Clemente, Carina de MirandaGuerreiro, Gracinda Rita DiogoBravo, Jorge Miguel2024-02-092024-02-092023-10-212183-489XPURE: 83055137PURE UUID: 889e1a2f-0bfd-418f-8d47-68b61c05d47eScopus: 85187545973ORCID: /0000-0002-7389-5103/work/152758649http://hdl.handle.net/10362/163312Clemente, C. D. M., Guerreiro, G. R. D., & Bravo, J. M. (2023). Gradient Boosting in Motor Insurance Claim Frequency Modelling. In CAPSI 2023 Proceedings (pp. 53-69). Article 5 (Atas da Conferência da Associação Portuguesa de Sistemas de Informação). Associação Portuguesa de Sistemas de Informação. https://doi.org/10.18803/capsi.v23.53-69 --- This research was funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 -- Center for Mathematics and Applications -- (G. R. Guerreiro) and grants UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020 -- BRU-ISCTE-IUL -- (J. M. Bravo).Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. This paper investigates the predictive performance of Gradient Boosting with Decision Trees as base learners to model the claim frequency in motor insurance using a private cross-country large insurance dataset. The Gradient Boosting algorithm combines many weak base learners to tackle conceptual uncertainty in empirical research. The findings show that the Gradient Boosting model is superior to the standard Generalised Linear Model in the sense that it provides closer predictions in the claim frequency model. The finding also shows that Gradient Boosting can capture the nonlinear relation between the claim counts and feature variables and their complex interactions being, thus, a valuable tool for feature engineering and the development of a data-driven approach to risk management.18680731engGradient BoostingNon-life Insurance PricingExpert systemsPredictive modellingRisk ManagementInformation Systems and ManagementManagement Information SystemsManagement of Technology and InnovationInformation SystemsComputer Science Applicationsconference object10.18803/capsi.v23.53-69https://www.scopus.com/pages/publications/85187545973