CS2 Risk Modelling
Stochastic processes, Markov chains, and survival models: the risk mathematics behind insurance.
About CS2
CS2 applies advanced stochastic methods to insurance risk: survival analysis for mortality modelling, Markov chains for multi-state health insurance, credibility theory for experience rating, and extreme value theory for catastrophic events. Includes a compulsory R programming component from 2024 onwards.
Who is this for?
Students who have cleared or are nearly through CS1. Typically the fourth or fifth paper in most students’ progression. Essential for roles in life insurance, health insurance, and long-tail non-life lines.
Complete Syllabus
- Survival analysis: hazard function, Nelson-Aalen estimator, Kaplan-Meier, log-rank test
- Parametric survival models: exponential, Weibull, Gompertz, Cox proportional hazards
- Markov chains: transition matrices, Chapman-Kolmogorov equations, stationary distributions
- Multi-state models: disability income, long-term care, critical illness insurance
- Graduation methods: maximum likelihood, Bayesian, exposed-to-risk calculations
- Credibility theory: classical, Bühlmann, Bühlmann-Straub, Empirical Bayes
- Extreme value theory: block maxima, peaks-over-threshold, GPD distribution
- Copulas: Gaussian, t, Archimedean copulas, tail dependence, Sklar’s theorem
- R programming: survival analysis, GLMs, simulation in R (compulsory component)
Career Outcomes
Life Actuary, Health Actuary, Reserving Actuary, Pricing (long-tail non-life)
₹8–15 LPA with CM1+CS1+CS2 cleared
Do not neglect the R programming component. Past papers have specific R questions. S.MONK sessions include live coding demonstrations for every R section.
“CS2 has an R programming component that most students panic about. S.MONK spent time on the actual exam questions in R. I felt confident about the programming section, and most candidates did not.”