Centro de Documentacion de Fundación MAPFRE - Bayesian analysis of big data in insurance predictive modeling using distributed computing

Descripción física: 19 p.Notas: Sumario: While Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling.Registros relacionados: En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961Materia / lugar / evento: Big data Matemática del seguro Modelos predictivos Otras clasificaciones: 6

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