Interpretable risk scores play a vital role in clinical decision support, yet traditional methods for deriving such scores often rely on manual preprocessing, task-specific modeling, and simplified assumptions that limit their flexibility and predictive power. We present SHAPoint, a novel, task-agnostic framework that integrates the predictive accuracy of gradient boosted trees with the interpretability of point-based risk scores. SHAPoint supports classification, regression, and survival tasks, while also inheriting valuable properties from tree-based models, such as native handling of missing data and support for monotonic constraints. Compared to existing frameworks, SHAPoint offers superior flexibility, reduced reliance on manual preprocessing, and faster runtime performance. Empirical results show that SHAPoint produces compact and interpretable scores with predictive performance comparable to state-of-the-art methods, but at a fraction of the runtime, making it a powerful tool for transparent and scalable risk stratification.