When large individual losses are rare, the empirical tail of the loss distribution becomes unstable and often underestimated. This workshop offers an applied and interactive case study focusing on heterogeneity and rare events. Participants will engage hands-on with data to explore modelling choices, assess tail risk, and compare approaches under limited information. Teams will experiment with a range of modelling techniques, such as alternative severity families, inclusion of covariates, regularization methods, extreme-value extensions, and strategies like resampling versus parametric tail modelling. They will also examine fair evaluation criteria in small-sample contexts. After a short industry framing of the main challenges (pricing, risk measurement, and portfolio management), participants will work in teams on a synthetic dataset calibrated to realistic large-loss scenarios.