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Master'sInsurance, Risk & Actuarial Data Applications open

Master’s in Motor and Fleet Insurance Engineering (UBI/Pay-per-Use)

Online master's programme of 1,900 hours in motor and fleet insurance engineering (UBI/pay-per-use), structured in 12 modules covering motor and fleet insurance, telematics, pay-per-use models, driving scores, telematics pricing, fleet risk management, ADAS, connected claims, data privacy and business cases, 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

Analyse motor and fleet insurance.

02

Select telematics solutions for insurance.

03

Design pay-per-use products.

04

Build validated driving scores.

05

Price using telematics variables.

06

Reduce fleet claims through prevention.

07

Manage privacy and ethics in the use of data.

Profile

Who it's for

  • Motor insurance actuaries.
  • Telematics data analysts.
  • Pay-per-use product managers.
  • Fleet risk managers.
  • Fleet underwriters.
  • Vehicle manufacturer partnership managers.
  • Engineering or science graduates seeking to enter the insurance sector.
Standards & tools

What you'll work with

  • Royal Legislative Decree 8/2004 (civil liability and insurance in motor vehicle traffic)
  • Directive (EU) 2021/2118 (motor vehicle insurance)
  • Law 35/2015 (compensation scale for road traffic accident damages)
  • Law 50/1980 on Insurance Contracts (Spanish insurance contract law)
  • Regulation (EU) 2019/2144 (general vehicle safety)
  • Regulation (EU) 2016/679 (data protection)
  • Regulation (EU) 2023/2854 (Data Act)
  • R
  • Python with pandas
  • WTW Emblem
  • Microsoft Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Compulsory third-party liability insurance
  2. 1.2Voluntary own-damage covers
  3. 1.3Personal injury compensation scale
  4. 1.4Spanish motor insurance market
  5. 1.5Corporate fleet insurance
  6. 1.6Fleet risk management
  7. 1.7Road accident rates and risk factors
  8. 1.8Technical profitability of the line
  9. 1.9Mobility trends
  10. 1.10Case study: technical account of a fleet portfolio
  1. 2.1In-vehicle telematics devices
  2. 2.2Mobile driving apps
  3. 2.3Connected vehicle data
  4. 2.4Data quality and validation
  5. 2.5Sampling frequency and trips
  6. 2.6Crash detection
  7. 2.7Insurance telematics platforms
  8. 2.8Costs of the telematics solution
  9. 2.9Market providers
  10. 2.10Laboratory: processing telematics trips
  1. 3.1Pay-per-kilometre
  2. 3.2Pay-how-you-drive
  3. 3.3Pay-per-time-of-use
  4. 3.4Hybrid fixed and variable premium products
  5. 3.5International pay-per-use experience
  6. 3.6Attracting low-risk drivers
  7. 3.7Effect on claims
  8. 3.8Regulatory fit of pay-per-use
  9. 3.9Communication with the customer
  10. 3.10Practical exercise: designing a per-kilometre tariff
  1. 4.1Relevant driving variables
  2. 4.2Harsh acceleration and braking
  3. 4.3Speed and speeding
  4. 4.4Mobile phone use while driving
  5. 4.5Night-time and weekend driving
  6. 4.6Road and traffic context
  7. 4.7Building a score
  8. 4.8Validation against claims
  9. 4.9Driver feedback
  10. 4.10Laboratory: driving score with Python
  1. 5.1Integrating telematics into the GLM
  2. 5.2Machine learning models
  3. 5.3Traditional versus telematics variables
  4. 5.4Credibility of driving data
  5. 5.5Discounts and bonuses
  6. 5.6Preventing adverse selection
  7. 5.7Tariff stability
  8. 5.8Portfolio impact testing
  9. 5.9Model governance
  10. 5.10Practical exercise: frequency model with telematics variables
  1. 6.1Claims diagnosis of a fleet
  2. 6.2Driver training
  3. 6.3Vehicle use policies
  4. 6.4Driver assistance systems
  5. 6.5Video telematics and cameras
  6. 6.6Fatigue and driving hours
  7. 6.7Preventive vehicle maintenance
  8. 6.8Fleet safety indicators
  9. 6.9Prevention plans
  10. 6.10Case study: claims reduction plan for a fleet
  1. 7.1Catalogue of current ADAS systems
  2. 7.2EU General Safety Regulation
  3. 7.3Effect of ADAS on frequency
  4. 7.4Repair cost of vehicles with ADAS
  5. 7.5Sensor calibration after a claim
  6. 7.6Levels of automation
  7. 7.7Liability in automated vehicles
  8. 7.8Event data recorder data
  9. 7.9Future impact on the line
  10. 7.10Practical exercise: impact of ADAS on average cost
  1. 8.1Automatic accident notification
  2. 8.2Claims reconstruction with data
  3. 8.3Fraud detection
  4. 8.4Digital loss assessment with images
  5. 8.5Repair management in workshops
  6. 8.6Connected roadside assistance
  7. 8.7Recoveries between insurers
  8. 8.8Claims management indicators
  9. 8.9Customer experience
  10. 8.10Case study: reconstructing a claim with telematics
  1. 9.1Protection of driving data
  2. 9.2Legal bases for processing
  3. 9.3Transparency with the policyholder
  4. 9.4Data minimisation
  5. 9.5Automated decisions about the policyholder
  6. 9.6Risk of indirect discrimination
  7. 9.7EU Data Act
  8. 9.8Access to vehicle data
  9. 9.9Ethical data governance
  10. 9.10Practical exercise: data protection impact assessment
  1. 10.1Motor distribution channels
  2. 10.2Partnerships with vehicle manufacturers
  3. 10.3Insurance embedded in the purchase
  4. 10.4Policyholder mobile apps
  5. 10.5Gamification and driving habits
  6. 10.6Retention and renewal
  7. 10.7Communicating variable prices
  8. 10.8Customer satisfaction
  9. 10.9Product commercial indicators
  10. 10.10Case study: launching a driving app
  1. 11.1Customer acquisition cost
  2. 11.2Telematics cost per policy
  3. 11.3Claims savings
  4. 11.4Return on investment
  5. 11.5Scaling pilot programmes
  6. 11.6Technology integration with systems
  7. 11.7Business model risks
  8. 11.8Success and failure stories
  9. 11.9Market trends
  10. 11.10Practical exercise: pay-per-use business case
  1. 12.1Definition of the product or programme
  2. 12.2Telematics data architecture
  3. 12.3Scoring model
  4. 12.4Pricing model
  5. 12.5Risk prevention plan
  6. 12.6Privacy analysis
  7. 12.7Business case
  8. 12.8Launch plan
  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 pandas, WTW Emblem and Microsoft Power BI, in free, academic or evaluation versions. Testing is studied through real procedures for processing driving data, telematics pricing models and fleet prevention plans. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Royal Legislative Decree 8/2004, Directive (EU) 2021/2118, Law 35/2015, Law 50/1980 on Insurance Contracts, Regulation (EU) 2019/2144, Regulation (EU) 2016/679, Regulation (EU) 2023/2854.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Royal Legislative Decree 8/2004, Directive (EU) 2021/2118 and Law 35/2015 applied to real cases, not as a theoretical annex.
Industry software:R, Python with pandas, WTW Emblem and Microsoft Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:a complete telematics insurance product or programme with data architecture, scoring and pricing models, prevention plan, privacy analysis and business case, 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.

Telematics data processing
Process telematics trips and assess data quality for an insurance programme.
Driving score
Build and validate a driving score in Python against claims experience.
Telematics pricing
Design a per-kilometre tariff and fit a frequency model with telematics variables.
Prevention and business case
Draw up a fleet claims reduction plan and a pay-per-use business case.
Master's Thesis
Develop a complete telematics insurance product or programme, defended before a panel.
Career opportunities

Where this takes you

Motor insurance actuaryTelematics data analystPay-per-use product managerFleet risk managerFleet underwriterVehicle manufacturer partnership 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.

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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, Python with pandas, WTW Emblem and Microsoft Power BI, 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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