meaningful information from this data, is rising. A modern issue in insurance is that data sets not only contain a lot of observations, but also many variables of different types. ...