Actuarial reserving techniques have evolved from the application of algorithms, like the chain ladder method, to stochastic models of claims development, and, more recently, have been enhanced by the application of machine learning techniques.

Despite this proliferation of theory and techniques, there is relatively little guidance on which reserving techniques should be applied and when. In this paper, we revisit traditional reserving techniques within the framework of supervised learning to select optimal reserving models. We show that the use of optimal techniques can lead to more accurate reserves and investigate the circumstances under which different scoring metrics should be used.