Build the analytical depth to project uncertain lifespan trends, price longevity-linked instruments, and stress-test capital reserves against tail scenarios.
Speak with usThis module introduces the mathematical machinery practitioners need to treat mortality as a stochastic process rather than a deterministic table. Participants work through Poisson processes, Markov chains, and counting process intensities, then apply these frameworks to model individual and aggregate mortality risk. The emphasis is on building intuition for how random fluctuations in death rates propagate through projection models and what that means for reserve adequacy.
The Lee-Carter model remains the workhorse of mortality forecasting, and the Cairns-Blake-Dowd family offers critical extensions for pricing and risk management. This course moves beyond formula repetition into model diagnostics, parameter uncertainty, and the practical trade-offs between simplicity and explanatory power. Participants learn to fit, critique, and adapt these models to their own population data.
Advanced techniques
Capital markets module
Participants complete foundational reading and guided exercises before live sessions begin, ensuring everyone arrives with shared baseline knowledge.
The pre-work phase typically requires eight to twelve hours of independent study. Materials include annotated mortality datasets, short video walkthroughs of key mathematical results, and diagnostic quizzes that identify knowledge gaps before group instruction starts. This asynchronous layer allows participants to revisit difficult concepts at their own pace and arrive prepared for intensive live work.
Dedicated laboratory sessions give participants structured time to fit models, diagnose failures, and interpret output under instructor supervision.
Each laboratory provides a curated dataset, a modeling objective, and a series of increasingly open-ended prompts. Participants work in pairs or small teams, with instructors circulating to address implementation questions and methodological doubts. The labs deliberately include messy data and ambiguous modeling choices, because that is what practitioners face.
Structured group work connects participants across organizations to share implementation approaches and critique each other's assumptions.
A final project requires participants to build, document, and defend a mortality projection model using real data from their jurisdiction or a provided alternative.
How specialists describe the shift in their analytical confidence and decision-making clarity after completing the curriculum.
Senior Actuary, Irish Life
Relying on static mortality tables that could not capture emerging longevity improvements or tail risks in annuity portfolios.
Building stochastic projections that quantify parameter uncertainty and inform capital allocation discussions with clear ranges.
Risk Analyst, Pension Co
Struggling to design meaningful stress scenarios for longevity risk that regulators would accept and trustees could understand.
Developing internally consistent stress tests grounded in stochastic model output, with narrative explanations for non-technical audiences.
"I had read about Lee-Carter before, but fitting it myself and watching it fail on real data changed everything. The peer review sessions taught me to spot assumption drift before it becomes a regulatory problem. I recommend this to anyone building internal models."
Quantitative Modeler, Reinsure Global
Facing internal resistance to machine learning methods due to concerns about interpretability and regulatory defensibility in mortality applications.
Presenting hybrid models that combine classical mortality structures with ML enhancements, backed by clear explainability frameworks.
"The instructors did not oversell machine learning. They showed exactly where it helps, where it complicates, and how to document it for skeptical reviewers. That honesty built my credibility internally. I now lead a small working group on advanced mortality methods."
Commitment
Most courses demand between forty and sixty hours of total engagement, split across pre-work, live sessions, laboratories, and the capstone project. Participants typically complete a course over eight to twelve weeks while maintaining full employment.