A Review of ML Techniques for Vehicle Symbols Determination

Vehicle Symbols group vehicle types into homogeneous clusters for motor insurance ratemaking. Despite their relevance in the motor insurance industry, little attention has been paid to their determination throughout actuarial literature. The paper reviews existing approaches and presents suitable machine learning algorithms that can be used to define vehicle symbols based on claim frequency as risk measure. Such methods will be contrasted in terms of predictive performance. An empirical section illustrates the application of the discussed techniques using open-source software and freely available data sources. The analysis found that a supervised approach based on Classification and Regression Trees (CART) outperformed the unsupervised approaches, however some latent features highlighted by Generalized Low Rank Models (GLRM) and Autoencoders showed interesting and relatively competitive performances. Address for Correspondence: spedicato_giorgio@yahoo.it