CS2 – Risk Modelling & Survival Analysis (Theory + R Programming)

Build expertise in survival models, time series, and R programming for actuarial risk analysis.

 Course Overview

CS2 focuses on modelling long-term risks such as mortality, survival, and insurance claims, with practical application in R programming. This course prepares students for analysing life insurance, pensions, and health-related data using statistical techniques and real-life datasets.

CS2 is offered by IAI (India) and IFoA (UK) and is divided into:

•  CS2A – Theory: Risk modelling, time series, and survival models
•  CS2B – R-based Practical: Data analysis, coding, and simulation in R

What You Will Learn

 CS2A – Risk Modelling Theory

• Principles of survival models (force of mortality, survival functions)
• Estimating lifetime distributions using censoring and truncation
• Graduation of mortality rates (using Makeham/Heligman-Pollard models)
• Markov and Poisson processes
• Time series models (AR, MA, ARIMA, smoothing)
• Ruin theory and reinsurance modelling
• Estimating parameters and assessing model fit

 CS2B – R Programming Practical

• Data importing, cleaning, and summarisation using R
• Creating survival curves, Kaplan-Meier estimators, and Cox models
• Simulating risk models and generating life tables
• Fitting time series models in R
• Writing custom functions and automating model fitting
• Working with large datasets in actuarial contexts

 Why Choose CS2?

• Essential for careers in life & health insurance, pensions, and analytics
• Equips you with R programming skills, widely used in the actuarial industry
• Builds on CS1 knowledge and leads into advanced subjects like SP1, SP7, SP9
• Teaches practical risk assessment and forecasting tools

 Applications of CS2 Knowledge

• Pricing and reserving in life & health insurance
• Building survival curves for patient data or customer churn
• Mortality & morbidity analysis for pension planning
• Forecasting using ARIMA for business & financial time series
• Stochastic modelling of claims and ruin probabilities

 Exam Structure

 Passing Criteria

 S MONK Tips to Crack CS2

• Understand actuarial interpretation of survival functions & estimators
• Practice R code early: use datasets and repeat simulations
• Use R packages: survival, forecast, actuar
• Prepare with mock papers & project-style tasks
• Master time series topics with both formulas + code

 Course Fee Breakdown

 Who Should Enroll?

 Students aiming to work in insurance, analytics, or health modelling

 Those who completed CS1 or have a background in stats/math

 Beginners with basic coding interest – no prior R required