The course

1
Start here
Paper A 3 h 20 in Word and Paper B 1 h 50 in R and Word, weighted 70:30
Free: A tour of CS2 Free: Working in Word and R
2
Loss distributions and reinsurance
Topic 1: Random variables and distributions for risk modelling, 20% of the exam
Free: Losses under deductibles and reinsurance 13 questions
3
Compound distributions, copulas and extreme values
Topic 1: Random variables and distributions for risk modelling, 20% of the exam
Compound distributions and tail dependence 13 questions
4
Time series models
Topic 2: Time series, 20% of the exam
AR, MA and ARMA processes 13 questions
5
Fitting and forecasting time series
Topic 2: Time series, 20% of the exam
Identifying and forecasting a time series 12 questions
6
Stochastic processes and Markov chains
Topic 3: Stochastic processes, 25% of the exam
Markov chains and no-claims discounts 16 questions
7
Markov jump processes
Topic 3: Stochastic processes, 25% of the exam
Kolmogorov equations for a sickness model 15 questions
8
Survival models
Topic 4: Survival models, 25% of the exam
Survival functions and the force of mortality 11 questions
9
Estimating lifetime distributions and transition intensities
Topic 4: Survival models, 25% of the exam
Kaplan-Meier and the Cox model 13 questions
10
Graduation and mortality projection
Topic 4: Survival models, 25% of the exam
Graduation tests 10 questions
11
Machine learning
Topic 5: Machine learning, 10% of the exam
Cross-validation and classifier metrics 12 questions
12
Mock exams
Two full mocks, each a Paper A and a Paper B in the exam's timings
The mocks

The course

Start here

Loss distributions and reinsurance

Compound distributions, copulas and extreme values

Time series models

Fitting and forecasting time series

Stochastic processes and Markov chains

Markov jump processes

Survival models

Estimating lifetime distributions and transition intensities

Graduation and mortality projection

Machine learning

Mock exams