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BAYESIAN ANALYSIS OF BIG DATA IN INSURANCE PREDICTIVE MODELING USING DISTRIBUTED COMPUTING

Published online by Cambridge University Press:  06 July 2017

Yanwei Zhang
Yanwei Zhang*
Affiliation:
Uber Technologies, 1455 Market Street, San Francisco, CA 94103, USA
*
E-Mail: actuary_zhang@hotmail.com
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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.

Keywords

Bayesianbig datadistributed computingpredictive modelingratemakingSpark
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 47 , Issue 3 , September 2017 , pp. 943 - 961
Copyright
Copyright © Astin Bulletin 2017 

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