Abstract: Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. In work with Pierre-­Olivier Goffard (Université de Strasbourg), we apply ABC to fit and compare insurance loss models using aggregated data. If time permits, I will also mention some of the computational aspects of the associated 'approxbayescomp' Python package.