Data analytics for non-life insurance pricing

These notes aim at giving a broad skill set to the actuarial profession in insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding. We then present several machine learning techniques such as regression trees, bagging, random forest, boosting machines and neural networks. Finally, we provide methodologies for analysing telematics car driving data from unsupervised learning