Actuarial Data Science

Bridging the Gap between Actuarial and Data Science

Fri 28 December 2018

PCA from several perspectives

Posted by Pieter Marres in Articles   

Principal Component Analysis (PCA) is a useful technique when you want to get a better understanding of a dataset containing a large number of correlated features. For example, in image processing, PCA is used for data compression. The main objective is to reduce the complexity of the data with a minimum loss of information.


Tue 27 November 2018

autoML: the Holy Grail for the insurance industry?

Posted by Pieter Marres and Mark Verhagen in Articles   

New technologies are transforming the insurance industry. Tomorrow’s winners will be those insurers able to convert vast amounts of data into actionable insights about clients and products alike. An increasing demand for machine learning methods, combined with a growing shortage of data scientists able to create, implement and communicate these methods, call for a data pipeline that is as efficient as possible.