Catastrophic events like pandemics (Covid 19 etc.) or natural disasters create extreme volatility for insurers — and require computationally heavy simulations. This session shows how Databricks Lakehouse + MLflow can be used to model fat-tail Loss/Risk or actuarial scenarios at scale. We’ll cover techniques for simulating mortality surges, stress-testing cash reserves, and integrating external demographic and health datasets alongside policy data. Attendees will see how distributed compute accelerates scenario evaluation and improves the transparency of actuarial assumptions.

By the end, actuaries and data scientists will understand how to leverage Databricks for catastrophe risk — moving from spreadsheets to scalable, explainable simulation models.