This thesis explores the use of machine learning techniques in an effort to increase insurer competitiveness. It asks whether it is possible to accurately estimate the expected financial loss of a given insurance contract and how this information can be used to gain a competitive edge in the business. To answer these questions, some basic principles of insurance are introduced, with a focus on statistical modeling. Furthermore, potentially successful algorithms and techniques are described, like ordinary least squares, generalized linear models (GLMs), generalized additive models, clustering, random forests and gradient boosting trees. It is shown that theory that was originally developed for GLMs, can easily be generalized to other methods, chiefly gradient boosting, with often better results. A new form of evaluation is introduced that helps to rate the efficiency of an insurance portfolio. This, and other metrics are finally applied to several designed models to demonstrate their effectiveness.