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

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<title>Bayesian analysis of big data in insurance predictive modeling using distributed computing</title>
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<abstract displayLabel="Summary">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.</abstract>
<note type="statement of responsibility">Yanwei Zhang</note>
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<topic>Big data</topic>
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<topic>Matemática del seguro</topic>
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<topic>Modelos predictivos</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
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<text>01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961</text>
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