Katrien Antonio: Micro level stochastic loss reserving for general insurance

To meet future liabilities general insurance companies will set up reserves. Predicting future cash flows is essential in this process. The vast literature on (stochastic) loss reserving methods concentrates on data aggregated in run off triangles. However, a triangle is a summary of an underlying data set with the development of individual claims. We refer to this data set as  micro level  data. The talk will give an overview of our recent work on the analysis of such micro level data. We use detailed information of the time of occurrence of the claim, the delay between occurrence and reporting to the insurance company, the occurrences of payments and their sizes and the final settlement. Our model specifications are (semi)parametric. We calibrate the model to historical data and use it to project the future development of open claims. An out of sample prediction exercise shows that we obtain detailed and valuable reserve calculations. The talk ends with a discussion of ongoing research in this area, and a sketch of opportunities in the domain of disability insurance.