The Mack model and the Bootstrap Over-Dispersed Poisson model have long been the primary modeling tools used by actuaries and insurers to forecast losses. With the emergence of faster computational technology, new and novel methods to calculate and simulate data are more applicable than ever before. This paper explores the use of various Bayesian Monte Carlo Markov Chain models recommended by Glenn Meyers and compares the results to the simulated data from the Mack model and the Bootstrap Over-Dispersed Poisson model. Although the Mack model and the Bootstrap Over-Dispersed Poisson model are accurate to a certain degree, newer models could be developed that may yield better results. However, a general concern is that no singular model is able to reflect underlying information that only an individual who has intimate knowledge of the data would know. Thus, the purpose of this paper is not to distinguish one model that works for all applicable data, but to propose various models that have pros and cons and suggest ways that they can be improved upon.

Regulation in the insurance market has increased greatly over the past four decades, and recent regulatory frameworks such as Solvency II have made simulations increasingly important. Monte Carlo simulations are often too inefficient to be used by themselves, and these Monte Carlo simulations begin to struggle when the complexity of insurance contracts increases. For that reason, there have been numerous suggested improvements to traditional MC methods such as the sample recycling method and a neural network method. This thesis will review various risk measures, the methods used to calculate them, and a detailed analysis of the neural network method and the sample recycling method. The sample recycling method and the neural network method will then be analyzed in detail, and a comparative analysis of the sample recycling method and the neural network method will be given. It was discovered that both the sample recycling method and the neural network method provide a large improvement in computational cost and overall run time with minor impacts on the accuracy. Thus, it was concluded that the sample recycling method is best suited for contracts where the inner loop estimations are particularly complex and the neural network is a general method that pairs well with complex input portfolios.

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.

For years, patients with Cystic Fibrosis (CF) were able to only treat the symptoms of the disease, but advancements over the last decade have made it possible to partially correct the genetic mutation which causes the disease. Treatment for this disease has long been complex due to the number of organs affected by the mutation, but these new modulator therapies add another level of complexity due to the large yearly cost of $300,000. While approvals for the drugs are reaching more age groups in many countries, ease of access still varies due to the reimbursement deals or prescription coverage available in each country.

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.