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Bayesian Analysis Of Big Data In Insurance Predictive Modeling Using Distributed Computing

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  • Zhang, Yanwei

Abstract

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.

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  • Zhang, Yanwei, 2017. "Bayesian Analysis Of Big Data In Insurance Predictive Modeling Using Distributed Computing," ASTIN Bulletin, Cambridge University Press, vol. 47(3), pages 943-961, September.
    • Handle: RePEc:cup:astinb:v:47:y:2017:i:03:p:943-961_00
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    Cited by:

    1. Hamza Abubakar & Muhammad Lawal Danrimi, 2023. "A simulation study on the insurance claims distribution using Weibull distribution," Economic Analysis Letters, Anser Press, vol. 2(3), pages 67-79, July.

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