Loss Modelling, Reserving and Fraud analytics (03 - 10 - 17/06/2021) (9 CPD)
This workshop introduces the essential concepts of building insurance loss models with R.
On the first session, you will gain insights in the foundations of handling insurance loss data, including useful data wrangling and visualization steps. You will cover a variety of discrete and continuous loss distributions, and techniques to build more flexible distributions from standard distributions (by mixing and splicing). You will learn how to fit these models to actual data and inspect their goodness-of-fit. Then, you will use the fitted model to estimate risk measures.
The second session then puts focus on reserving analytics. Starting from a granular data set with the development of individual claims, you will learn how to get useful insights from the data. Step-by-step you will move from the granular data to aggregated data in a run-off triangle. You will fit the famous chain ladder method and examine its validity on the given data. Alternative modelling strategies based on recent research will be discussed.
The third session then covers challenges in building fraud detection models from tabular and networked data. You will acquire insights in the foundations of these analytic methods, learn how to set-up the model building process, and focus on building a good understanding of the resulting model output and predictions.
Leaving this workshop, you should have a firm grasp of the working principles of a variety of loss models for frequency and severity data and be able to explore their use in practical settings. Moreover, you should have acquired the fundamental insights to explore some other methods on your own.
The course offers a good mix of theory, examples and hands-on coding demonstrations with R and RStudio.
The online sessions will be organized via MS Teams or Zoom.
Since (at least) two teachers will run the course, participants have the possibility to join a break out room for one-on-one assistance regarding questions or coding examples.