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- All Subjects: Actuarial
- All Subjects: Nested Monte Carlo Simulation
- Creators: Milovanovic, Jelena
- Creators: Sturm, Brendan
- Creators: Swoverland, Robert Bo
- Resource Type: Text
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.