Postgraduate Diploma in Data-Driven Price Modelling Techniques
Online Postgraduate Diploma of 800 h in Data-Driven Price Modelling Techniques. 8 modules covering Directive 2009/138/EC (Solvency II), Directive (EU) 2016/97 (IDD), product governance and Emblem (WTW). Dedicated tutor and open enrolment.
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 technical and commercial components of an insurance price.
Fit generalised linear models for frequency, severity and pure premium.
Apply advanced techniques such as GAM and gradient boosting to pricing.
Incorporate external data to enrich rating variables.
Integrate demand models into the optimisation of the final price.
Validate and document pricing models within a governance framework.
Deploy rates in quotation systems and monitor their performance.
Who it's for
- Pricing actuary wishing to specialise in Data-Driven Price Modelling Techniques.
- Pricing analyst wishing to specialise in Data-Driven Price Modelling Techniques.
- Actuarial data scientist wishing to specialise in Data-Driven Price Modelling Techniques.
- Rates manager wishing to specialise in Data-Driven Price Modelling Techniques.
- Price model validator wishing to specialise in Data-Driven Price Modelling Techniques.
- Actuarial consultant wishing to specialise in Data-Driven Price Modelling Techniques.
- Graduates in engineering or science seeking to enter non-life insurers.
What you'll work with
- Directive 2009/138/EC (Solvency II)
- Directive (EU) 2016/97 (IDD), product governance
- Directive 2004/113/EC (equal treatment in insurance)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- Law 20/2015 (LOSSEAR)
- Law 15/2022 on comprehensive equal treatment and non-discrimination
8 modules · 800 h
1.1Pure premium, technical premium and commercial premium1.2Equivalence principle and loadings1.3Cost of capital and profit margin1.4Underwriting cycle and soft or hard market1.5Risk price versus market price1.6Permitted rating factors1.7Gender directive and the Test-Achats ruling1.8Data required for pricing1.9Organisation of the pricing team1.10Case study: breakdown of a home insurance premium
2.1Exponential family and link functions2.2Frequency and severity models2.3Tweedie model for pure premium2.4Offset and weights2.5Level grouping and smoothing2.6Variable interactions2.7Hypothesis testing and selection2.8Diagnostics and deviance residuals2.9Translating coefficients into rates2.10Practical: complete GLM rating plan in Python
3.1Lasso, Ridge and elastic net regularisation3.2Generalised additive models3.3Mixed models and credibility3.4Gradient boosting for pricing3.5Neural networks and combined actuarial neural network3.6Monotonicity constraints3.7Distilling complex models into GLMs3.8Handling high-cardinality variables3.9Comparison with Gini and lift curves3.10Laboratory: GBM with monotonicity constraints
4.1Geographic and housing data4.2Vehicle data and technical bases4.3Telematics and usage data4.4Credit data and its legal limits4.5Open and third-party data4.6Quality and stability of sources4.7Cost and incremental value of data4.8Proxy variables and discrimination risk4.9Data protection and purpose limitation4.10Workshop: assessing the contribution of a new data source
5.1New policy conversion models5.2Renewal retention models5.3Price elasticity of demand5.4Competitor and market price analysis5.5Constrained price optimisation5.6Efficient frontier of volume and profit5.7Prohibition of price walking in the market5.8Experiments and controlled tests5.9Results monitoring5.10Practical: optimisation of renewal prices
6.1Out-of-sample and out-of-time validation6.2Stability of factors6.3Portfolio impact analysis6.4Explainability for business and supervisors6.5Fairness and bias in rating6.6EIOPA guidelines on ethics and data6.7European AI Regulation and life and health insurance6.8Pricing model documentation6.9Pricing committees and approval6.10Case study: approval dossier for a rate
7.1Rating engines7.2Real-time model deployment7.3Rate version control7.4Price regression testing7.5Integration with comparison websites7.6Rate performance monitoring7.7Drift and loss ratio alerts7.8Automation of the review cycle7.9Tools: Python, R, Emblem and Radar7.10Laboratory: deploying a premium model in an API
8.1Product definition and objectives8.2Data preparation8.3Risk premium model8.4Demand model8.5Price optimisation8.6Validation and impact analysis8.7Implementation plan8.8Governance documentation8.9Report and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Emblem (WTW), Akur8, R (glm, mgcv), Python (statsmodels, XGBoost), Radar (WTW), in free, academic or evaluation versions. Case studies cover real procedures for fitting generalised linear models of frequency and severity, enriching rates with external geographic data, and validating rates on out-of-period samples. Assessment is continuous per module plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the technical and commercial components of an insurance price.
Fit generalised linear models for frequency, severity and pure premium.
Apply advanced techniques such as GAM and gradient boosting to pricing.
Incorporate external data to enrich rating variables.
Integrate demand models into the optimisation of the final price.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- A sufficient level of Spanish to follow technical regulatory texts.
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 800 hours spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
The programme uses Emblem (WTW), Akur8, R (glm, mgcv), Python (statsmodels, XGBoost), Radar (WTW). Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM in-house diploma on passing the continuous assessment and the integrative 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 towards the advanced programme. Please consult the recognition policy when enrolling.
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