Zvezdov, Ivelin (2017): Competitive Premium Pricing and Cost Savings for Insurance Policy Holders: leveraging Big Data.
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Abstract
Examining the intersection of research on the effects of (re)insurance risk diversification and availability of big insurance data components for competitive underwriting and premium pricing is the purpose for this paper. We study the combination of physical diversification by geography and insured natural peril with the complexity of aggregate structured insurance products, and furthermore how big historical and modeled data components impact product underwriting decisions. Under such market conditions, the availability of big data components facilitates accurate measurement of inter-dependencies among risks, and the definition of optimal and competitive insurance premium at the level of the firm and the policy holders. We extend the discourse to a notional micro-economy and examine the impact of diversification and insurance big data components on the potential for developing strategies for sustainable and economical insurance policy underwriting. We review concepts of parallel and distributed algorithmic computing for big data clustering, mapping and resource reducing algorithms.
Item Type: | MPRA Paper |
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Original Title: | Competitive Premium Pricing and Cost Savings for Insurance Policy Holders: leveraging Big Data |
Language: | English |
Keywords: | Effects of insurance risk diversification on premium definition; contribution of big data components to measuring inter-dependencies; rational for sustainable and economic underwriting practices and cost savings |
Subjects: | C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief ; Unawareness G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies |
Item ID: | 77502 |
Depositing User: | Ivelin Zvezdov |
Date Deposited: | 16 Mar 2017 06:26 |
Last Modified: | 29 Sep 2019 14:08 |
References: | Ayma, V. A. et al (2015) Classification of Algorithms for Big Data Analysis, A Map Reduce Approach, Remote Sensing and Spatial Information Sciences Conference, May 2015 Fedak, Jilles (2013) MapReduce Runtime Environments, INRIA, University Of Lyon, France. Goovaerts, Mark, Laeven Roger (2011) Premium Calculation and Insurance Pricing Hurlimann, Werner (2006) On a Robust Parameter Free Pricing Principle: Fair Value and Risk Adjusted Principle Jin, B. X, et al (2015) Building Spatiotemporal Cloud Platform for Supporting GIS Application, International Workshop on Spatiotemporal Computing, June 2015 Isaac, Luke P. (2014) Basics of Map Reduce Algorithm Explained with a Simple Example, Geek Stuff, May 2014 Nandakumar, A. N. et al (2014) A Survey of Data Mining Algorithms on Apache Hadoop, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 1, January 2014 Nivranshu, Hans, (2015) Big Data Clustering Using Genetic Algorithm On Hadoop MapReduce, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 04, April 2015 Rau Chaplin, Andrew (2015) Scaling up to Big Data: Algorithmic Engineering and HPC, Statistical and Computational Analytics for Big data Conference 2015 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/77502 |