Actuarial Modelling for Insurance & Risk Management Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Explain the principles and structure of actuarial models used in insurance and risk management.
• Analyze insurance data and identify variables suitable for actuarial modelling.
• Develop appropriate frequency and severity models for insurance losses.
• Apply statistical and probability concepts to insurance risk modelling.
• Evaluate actuarial assumptions, model parameters, and data quality.
• Apply actuarial modelling concepts to insurance pricing and premium adequacy.
• Analyze claims development and apply modelling approaches to reserving.
• Evaluate portfolio risk, loss trends, profitability, and financial exposure.
• Apply scenario analysis, sensitivity analysis, and stress testing to actuarial models.
• Interpret model outputs and communicate their implications to management and non-technical stakeholders.
• Identify model limitations, uncertainty, bias, and potential sources of model risk.
• Strengthen actuarial model governance, validation, documentation, and ongoing monitoring.

Who Should Attend

Learning Outcomes

• Upon successful completion of the course, participants will be able to:
• Describe the architecture and purpose of actuarial models used in insurance.
• Prepare and assess insurance data for actuarial modelling.
• Select appropriate modelling approaches based on the nature of the insurance risk.
• Analyze claim frequency, severity, distributions, and loss patterns.
• Interpret actuarial assumptions, parameters, coefficients, and model outputs.
• Apply modelling principles to insurance pricing and premium adequacy.
• Evaluate claims development and reserve modelling approaches.
• Assess portfolio risk, profitability, exposure, and emerging trends using quantitative techniques.
• Conduct sensitivity analysis, scenario analysis, and stress testing.
• Evaluate uncertainty and limitations associated with actuarial estimates.
• Identify sources of model risk, data bias, overfitting, and inappropriate assumptions.
• Assess actuarial model performance through validation and monitoring.
• Communicate modelling results and recommendations clearly to technical and executive audiences.
• Apply actuarial modelling insights to practical insurance and risk management decisions.

Course Outline