Predictive Modelling

Sparse Regression with Multi-type Regularized Feature Modeling

We develop the SMuRF algorithm that accurately and effectively solves the general problem of convex optimization with a differentiable loss function and multi-type penalty terms. We show in full detail how this algorithm creates sparse models, using varying combinations of Lasso-type penalties, and investigating and documenting all possible model choices.

Boosting insights in insurance tariff plans with tree-based machine learning methods

We adapt tree-based machine learning methods to the problem of insurance pricing, thereby leaving the comfort zone of both traditional ratemaking and machine learning. State-ofthe-art GLMs are compared to regression trees, random forests and gradient boosting machines.

Unraveling the predictive power of telematics data in car insurance pricing

A data set from a Belgian telematics product aimed at young drivers is used to identify how car insurance premiums can be designed based on the telematics data collected by a black box installed in the vehicle.

A data driven binning strategy for the construction of insurance tariff classes

A fully data driven strategy to incorporate continuous risk factors and geographical information in an insurance tariff.

Data analytics for insurance loss modelling, telematics pricing and claims reserving.

PhD Thesis

Telematics insurance: the safer you drive, the less you pay

Telematics technology - the integrated use of telecommunication and informatics - may fundamentally change the car insurance industry by allowing insurers to base their prices on the real driving behavior instead of on traditional policyholder characteristics and historical claims information.