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Postgraduate DiplomaInsurance, Risk & Actuarial Data Applications open

Diploma in Stochastic Models for Climate Risk

Online postgraduate diploma of 800 hours in stochastic models for climate risk. 8 modules covering stochastic models for climate risk from fundamentals to validation and implementation, applying Solvency II, IFRS 17 and Delegated Regulation (EU) 2015/35 with R, Python, SAS and Prophet, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Master the technical fundamentals of stochastic models for climate risk and its regulatory vocabulary.

02

Apply Solvency II (Directive 2009/138/EC), IFRS 17 and Delegated Regulation (EU) 2015/35 to real design and validation decisions.

03

Build and correlate calculation models with R and Python (pandas, scikit-learn).

04

Plan and run test campaigns in an actuarial modelling environment and a claims data platform.

05

Analyse operational data and translate it into engineering decisions.

06

Prepare the technical and compliance dossier required by the client or the regulator.

07

Lead a stochastic models for climate risk project technically from start to finish.

Profile

Who it's for

  • Non-life actuaries looking to specialise in stochastic models for climate risk.
  • Risk analysts who want to deepen their technical expertise in this field.
  • Technical underwriters.
  • Claims adjusters moving into technical roles.
  • Pricing managers.
  • Technical reserves managers.
  • Engineering or science graduates seeking to enter motor insurance.
Standards & tools

What you'll work with

  • Solvency II (Directive 2009/138/EC) (EU prudential regime for insurers)
  • IFRS 17 (international accounting standard for insurance contracts)
  • Delegated Regulation (EU) 2015/35 (Solvency II implementing rules)
  • EIOPA Guidelines (European supervisory guidance on Solvency II)
  • ISO 31000 (risk management guidelines)
  • Law 20/2015 (LOSSEAR) (Spanish law on the supervision of insurers)
  • Regulation (EU) 2016/679 (GDPR) (data protection)
  • DORA (Regulation (EU) 2022/2554) (digital operational resilience)
  • AI Act (Regulation (EU) 2024/1689) (EU rules on artificial intelligence)
  • R
  • Python (pandas, scikit-learn)
  • SAS
  • Prophet
  • ResQ
Syllabus

8 modules

  1. 1.1Core concepts and quantities of Stochastic Models for Climate Risk
  2. 1.2Technology landscape and state of the art
  3. 1.3Technical terminology and reference documentation
  4. 1.4Physical and engineering principles applied to the case
  5. 1.5Functional architecture: from requirements to components
  6. 1.6Interfaces and dependencies with adjacent systems
  7. 1.7Typical use cases in the sector
  8. 1.8Common errors and how to detect them early
  9. 1.9Data sources and quality criteria
  10. 1.10Guided initial diagnostic exercise
  1. 2.1Applicable regulatory framework: overview
  2. 2.2Regulatory hierarchy and conflicts between standards
  3. 2.3Mandatory requirements versus good practice
  4. 2.4Acceptance criteria and required evidence
  5. 2.5Technical documentation and traceability
  6. 2.6Change management and configuration control
  7. 2.7Audits, inspections and non-conformities
  8. 2.8Responsibilities and division of scope
  9. 2.9Interpretation of critical clauses
  10. 2.10Exercise: regulatory compliance checklist
  1. 3.1Model set-up: assumptions and simplifications
  2. 3.2Selection of tools and working environment
  3. 3.3Preparation of input data
  4. 3.4Workflow and good modelling practice
  5. 3.5Discretisation and case preparation
  6. 3.6Boundary conditions and load cases
  7. 3.7Convergence control and numerical verification
  8. 3.8Sensitivity analysis and parametric study
  9. 3.9Automation and reuse of models
  10. 3.10Exercise: building a base model
  1. 4.1Design requirements and starting criteria
  2. 4.2Solution alternatives and comparative analysis
  3. 4.3Sizing and safety margins
  4. 4.4Selection of materials, equipment or components
  5. 4.5Integration with the rest of the system
  6. 4.6Cost, schedule and manufacturing constraints
  7. 4.7Reliability, maintainability and service life
  8. 4.8Sustainability and efficiency in design
  9. 4.9Design review and risk management
  10. 4.10Exercise: documented design proposal
  1. 5.1System architecture and functional breakdown
  2. 5.2Interface definition and control
  3. 5.3Protocols and data exchange
  4. 5.4Progressive integration and interface testing
  5. 5.5Electromagnetic and environmental compatibility
  6. 5.6Configuration management of the whole system
  7. 5.7Resolving conflicts between subsystems
  8. 5.8Tuning of the integrated system
  9. 5.9Traceability from requirements to verification
  10. 5.10Exercise: interface matrix
  1. 6.1Hazard identification and preliminary analysis
  2. 6.2Failure modes and effects analysis
  3. 6.3Fault trees and quantitative analysis
  4. 6.4Allocation of reliability targets
  5. 6.5Barriers, redundancies and fault tolerance
  6. 6.6Human factors and operating errors
  7. 6.7Safety evidence and argumentation
  8. 6.8Risk management throughout the life cycle
  9. 6.9Indicators and in-service monitoring
  10. 6.10Exercise: risk analysis of a case
  1. 7.1Verification and validation strategy
  2. 7.2Test plan: scope and coverage
  3. 7.3Instrumentation and data acquisition
  4. 7.4Laboratory testing versus field testing
  5. 7.5Correlation between model and measurement
  6. 7.6Handling uncertainty and tolerances
  7. 7.7Failure analysis and corrective actions
  8. 7.8Commissioning and acceptance criteria
  9. 7.9Documenting the validation file
  10. 7.10Exercise: validation report
  1. 8.1Case definition and success criteria
  2. 8.2Gathering input data and requirements
  3. 8.3Work planning and deliverables
  4. 8.4Technical development guided by the tutor
  5. 8.5Applying the regulatory framework to the case
  6. 8.6Verification of results and critical analysis
  7. 8.7Documenting the deliverable
  8. 8.8Technical defence and peer review
  9. 8.9Lessons learned and continuous improvement
  10. 8.10Final project: final assessment and programme close-out
Methodology, software & labs

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: you progress at your own pace within the intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, in free, academic or evaluation versions. Testing is studied on real procedures from an actuarial modelling environment, a claims data platform and a catastrophe scenario simulator. Assessment is continuous by module plus the final integrated project.

Standards covered: Solvency II (Directive 2009/138/EC), IFRS 17, Delegated Regulation (EU) 2015/35, EIOPA Guidelines, ISO 31000, Law 20/2015 (LOSSEAR), Regulation (EU) 2016/679 (GDPR), DORA (Regulation (EU) 2022/2554), AI Act (Regulation (EU) 2024/1689).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Solvency II, IFRS 17 and Delegated Regulation (EU) 2015/35 applied to real cases, not as a theoretical annex.
Industry software:R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, in free, academic or evaluation versions.
Final project:an integrated case study in stochastic models for climate risk, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Diagnosis and case definition
Define the case, its success criteria and the input data and requirements.
Regulatory compliance checklist
Map Solvency II, IFRS 17 and Delegated Regulation (EU) 2015/35 requirements to the case.
Base calculation model
Build the base model in R or Python and run sensitivity and parametric analyses.
Risk analysis and validation
Analyse risks, correlate model and data and prepare the validation report.
Final project
Document and defend the integrated case in stochastic models for climate risk, reviewed by your tutor.
Career opportunities

Where this takes you

Non-life actuaryRisk analystTechnical underwriterClaims adjusterPricing managerTechnical reserves manager
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • An internet connection and a computer able to run the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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