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

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      <subfield code="a">Zhang, Yanwei</subfield>
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      <subfield code="a">Bayesian analysis of big data in insurance predictive modeling using distributed computing</subfield>
      <subfield code="c">Yanwei Zhang</subfield>
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      <subfield code="a">19 p.</subfield>
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      <subfield code="a">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.</subfield>
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      <subfield code="a">Big data</subfield>
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      <subfield code="a">Matemática del seguro</subfield>
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      <subfield code="a">Modelos predictivos</subfield>
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      <subfield code="t">Astin bulletin</subfield>
      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
      <subfield code="x">0515-0361</subfield>
      <subfield code="g">01/09/2017 Volumen 47 Número 3 - septiembre 2017 , p. 943-961</subfield>