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Predictive modeling is central to an insurer’s mission: understanding, assessing, and predicting biometric risks. In Life & Health, this often means working with survival analysis , a domain that brings specific methodological challenges due to censored data, non-proportional hazards, complex multivariate relationships, and the need for medically coherent outputs.

Within our team, we have developed a robust Python-based survival modeling framework that adapts both traditional actuarial methods and modern machine-learning (ML) algorithms to censored data. This internal library already integrates a wide range of models—from classical Cox variants to advanced ML approaches—and continues to evolve to meet emerging needs such as handling richer datasets, improving interpretability, and aligning with new regulatory and actuarial standar...