Centro de Documentacion de Fundación MAPFRE - Cost-sensitive multi-class adaboost for understanding driving behavior based on telematics

Notas: Sumario: Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.Registros relacionados: En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 13/09/2021 Volumen 51 Número 3 - septiembre 2021 , P. 719-751Materia / lugar / evento: Cálculo de probabilidades Conducción Pago por uso Telemática Otros autores: Boucher, Jean-Philippe
Valdez, Emiliano A.
Otras clasificaciones: 322

Derechos: In Copyright (InC)

‌ Ver detalle del número