Bayesian analysis of big data in insurance predictive modeling using distributed computing

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008  170920e20170706bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20140001262‎$a‎Zhang, Yanwei
24510‎$a‎Bayesian analysis of big data in insurance predictive modeling using distributed computing‎$c‎Yanwei Zhang
300  ‎$a‎19 p.
520  ‎$a‎While 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.
650 4‎$0‎MAPA20140022717‎$a‎Big data
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080592059‎$a‎Modelos predictivos
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961