Matching Items (6)

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Using Generalized Linear Models to Develop Loss Triangles in Reserving

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The use of generalized linear models in loss reserving is not new; many statistical models have been developed to fit the loss data gathered by various insurance companies. The most popular models belong to what Glen Barnett and Ben Zehnwirth

The use of generalized linear models in loss reserving is not new; many statistical models have been developed to fit the loss data gathered by various insurance companies. The most popular models belong to what Glen Barnett and Ben Zehnwirth in "Best Estimates for Reserves" call the "extended link ratio family (ELRF)," as they are developed from the chain ladder algorithm used by actuaries to estimate unpaid claims. Although these models are intuitive and easy to implement, they are nevertheless flawed because many of the assumptions behind the models do not hold true when fitted with real-world data. Even more problematically, the ELRF cannot account for environmental changes like inflation which are often observed in the status quo. Barnett and Zehnwirth conclude that a new set of models that contain parameters for not only accident year and development period trends but also payment year trends would be a more accurate predictor of loss development. This research applies the paper's ideas to data gathered by Company XYZ. The data was fitted with an adapted version of Barnett and Zehnwirth's new model in R, and a trend selection algorithm was developed to accompany the regression code. The final forecasts were compared to Company XYZ's booked reserves to evaluate the predictive power of the model.

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2018-05

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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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Healthcare Cost Trend Impacts by COVID-19 in the State of Arizona

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Actuaries can analyze healthcare trends to determine if rates are reasonable and if reserves are adequate. In this talk, we will provide a framework of methods to analyze the healthcare trend during the pandemic. COVID-19 may influence future healthcare cost

Actuaries can analyze healthcare trends to determine if rates are reasonable and if reserves are adequate. In this talk, we will provide a framework of methods to analyze the healthcare trend during the pandemic. COVID-19 may influence future healthcare cost trends in many ways. First, direct COVID-19 costs may increase the amount of total experienced healthcare costs. However, with the implementation of social distancing, the amount of regularly scheduled care may be deferred to a future date. There are also many unknown factors regarding the transmission of the virus. Implementing epidemiology models allows us to predict infections by studying the dynamics of the disease. The correlation between infection amounts and hospitalization occupancies provide a methodology to estimate the amount of deferred and recouped amounts of regularly scheduled healthcare costs. Thus, the combination of the models allows to model the healthcare cost trend impact due to COVID-19.

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2021-05

To Retire or Not to Retire: Will pension plans keep their promise when the time comes?

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Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions,

Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions, enabled by weak funding and risk management policies, poses uncertainty for the retirement of many. The cost of paying pension benefits rises as defined benefit pension plans become increasingly underfunded, burdening the employers who continue to pay them. However, without increasing these already unaffordable pension benefits alongside inflation, they become less valuable to retirees. As pension benefits lose their value and the costs of retirement, such as healthcare and assisted living, increase, defined benefit pension plans may not provide the retirement security that was once promised.

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2022-05

US Healthcare's Spending Problem: A Deep Dive into Why Americans Pay More for Treatment without Better Outcomes

Description

The United States spends far more on healthcare than other developed countries, and it is increasing at a rapid pace that places intense financial pressure on the American public. The high levels of spending are not attributable to increased quality

The United States spends far more on healthcare than other developed countries, and it is increasing at a rapid pace that places intense financial pressure on the American public. The high levels of spending are not attributable to increased quality of care or a healthier general population. Rather, the culprits are a combination of uniquely American social and cultural factors that increase the prevalence of chronic illness coupled with a large and complex healthcare industry that has a multitude of stakeholders, each with their own motivations and expense margins that inflate prices. Additionally, rampant lack of transparency, overutilization and low-quality care contribute to unnecessarily frequent and expensive payments. Public and private institutions have implemented legislation and programs that provide temporary relief, but powerful lobbying efforts by healthcare-related organizations and a general American aversion to high government involvement have prevented the United States from creating effective, long-lasting reform.

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2022-05

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

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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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2022-05