The Impact of Different Loss Functions and Calibration Methods on the Insurance Claim Severity Model

This study delves into the predictive capabilities of machine learning models for insurance claim severity estimation. By leveraging a dataset from French motor third-party liability insurance, two leading models, gradient boosting machine (GBM) and random forest (RF), are employed. Three distinct loss functions, RMSE, MAE, and gamma deviation, are utilized to assess their influence on model performance. Additionally, four novel calibration methods — additive, multiplicative, linear, and binning calibration — are explored to refine model predictions and better align them with actual claim amounts, thus innovating premium pricing strategies. Findings reveal that the loss function choice impacts prediction accuracy differently across models. The calibration methods generally enhance prediction accuracy, with the optimal one varying by scenario. The work underscores the criticality of selecting appropriate loss functions and calibration methods for optimizing insurance pricing prediction accuracy.