Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

dc.contributor.author Freeman, Steven
dc.contributor.author Kakhki, Fatemeh Davoudi
dc.contributor.author Freeman, Steven
dc.contributor.author Mosher, Gretchen
dc.contributor.author Mosher, Gretchen
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-12-12T23:19:34.000
dc.date.accessioned 2020-06-29T22:44:09Z
dc.date.available 2020-06-29T22:44:09Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-12-01
dc.description.abstract <p>Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R2 = 0.79). Injury characteristics and worker’s occupation were predictive of large claims’ occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers’ compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.</p>
dc.description.comments <p>This article is published as Davoudi Kakhki, Fatemeh, Steven Freeman, and Gretchen Mosher. "Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation." <em>Safety</em> 4, no. 4 (2018): 57. DOI: <a href="https://dx.doi.org/10.3390/safety4040057" target="_blank">10.3390/safety4040057</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/977/
dc.identifier.articleid 2262
dc.identifier.contextkey 13429315
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/977
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1798
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/977/2018_Mosher_AnalyzingLarge.pdf|||Sat Jan 15 02:37:18 UTC 2022
dc.source.uri 10.3390/safety4040057
dc.subject.disciplines Agricultural Economics
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords predictive generalized linear models
dc.subject.keywords heavy-tailed distributions
dc.subject.keywords Monte Carlo simulation
dc.subject.keywords insurance risk analysis
dc.title Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isAuthorOfPublication d4270080-adb0-4e32-aa5c-6bce7f39e6a8
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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