Master'sInsurance, Risk & Actuarial Data Applications open

Master’s in Actuarial, Telematics & Data Engineering

Online master's programme of 1,900 hours in actuarial, telematics and data engineering, structured in 12 modules covering non-life actuarial mathematics, telematics data, driving feature engineering, GLMs, machine learning, data platforms, usage-based insurance, geospatial risk, data-driven claims, regulation and solvency, with an assigned technical tutor.

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

Master’s in Actuarial, Telematics & Data Engineering — studying from Canada

Canada has a major insurance and financial services sector, with headquarters and operations concentrated in Toronto and Montréal. Insurers and reinsurers rely on actuarial science, risk modelling and data analytics for pricing, reserving and capital management. Federal and provincial regulators oversee solvency, conduct and consumer protection. Engineers and data specialists with risk and modelling skills find roles at insurers, brokers, consultancies and regulatory bodies.

Fees for Canada: C$12,400 (Canadian dollar), with instalments and employer sponsorship available.

Relevant bodies in Canada

  • Office of the Superintendent of Financial Institutions (OSFI)
  • Canadian Institute of Actuaries (CIA)
  • Financial Services Regulatory Authority of Ontario (FSRA)
  • Insurance Bureau of Canada (IBC)

Typical roles

  • Actuarial Analyst
  • Risk Analyst
  • Data Scientist – Insurance
  • Reserving Analyst

Questions from Canada

Is this programme recognised by professional bodies in Canada for insurance and actuarial data work?

SEIUM's qualifications are private, not official state degrees, and are not accredited by a national body in Canada. Actuarial credentials in Canada are governed by the Canadian Institute of Actuaries, and actuarial practice is regulated under provincial and federal rules. A private engineering qualification does not confer these credentials, so confirm recognition with employers or the relevant body yourself.

How might this qualification be used by employers in Canada in insurance or risk roles?

Insurers, banks and risk teams in Canada may value engineering-based data analysis and risk modelling skills, but how they regard a private qualification is their decision. Hiring in these sectors often depends on experience, professional exams and specific credentials. Your final project can serve as evidence of applied skills, though it does not guarantee any role or salary outcome.

What are the practical demands of studying this online from Canada?

Study is fully online and in English, and no student visa is required if you study from home. Programme lengths range from 300 to 1,900 hours, so consider how many hours a week you can commit alongside work. You are assigned a technical tutor for support. Fees can be paid in instalments, and some students receive employer sponsorship, which is possible but not guaranteed.

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Learning outcomes

What you will be able to do

01

Apply non-life actuarial mathematics to premium and reserve calculation.

02

Design risk variables from driving telematics data.

03

Build GLM and machine learning models for ratemaking.

04

Develop scalable data architectures for large telematics volumes.

05

Design profitable and transparent usage-based insurance products.

06

Assess compliance of pricing models with the GDPR and AI regulation.

07

Integrate analytical results into the insurer's solvency management.

Profile

Who it's for

  • Pricing actuaries.
  • Data scientists working in insurance.
  • Telematics analysts.
  • Pricing managers.
  • Actuarial data engineers.
  • Usage-based insurance 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)
  • Directive (EU) 2016/97 (IDD) (insurance distribution)
  • Law 50/1980 on Insurance Contracts (Spanish insurance contract law)
  • ISO/IEC 27001:2022 (information security management)
  • Regulation (EU) 2015/758 (eCall)
  • R
  • Python (scikit-learn, LightGBM)
  • Apache Spark
  • QGIS
  • SAS
Syllabus

12 modules · 1900 h

  1. 1.1Frequency and severity random variables
  2. 1.2Poisson, negative binomial and Pascal distributions
  3. 1.3Severity distributions: lognormal, gamma and Pareto
  4. 1.4Collective risk model and Panjer's formula
  5. 1.5Pure premium, safety loadings and expenses
  6. 1.6Premium calculation principles
  7. 1.7Bühlmann credibility theory
  8. 1.8Claims development triangles
  9. 1.9Chain-Ladder and Bornhuetter-Ferguson methods
  10. 1.10Workshop: IBNR reserving with real triangles
  1. 2.1Architecture of a fleet telematics solution
  2. 2.2OBD-II devices, black boxes and smartphones
  3. 2.3CAN bus and the SAE J1939 protocol
  4. 2.4GNSS, accelerometer and gyroscope signals
  5. 2.5Sampling frequency and signal quality
  6. 2.6Data ingestion with MQTT and message queues
  7. 2.7Time-series storage
  8. 2.8Consent and transparency with the driver
  9. 2.9Deployment costs and business models
  10. 2.10Laboratory: decoding a vehicle's CAN frames
  1. 3.1Trip detection and journey segmentation
  2. 3.2Map matching on road networks
  3. 3.3Harsh braking, acceleration and cornering events
  4. 3.4Speeding relative to the legal limit
  5. 3.5Night-time driving and exposure by time band
  6. 3.6Mobile phone use while driving
  7. 3.7Exposure indicators: kilometres, hours, trips
  8. 3.8Aggregation by driver, vehicle and policy
  9. 3.9Handling outliers and noise
  10. 3.10Practical exercise: building a driving score
  1. 4.1Exponential family and link functions
  2. 4.2Poisson GLM for frequency with exposure offset
  3. 4.3Gamma GLM for average cost
  4. 4.4Tweedie model for total cost
  5. 4.5Variable selection and AIC/BIC criteria
  6. 4.6Interactions and smoothing of continuous variables
  7. 4.7Validation: lift charts and Lorenz curves
  8. 4.8Actuarial Gini index
  9. 4.9Comparison of current and proposed tariffs
  10. 4.10Case study: motor third-party liability tariff
  1. 5.1Decision trees and random forests
  2. 5.2Gradient boosting with XGBoost and LightGBM
  3. 5.3Poisson and Tweedie loss functions
  4. 5.4Time-based cross-validation
  5. 5.5Hyperparameter tuning
  6. 5.6Monotonicity and business constraints
  7. 5.7Explainability with SHAP and partial dependence
  8. 5.8GAMs and hybrid GLM-ML models
  9. 5.9Overfitting risk and temporal stability
  10. 5.10Laboratory: frequency model with gradient boosting
  1. 6.1Data lake and data warehouse in insurers
  2. 6.2Lakehouse architecture and Delta/Parquet formats
  3. 6.3Distributed processing with Spark
  4. 6.4ETL/ELT pipelines and orchestration
  5. 6.5Dimensional modelling of policies and claims
  6. 6.6Data catalogue and lineage
  7. 6.7Data quality: rules and monitoring
  8. 6.8Integration with core policy systems
  9. 6.9Cloud costs and optimisation
  10. 6.10Practical exercise: telematics data pipeline in Spark
  1. 7.1Pay As You Drive and Pay How You Drive
  2. 7.2Per-kilometre and per-minute insurance
  3. 7.3Discount and bonus programmes
  4. 7.4Insurance for fleets and shared vehicles
  5. 7.5Covers for personal mobility and e-scooters
  6. 7.6Designing the customer value proposition
  7. 7.7Adverse selection and self-selection risks
  8. 7.8Retention and renewal in UBI products
  9. 7.9Profitability analysis by cohort
  10. 7.10Case study: launching a PAYD product
  1. 8.1Reference systems and projections
  2. 8.2Geocoding of risk addresses
  3. 8.3Claims maps by zone
  4. 8.4Road risk indices by road segment
  5. 8.5Spatial statistics and autocorrelation
  6. 8.6Open traffic and accident data
  7. 8.7Rating zones
  8. 8.8Visualisation with QGIS and Python libraries
  9. 8.9Territorial bias and fairness
  10. 8.10Workshop: risk zoning from claims data
  1. 9.1Accident notification from telematics
  2. 9.2Accident reconstruction with vehicle data
  3. 9.3eCall and automatic emergency alerts
  4. 9.4Automatic claims triage
  5. 9.5Early claim cost estimation
  6. 9.6Fraud detection with telematics signals
  7. 9.7Workshop analytics and repair times
  8. 9.8Recoveries and liability between insurers
  9. 9.9Customer experience indicators
  10. 9.10Practical exercise: reconstructing a claim with GNSS data
  1. 10.1Processing location data under the GDPR
  2. 10.2Legal bases and impact assessments
  3. 10.3Pseudonymisation and anonymisation
  4. 10.4Price discrimination and prohibited variables
  5. 10.5Algorithmic fairness in pricing
  6. 10.6Requirements of the EU AI Act
  7. 10.7Product governance under the IDD
  8. 10.8Transparency and the right to explanation
  9. 10.9Information security in telematics
  10. 10.10Case study: impact assessment of a UBI programme
  1. 11.1Pillars of Solvency II
  2. 11.2Best estimate and risk margin
  3. 11.3Non-life solvency capital requirement
  4. 11.4Premium and reserve risk
  5. 11.5ORSA and risk appetite
  6. 11.6Accounting measurement under IFRS 17
  7. 11.7Reinsurance as a capital tool
  8. 11.8Stress testing of motor portfolios
  9. 11.9Actuarial function and internal control
  10. 11.10Workshop: simplified premium and reserve risk SCR calculation
  1. 12.1Definition of the actuarial problem and scope
  2. 12.2Literature and state-of-the-art review
  3. 12.3Data selection and preparation
  4. 12.4Model and validation design
  5. 12.5Development of the analytical solution
  6. 12.6Evaluation of results and sensitivity
  7. 12.7Business and solvency impact
  8. 12.8Ethical and regulatory considerations
  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 (scikit-learn, LightGBM), Apache Spark, QGIS and SAS, in free, academic or evaluation versions. Testing is studied through real procedures for pricing a motor portfolio with telematics data, reserving claims with development triangles and validating driving score models. 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, Directive (EU) 2016/97 (IDD), Law 50/1980 on Insurance Contracts, ISO/IEC 27001:2022, Regulation (EU) 2015/758.

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, Python (scikit-learn, LightGBM), Apache Spark, QGIS and SAS, in free, academic or evaluation versions.
Master's Thesis with defence:a complete actuarial analytics solution on real data, from model design and validation to business, solvency, ethical and regulatory impact, 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.

Reserving and data intake
Calculate IBNR reserves with real triangles and decode a vehicle's CAN frames.
Driving features
Build a driving score and a telematics data pipeline in Spark.
Pricing models
Fit a motor liability tariff with GLMs and a gradient boosting frequency model.
Risk zoning and compliance
Zone risk from claims data and assess the impact of a UBI programme.
Master's Thesis
Develop and validate an actuarial analytics solution with its solvency impact, defended before a panel.
Career opportunities

Where this takes you

Pricing actuaryInsurance data scientistTelematics analystPricing managerActuarial data engineerUsage-based insurance 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.

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 (scikit-learn, LightGBM), Apache Spark, QGIS and SAS, 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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