This master thesis delves into machine learning predictive modelling to predict missing values in loss reserving, focusing on predicting missing values for individual features (age, accident year, etc) and annual insurance payments. Leveraging machine learning techniques such as random forest and decision trees, we explore their performance for missing value prediction compared to traditional regression models. Moreover, the study transforms individual payments into run-off triangle versions. It uses the imputed dataset and complete dataset to compare the performance of different data imputation models by the loss reserves estimation from the Mack and GLM reserves model. By evaluating the performance of these diverse techniques, this research aims to contribute valuable insights to the evolving landscape of predictive analytics in insurance, guiding industry practices toward more accurate and efficient modelling approaches.

Master of Science (MSc)

Thesis