The insurance industry is a multibillion-dollar industry, yet it lags far behind other industries like banking and big tech in its adaptation of automation. I experienced this first-hand as an intern at State Farm. I completed a project that was a massive data entry job and made it into a process that took clicking three buttons to finish. Although just one example, it was clear that State Farm as well as the insurance industry in general are not utilizing automation and machine learning. The adaptation of automation and machine learning will have internal and external benefits for insurance companies like increased efficiencies in business processes and increased customer satisfaction. However, to realize these external and internal benefits, companies, like State Farm, must implement an adhocratic culture where risk taking is incentivized, and companies must invest resources into their underwriting processes, rather through internal investment or an acquisition, to automate the process.

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.