Course on Monte Carlo Models Applied to Insurance
Online 300 h course on Monte Carlo models applied to insurance. 6 modules covering Directive 2009/138/EC (Solvency II, internal models), Delegated Regulation (EU) 2015/35 and R. Tutor assigned and enrolment open.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain the fundamentals of stochastic simulation.
Simulate aggregate loss using frequency and severity models.
Apply variance reduction techniques to improve efficiency.
Simulate assets and balance sheets using economic scenario generators.
Validate results and analyse the convergence of the simulation.
Use simulation for capital, reserves and reinsurance.
Develop a Monte Carlo model applied to a final project.
Who it's for
- Internal models actuary who wants to specialise in Monte Carlo models applied to insurance.
- Quantitative analyst who wants to specialise in Monte Carlo models applied to insurance.
- Economic capital modeller who wants to specialise in Monte Carlo models applied to insurance.
- Asset-liability management actuary who wants to specialise in Monte Carlo models applied to insurance.
- Stochastic model validator who wants to specialise in Monte Carlo models applied to insurance.
- Actuarial consultant who wants to specialise in Monte Carlo models applied to insurance.
- Graduates in engineering or sciences seeking to enter insurance companies.
What you'll work with
- Directive 2009/138/EC (Solvency II, internal models)
- Delegated Regulation (EU) 2015/35
- IFRS 17 (insurance contracts)
- EIOPA guidelines on the use of internal models
- ASOP No. 56 (modelling)
- ISAP 1 of the IAA (general actuarial practice)
6 modules · 300 h
1.1Why simulate when there is no closed-form formula1.2Pseudo-random numbers and reproducible seeds1.3Inverse transform and acceptance-rejection method1.4Simulation of common distributions in insurance1.5Monte Carlo error and its order of convergence1.6Number of replications required for a given precision1.7Confidence intervals for a simulated estimator1.8Bias versus variance in the estimator1.9Good coding practice and traceability1.10Workshop: first simulation of aggregate loss
2.1Simulation by frequency and severity2.2Simulation of Poisson processes and claim arrivals2.3Deductibles, limits and co-insurance in simulation2.4Proportional and excess-of-loss reinsurance2.5Peak claims and heavy tails2.6Dependence between lines of business using copulas2.7Simulation of the full portfolio by segment2.8High percentiles and stability of the tail2.9Comparison with analytical methods2.10Practice: aggregate distribution of a multi-line portfolio
3.1Antithetic variables3.2Control variates3.3Stratified and importance sampling3.4Importance sampling for extreme events3.5Low-discrepancy sequences3.6Reuse of common random numbers3.7Parallelisation and reproducibility3.8Computational cost and simulation budget3.9Convergence diagnostics3.10Practice: estimating a tail using importance sampling
4.1Interest rate models and simulated curves4.2Equities: paths and volatility4.3Integrated economic scenarios4.4Joint projection of assets and liabilities4.5Nested simulation and its cost4.6Replicating portfolios as an alternative4.7Valuation of guarantees and embedded options4.8Simulation of the solvency ratio4.9Stress testing based on simulated scenarios4.10Practice: simulated projection of own funds
5.1Verification of code against cases with known solutions5.2Sensitivity analysis of assumptions5.3Variance decomposition by source of risk5.4Presenting uncertainty in results5.5Charts that help and charts that mislead5.6Documentation of assumptions and seeds5.7Independent review of the simulation model5.8Version control and computing environment5.9Common errors in actuarial simulations5.10Practice: report on the results of a simulation
6.1Economic capital through simulation6.2Pricing of coverages with complex structures6.3Designing reinsurance programmes through simulation6.4Simulation of reserves and their development6.5Business decisions supported by simulation6.6Communicating to non-technical committees6.7Process automation and periodic execution6.8Integration with the risk dashboard6.9Limits of simulation and when not to use it6.10Final project: complete simulation model of a portfolio
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (NumPy), @RISK, Igloo (WTW), MATLAB, in free, academic or evaluation versions. Tests are studied using real procedures for simulating aggregate loss, convergence and variance reduction tests, and generation of economic scenarios. Assessment is continuous, module by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the fundamentals of stochastic simulation.
Simulate aggregate loss using frequency and severity models.
Apply variance reduction techniques to improve efficiency.
Simulate assets and balance sheets using economic scenario generators.
Validate results and analyse the convergence of the simulation.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow technical regulatory reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The course works with R, Python (NumPy), @RISK, Igloo (WTW), MATLAB. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An in-house diploma from SEIUM on passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.
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