Principles of Actuarial Modelling

Explore actuarial modelling foundations, including pricing, reserving, capital, asset, loss, survival, and population models, plus deterministic versus stochastic approaches, time horizons, and key analysis and communication techniques.

Identify uncertainty and risk, observe data, analyze it with statistical methods, build actuarial modelling approaches to quantify and simulate risk outcomes, monitor actual versus expected results, and adapt strategies.

Define the objectives and explore model components—assumptions, inputs, parameters, calculations, outputs—then connect conclusions to decision making through stakeholder roles.

In this course, understand the limitations of models, including garbage in, garbage out, high costs, overconfidence, incomplete data, biased estimates, and the risk of misinterpretation that can undermine decisions.

Explain deterministic versus stochastic models, showing fixed-input outputs versus random-parameter outcomes, using y plus 3x with x as a dice value; guide model choice by objectives, budget, and audience.

Examine how time horizons shape uncertainty in stochastic models. As horizons lengthen, the funnel of doubt widens with changing uncertainty sources and discount-rate sensitivity.

Learn sensitivity analysis, scenario analysis, and stress testing for deterministic models, explore stochastic alternatives, and assess high impact low probability events for robust planning.

Communicate model results clearly, acknowledge that models are almost always wrong and require human judgment, and use unambiguous data visualizations that convey the story without interpretation.

Explore the data analysis pipeline from data to wisdom, detailing descriptive, inferential, and predictive analyses with actuarial modelling examples like lapse risk and truncated versus censored data.