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Linear Modeling for Insurance Ratemaking/Reserving: Modeling Loss Development Factors for Catastrophe Claims

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Catastrophe events occur rather infrequently, but upon their occurrence, can lead to colossal losses for insurance companies. Due to their size and volatility, catastrophe losses are often treated separately from other insurance losses. In fact, many property and casualty insurance

Catastrophe events occur rather infrequently, but upon their occurrence, can lead to colossal losses for insurance companies. Due to their size and volatility, catastrophe losses are often treated separately from other insurance losses. In fact, many property and casualty insurance companies feature a department or team which focuses solely on modeling catastrophes. Setting reserves for catastrophe losses is difficult due to their unpredictable and often long-tailed nature. Determining loss development factors (LDFs) to estimate the ultimate loss amounts for catastrophe events is one method for setting reserves. In an attempt to aid Company XYZ set more accurate reserves, the research conducted focuses on estimating LDFs for catastrophes which have already occurred and have been settled. Furthermore, the research describes the process used to build a linear model in R to estimate LDFs for Company XYZ's closed catastrophe claims from 2001 \u2014 2016. This linear model was used to predict a catastrophe's LDFs based on the age in weeks of the catastrophe during the first year. Back testing was also performed, as was the comparison between the estimated ultimate losses and actual losses. Future research consideration was proposed.

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2018-05

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Automating by Developing Model Components for the Insurance Ratemaking Actuarial Procedures

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The objective of this study is to build a model using R and RStudio that automates ratemaking procedures for Company XYZ’s actuaries in their commercial general liability pricing department. The purpose and importance of this objective is to allow actuaries

The objective of this study is to build a model using R and RStudio that automates ratemaking procedures for Company XYZ’s actuaries in their commercial general liability pricing department. The purpose and importance of this objective is to allow actuaries to work more efficiently and effectively by using this model that outputs the results they otherwise would have had to code and calculate on their own. Instead of spending time working towards these results, the actuaries can analyze the findings, strategize accordingly, and communicate with business partners. The model was built from R code that was later transformed to Shiny, a package within RStudio that allows for the build-up of interactive web applications. The final result is a Shiny app that first takes in multiple datasets from Company XYZ’s data warehouse and displays different views of the data in order for actuaries to make selections on development and trend methods. The app outputs the re-created ratemaking exhibits showing the resulting developed and trended loss and premium as well as the experience-based indicated rate level change based on prior selections. The ratemaking process and Shiny app functionality will be detailed in this report.

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2022-05

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The Effects of Mortality by Socioeconomic Category on Group Life Insurance Rates and Plan Designs in the United States

Description

Recent life tables provided by the Society of Actuaries demonstrate mortality rate estimates for the United States by year from 1982 through 2018, separated by socioeconomic deciles and quintiles. These estimates were utilized to determine how life insurance rates might

Recent life tables provided by the Society of Actuaries demonstrate mortality rate estimates for the United States by year from 1982 through 2018, separated by socioeconomic deciles and quintiles. These estimates were utilized to determine how life insurance rates might vary based on the socioeconomic category of a specific United States county. The aim of this study is to determine how the data provided in these life tables can be utilized to curate life insurance rates and plan designs for employees at a specific company in the United States. The results indicate that there are significant differences in mortality across these socioeconomic quintiles, including greater life expectancy for individuals located in counties of a higher quintile. While there are no limits to the implications of these results in the insurance industry, this report highlights how the demographics of individuals working for a specific company could potentially alter life insurance rates for its employees.

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Date Created
2022-05

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Looking at COVID-19 as a Factor in Insurance Loss Reserving Models

Description

A factor accounting for the COVID-19 pandemic was added to a generalized linear model to more accurately predict unpaid claims. COVID-19 has affected not just healthcare, but all sectors of the economy. Because of this, whether or not an automobile

A factor accounting for the COVID-19 pandemic was added to a generalized linear model to more accurately predict unpaid claims. COVID-19 has affected not just healthcare, but all sectors of the economy. Because of this, whether or not an automobile insurance claim is filed during the pandemic needs to be taken into account while estimating unpaid claims. Reserve-estimating functions such as glmReserve from the “ChainLadder” package in the statistical software R were experimented with to produce their own results. Because of their insufficiency, a manual approach to building the model turned out to be the most proficient method. Utilizing the GLM function, a model was built that emulated linear regression with a factor for COVID-19. The effects of such a model are analyzed based on effectiveness and interpretablility. A model such as this would prove useful for future calculations, especially as society is now returning to a “normal” state.

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2022-05