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Master'sInsurance, Risk & Actuarial Data Applications open

Master’s in Underwriting, Product & Pricing Engineering

Online master's programme of 1,900 hours in underwriting, product and pricing engineering, structured in 12 modules covering the insurance business in mobility, regulation, actuarial premium calculation, technical underwriting, GLMs, machine learning in pricing, price optimisation, product design, reinsurance and portfolio profitability, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand the insurance value chain and regulation.

02

Apply actuarial fundamentals to the pure premium.

03

Define technical underwriting policies.

04

Build ratemaking models with GLMs and machine learning.

05

Optimise commercial prices within regulatory constraints.

06

Design insurance products for mobility.

07

Monitor the technical profitability of a portfolio.

Profile

Who it's for

  • Technical underwriters.
  • Pricing actuaries.
  • Pricing analysts.
  • Insurance product managers.
  • Data scientists working in insurance.
  • Risk portfolio managers.
  • Engineering or science graduates seeking to enter the insurance sector.
Standards & tools

What you'll work with

  • Law 50/1980 on Insurance Contracts (Spanish insurance contract law)
  • Law 20/2015 (regulation, supervision and solvency of insurance undertakings)
  • Directive 2009/138/EC (Solvency II) (EU prudential regime for insurers)
  • Directive (EU) 2016/97 (insurance distribution)
  • Regulation (EU) 2022/2554 (DORA) (digital operational resilience)
  • Regulation (EU) 2024/1689 (artificial intelligence)
  • Regulation (EU) 2016/679 (data protection)
  • IFRS 17 (insurance contracts)
  • R
  • Python with scikit-learn
  • WTW Emblem
  • Earnix
Syllabus

12 modules · 1900 h

  1. 1.1Insurance value chain
  2. 1.2Motor, transport and infrastructure lines
  3. 1.3The role of underwriting
  4. 1.4Insurance product life cycle
  5. 1.5Technical profitability and combined ratio
  6. 1.6Spanish and European markets
  7. 1.7Agents, brokers and bancassurance
  8. 1.8Mobility trends
  9. 1.9Technological challenges in the sector
  10. 1.10Case study: analysis of a line's technical account
  1. 2.1Insurance Contract Act
  2. 2.2Insurance regulation and supervision act
  3. 2.3Solvency II and its three pillars
  4. 2.4Insurance Distribution Directive
  5. 2.5Product oversight and governance
  6. 2.6Consumer protection
  7. 2.7EU Digital Operational Resilience Regulation
  8. 2.8Artificial intelligence in insurance
  9. 2.9Data protection in underwriting
  10. 2.10Laboratory: governance analysis of a product
  1. 3.1Probability applied to insurance
  2. 3.2Claims frequency and severity
  3. 3.3Poisson and negative binomial distributions
  4. 3.4Cost distributions: gamma and lognormal
  5. 3.5Pure premium and loadings
  6. 3.6Risk exposure
  7. 3.7Treatment of large claims
  8. 3.8Premium calculation principles
  9. 3.9Credibility theory
  10. 3.10Practical exercise: pure premium calculation by frequency and severity
  1. 4.1Underwriting policy
  2. 4.2Risk appetite
  3. 4.3Risk information
  4. 4.4Risk selection and classification
  5. 4.5Risk inspections
  6. 4.6Underwriting authorities and limits
  7. 4.7Accumulations and concentrations
  8. 4.8Rule-based automated underwriting
  9. 4.9Underwriting indicators
  10. 4.10Case study: fleet underwriting guide
  1. 5.1Generalised linear models in ratemaking
  2. 5.2Link and variance functions
  3. 5.3Frequency models
  4. 5.4Severity models
  5. 5.5Tweedie models of total cost
  6. 5.6Variable selection
  7. 5.7Interactions and smoothing
  8. 5.8Model validation
  9. 5.9Interpretation of relativities
  10. 5.10Laboratory: frequency GLM in R
  1. 6.1Decision trees and random forests
  2. 6.2Gradient boosting models
  3. 6.3Neural networks for ratemaking
  4. 6.4Comparison with linear models
  5. 6.5Interpretability with SHAP
  6. 6.6Risk of overfitting
  7. 6.7Fairness and discrimination
  8. 6.8Model governance
  9. 6.9Model deployment
  10. 6.10Practical exercise: gradient boosting claims model
  1. 7.1From technical premium to commercial price
  2. 7.2Price elasticity of demand
  3. 7.3Conversion and renewal models
  4. 7.4Price optimisation
  5. 7.5Regulatory constraints on pricing
  6. 7.6Competition and price comparison sites
  7. 7.7Renewal pricing
  8. 7.8Monitoring prices in production
  9. 7.9Pricing indicators
  10. 7.10Case study: renewal price optimisation
  1. 8.1Customer needs
  2. 8.2Covers and exclusions
  3. 8.3Deductibles and limits
  4. 8.4Drafting policy wordings
  5. 8.5Design of modular products
  6. 8.6Services associated with insurance
  7. 8.7Parametric mobility insurance
  8. 8.8Product testing
  9. 8.9Market launch
  10. 8.10Practical exercise: designing a product for delivery fleets
  1. 9.1Internal data sources
  2. 9.2External and geographical data
  3. 9.3Telematics data
  4. 9.4Data quality
  5. 9.5Insurance data architecture
  6. 9.6Rating platforms
  7. 9.7Process automation
  8. 9.8Cybersecurity in insurers
  9. 9.9Technology supplier management
  10. 9.10Laboratory: preparing a policy dataset
  1. 10.1Types of reinsurance
  2. 10.2Proportional and non-proportional reinsurance
  3. 10.3Reinsurance programmes
  4. 10.4Cost of reinsurance
  5. 10.5Solvency capital
  6. 10.6Internal models and standard formula
  7. 10.7Stress testing
  8. 10.8Catastrophe management
  9. 10.9Risk appetite and limits
  10. 10.10Case study: reinsurance programme for a motor line
  1. 11.1Portfolio technical indicators
  2. 11.2Loss ratio
  3. 11.3Claims development
  4. 11.4Calculation of technical provisions
  5. 11.5Variance analysis
  6. 11.6Market cycles
  7. 11.7Management reporting
  8. 11.8Periodic rate review
  9. 11.9Continuous product improvement
  10. 11.10Practical exercise: technical dashboard for a product
  1. 12.1Product definition
  2. 12.2Market analysis
  3. 12.3Data and exposure
  4. 12.4Technical premium model
  5. 12.5Commercial price proposal
  6. 12.6Underwriting policy proposal
  7. 12.7Governance and compliance
  8. 12.8Profitability analysis
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
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, so the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python with scikit-learn, WTW Emblem and Earnix, in free, academic or evaluation versions. Testing is studied through real procedures for GLM ratemaking models, insurance product design and portfolio profitability analysis. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Law 50/1980 on Insurance Contracts, Law 20/2015, Directive 2009/138/EC (Solvency II), Directive (EU) 2016/97, Regulation (EU) 2022/2554 (DORA), Regulation (EU) 2024/1689, Regulation (EU) 2016/679, IFRS 17.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Law 50/1980, Law 20/2015 and Solvency II applied to real cases, not as a theoretical annex.
Industry software:R, Python with scikit-learn, WTW Emblem and Earnix, in free, academic or evaluation versions.
Master's Thesis with defence:a complete insurance product with technical premium model, commercial price, underwriting policy, governance and profitability analysis, defended before a panel.
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.

Technical account analysis
Analyse the technical account of a line and its regulatory framework.
Pure premium modelling
Calculate a pure premium by frequency and severity and fit a frequency GLM in R.
Machine learning and optimisation
Build a gradient boosting claims model and optimise renewal prices.
Product and reinsurance design
Design a product for delivery fleets and its reinsurance programme.
Master's Thesis
Develop a complete insurance product with pricing, underwriting and profitability analysis, defended before a panel.
Career opportunities

Where this takes you

Technical underwriterPricing actuaryPricing analystInsurance product managerInsurance data scientistRisk portfolio manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's 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.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 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 with scikit-learn, WTW Emblem and Earnix, 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), 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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