Master'sInsurance, Risk & Actuarial Data Applications open

Master’s in Actuarial Models with Telematics Data Engineering

Online master's programme of 1,900 hours in actuarial modelling engineering with telematics data, structured in 12 modules covering actuarial statistics, telematics data, frequency and severity models, credibility and bonus-malus, driving time series, actuarial machine learning, model validation, granular reserving, fairness and implementation, with an assigned technical tutor.

Duration1900 h

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in Actuarial Models with Telematics Data Engineering — studying from Australia

Australia has a mature insurance and superannuation sector, with major insurers, reinsurers and financial institutions relying heavily on actuarial analysis and risk modelling. Engineers and data specialists contribute to catastrophe modelling, telematics-based pricing and claims analytics, particularly given the country's exposure to bushfire, flood and cyclone risk. The regulatory environment is shaped by prudential oversight and consumer protection frameworks. Employers include general and life insurers, consultancies, reinsurance firms and government risk agencies.

Fees for Australia: A$12,600 (AUD), with instalments and employer sponsorship available.

Relevant bodies in Australia

Australian Prudential Regulation Authority (APRA)
Australian Securities and Investments Commission (ASIC)
Institute of Actuaries of Australia (IAA)
Insurance Council of Australia (ICA)

Typical roles

Actuarial Analyst
Risk Data Scientist
Catastrophe Modeller
Insurance Analytics Engineer

Questions from Australia

Does this programme lead to recognition by the Actuaries Institute or another Australian professional body?

SEIUM qualifications are private and are not accredited by the Actuaries Institute, Engineers Australia or any national body. The Actuaries Institute sets its own examination and membership requirements, which a private qualification does not replace. Recognition for any role is decided by the employer or the relevant body, so confirm their requirements before enrolling.

Which Australian insurers and risk employers might consider this training?

Insurers, reinsurers, banks, consultancies and risk management teams in Australia employ people with data and risk skills. Whether they consider a private engineering qualification relevant depends on the role and their own hiring policy. Your demonstrated skills, portfolio and final project are often what matter most to them.

Is the course manageable while working, and can my employer fund it?

Yes, the programmes are delivered entirely online in English, with an assigned technical tutor, so you can fit study around work from Australia. Fees can be paid in instalments, and employer sponsorship is possible. Check the total hours for your chosen programme and agree a realistic weekly schedule before you start.

Virtual lab · try it before you enrol

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Learning outcomes

What you will be able to do

Apply advanced statistical techniques to claims data.

Build frequency and severity models with telematics variables.

Develop dynamic credibility and bonus-malus systems.

Analyse driving time series with deep learning.

Validate actuarial models and manage their model risk.

Assess the fairness and regulatory compliance of models.

Implement actuarial models in the insurer's processes.

Profile

Who it's for

Modelling actuaries.
Actuarial data scientists.
Model validators.
Telematics pricing analysts.
Heads of the actuarial function.
Model risk consultants.
Engineering or science graduates seeking to enter motor insurance.

Standards & tools

What you'll work with

Directive 2009/138/EC (Solvency II) (EU prudential regime for insurers)
IFRS 17 (insurance contracts)
Regulation (EU) 2016/679 (GDPR) (data protection)
Regulation (EU) 2024/1689 (artificial intelligence)
Law 35/2015 (reform of the system for assessing damages in road traffic accidents)
Royal Legislative Decree 8/2004 (civil liability and insurance in motor vehicle traffic)
Directive 2004/113/EC (equal treatment between men and women in access to goods and services)
R (glm, mgcv, ChainLadder)
Python (statsmodels, PyTorch)
H2O.ai
Emblem (WTW)
Akur8

Syllabus

12 modules · 1900 h

1.1Maximum likelihood inference
1.2Bayesian estimation and prior distributions
1.3Count distributions with overdispersion
1.4Zero-inflated models
1.5Heavy-tailed distributions
1.6Extreme value theory applied to claims
1.7Finite mixture distributions
1.8Bootstrap and resampling
1.9Goodness-of-fit tests
1.10Workshop: fitting distributions to motor claims

2.1Data types: trip, event and time series
2.2Usage-based exposure variables
2.3Quality and completeness of records
2.4Observation period and entry bias
2.5Joining telematics data with policies and claims
2.6Assigning claims to trips
2.7Multiple drivers per vehicle
2.8Smartphone versus black-box data
2.9Sampling and portfolio representativeness
2.10Practical exercise: policy-trip-claim modelling dataset

3.1Poisson GLM with exposure in kilometres
3.2Negative binomial and unobserved heterogeneity
3.3Zero-inflated Poisson models
3.4Random effects by driver
3.5Driving variables as predictors
3.6Risk response curves to speed
3.7Variable selection with Lasso regularisation
3.8Elastic net and multicollinearity
3.9Residual diagnostics
3.10Laboratory: frequency model with kilometres as exposure

4.1Gamma and inverse Gaussian GLMs
4.2Modelling large claims
4.3Separating property damage and bodily injury
4.4Tweedie model with power parameter
4.5Double GLMs for dispersion
4.6Long-tail claims and the traffic compensation scale
4.7Expected cost per kilometre
4.8Adjustment for claims inflation
4.9Validation of aggregate cost
4.10Case study: total liability cost with a Tweedie model

5.1Bühlmann-Straub model
5.2Credibility with continuous driving data
5.3Classic bonus-malus systems
5.4Dynamic bonus-malus with telematics
5.5Bayesian updating of the risk profile
5.6Dynamic Poisson-gamma models
5.7Fleet experience and group credibility
5.8Premium stability and commercial acceptance
5.9Comparison of experience rating systems
5.10Practical exercise: Bayesian updating of a driver's premium

6.1Representing trips as time series
6.2Feature extraction with sliding windows
6.3Fourier and wavelet transforms
6.4Detection of behaviour changes
6.5Clustering of driving styles
6.6Recurrent neural networks and LSTM
6.7Convolutional networks on signals
6.8Autoencoders for latent representations
6.9Interpreting driving patterns
6.10Laboratory: clustering of driving profiles

7.1Gradient boosting with Poisson loss
7.2Neural networks with GLM structure (CANN)
7.3Generalised additive models
7.4Monotonic tree models
7.5Calibration and premium balance
7.6Explainability with SHAP and ALE
7.7Comparison of discriminatory power
7.8Model risk and documentation
7.9Deployment in rating systems
7.10Case study: CANN model versus benchmark GLM

8.1Training, validation and test samples
8.2Out-of-time validation
8.3Double lift and concentration curves
8.4Variable stability tests (PSI)
8.5Backtesting expected claims
8.6Sensitivity analysis
8.7Independent validation
8.8Model inventory and governance
8.9Documentation for supervisors
8.10Practical exercise: validation report for a frequency model

9.1Limitations of aggregate methods
9.2Individual claim-level reserving
9.3Settlement time models
9.4Using telematics accident data
9.5Machine learning for reserving
9.6Uncertainty with Mack's method
9.7Reserve bootstrapping
9.8Reserving under IFRS 17 and Solvency II
9.9Communicating results to the committee
9.10Workshop: individual reserves versus Chain-Ladder

10.1Protected variables and indirect discrimination
10.2Fairness metrics in ratemaking
10.3Bias mitigation techniques
10.4GDPR and location data
10.5Automated decisions and Article 22 of the GDPR
10.6EU AI Act and high-risk systems
10.7EIOPA principles on AI governance
10.8Price transparency for the customer
10.9Price optimisation practices
10.10Case study: fairness audit of a tariff

11.1From model to commercial tariff
11.2Portfolio impact simulation
11.3Elasticity and retention
11.4Integration with the rating engine
11.5Monitoring in production
11.6Retraining and data drift
11.7Indicators for management
11.8Coordination with underwriting and distribution
11.9Change management in the technical area
11.10Practical exercise: impact simulation of a new tariff

12.1Framing the modelling case
12.2Review of telematics actuarial literature
12.3Dataset design
12.4Selection of the model family
12.5Estimation and calibration
12.6Validation and robustness analysis
12.7Business and regulatory implications
12.8Conclusions and future lines of work
12.9Thesis report
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 (glm, mgcv, ChainLadder), Python (statsmodels, PyTorch), H2O.ai, Emblem (WTW) and Akur8, in free, academic or evaluation versions. Testing is studied through real procedures for frequency modelling with exposure in kilometres, comparison of GLMs and actuarial neural networks, and validation of pricing models for the regulator. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2009/138/EC (Solvency II), IFRS 17, Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2024/1689, Law 35/2015, Royal Legislative Decree 8/2004, Directive 2004/113/EC.

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 the GDPR applied to real cases, not as a theoretical annex.

Industry software:R (glm, mgcv, ChainLadder), Python (statsmodels, PyTorch), H2O.ai, Emblem (WTW) and Akur8, in free, academic or evaluation versions.

Master's Thesis with defence:a complete actuarial modelling case on telematics data, from dataset design and estimation to validation, robustness and business and regulatory implications, 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.

Modelling dataset

Fit claim distributions and build a policy-trip-claim modelling dataset.

Frequency and severity

Model frequency with kilometres as exposure and total liability cost with a Tweedie model.

Advanced models

Update a driver's premium with Bayesian credibility and benchmark a CANN model against a GLM.

Validation and fairness

Prepare a validation report and a fairness audit of a tariff.

Master's Thesis

Develop, validate and assess a telematics actuarial model on real data, defended before a panel.

Career opportunities

Where this takes you

Modelling actuaryActuarial data scientistModel validatorTelematics pricing analystHead of actuarial functionModel risk consultant

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.

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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 (glm, mgcv, ChainLadder), Python (statsmodels, PyTorch), H2O.ai, Emblem (WTW) and Akur8, 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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