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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. 1.1. Compulsory third-party liability insurance
  2. 1.2. Voluntary own-damage covers
  3. 1.3. Personal injury compensation scale
  4. 1.4. Spanish motor insurance market
  5. 1.5. Corporate fleet insurance
  6. 1.6. Fleet risk management
  7. 1.7. Road accident rates and risk factors
  8. 1.8. Technical profitability of the line
  9. 1.9. Mobility trends
  10. 1.10. Case study: technical account of a fleet portfolio
  1. 2.1. In-vehicle telematics devices
  2. 2.2. Mobile driving apps
  3. 2.3. Connected vehicle data
  4. 2.4. Data quality and validation
  5. 2.5. Sampling frequency and trips
  6. 2.6. Crash detection
  7. 2.7. Insurance telematics platforms
  8. 2.8. Costs of the telematics solution
  9. 2.9. Market providers
  10. 2.10. Laboratory: processing telematics trips
  1. 3.1. Pay-per-kilometre
  2. 3.2. Pay-how-you-drive
  3. 3.3. Pay-per-time-of-use
  4. 3.4. Hybrid fixed and variable premium products
  5. 3.5. International pay-per-use experience
  6. 3.6. Attracting low-risk drivers
  7. 3.7. Effect on claims
  8. 3.8. Regulatory fit of pay-per-use
  9. 3.9. Communication with the customer
  10. 3.10. Practical exercise: designing a per-kilometre tariff
  1. 4.1. Relevant driving variables
  2. 4.2. Harsh acceleration and braking
  3. 4.3. Speed and speeding
  4. 4.4. Mobile phone use while driving
  5. 4.5. Night-time and weekend driving
  6. 4.6. Road and traffic context
  7. 4.7. Building a score
  8. 4.8. Validation against claims
  9. 4.9. Driver feedback
  10. 4.10. Laboratory: driving score with Python
  1. 5.1. Integrating telematics into the GLM
  2. 5.2. Machine learning models
  3. 5.3. Traditional versus telematics variables
  4. 5.4. Credibility of driving data
  5. 5.5. Discounts and bonuses
  6. 5.6. Preventing adverse selection
  7. 5.7. Tariff stability
  8. 5.8. Portfolio impact testing
  9. 5.9. Model governance
  10. 5.10. Practical exercise: frequency model with telematics variables
  1. 6.1. Claims diagnosis of a fleet
  2. 6.2. Driver training
  3. 6.3. Vehicle use policies
  4. 6.4. Driver assistance systems
  5. 6.5. Video telematics and cameras
  6. 6.6. Fatigue and driving hours
  7. 6.7. Preventive vehicle maintenance
  8. 6.8. Fleet safety indicators
  9. 6.9. Prevention plans
  10. 6.10. Case study: claims reduction plan for a fleet
  1. 7.1. Catalogue of current ADAS systems
  2. 7.2. EU General Safety Regulation
  3. 7.3. Effect of ADAS on frequency
  4. 7.4. Repair cost of vehicles with ADAS
  5. 7.5. Sensor calibration after a claim
  6. 7.6. Levels of automation
  7. 7.7. Liability in automated vehicles
  8. 7.8. Event data recorder data
  9. 7.9. Future impact on the line
  10. 7.10. Practical exercise: impact of ADAS on average cost
  1. 8.1. Automatic accident notification
  2. 8.2. Claims reconstruction with data
  3. 8.3. Fraud detection
  4. 8.4. Digital loss assessment with images
  5. 8.5. Repair management in workshops
  6. 8.6. Connected roadside assistance
  7. 8.7. Recoveries between insurers
  8. 8.8. Claims management indicators
  9. 8.9. Customer experience
  10. 8.10. Case study: reconstructing a claim with telematics
  1. 9.1. Protection of driving data
  2. 9.2. Legal bases for processing
  3. 9.3. Transparency with the policyholder
  4. 9.4. Data minimisation
  5. 9.5. Automated decisions about the policyholder
  6. 9.6. Risk of indirect discrimination
  7. 9.7. EU Data Act
  8. 9.8. Access to vehicle data
  9. 9.9. Ethical data governance
  10. 9.10. Practical exercise: data protection impact assessment
  1. 10.1. Motor distribution channels
  2. 10.2. Partnerships with vehicle manufacturers
  3. 10.3. Insurance embedded in the purchase
  4. 10.4. Policyholder mobile apps
  5. 10.5. Gamification and driving habits
  6. 10.6. Retention and renewal
  7. 10.7. Communicating variable prices
  8. 10.8. Customer satisfaction
  9. 10.9. Product commercial indicators
  10. 10.10. Case study: launching a driving app
  1. 11.1. Customer acquisition cost
  2. 11.2. Telematics cost per policy
  3. 11.3. Claims savings
  4. 11.4. Return on investment
  5. 11.5. Scaling pilot programmes
  6. 11.6. Technology integration with systems
  7. 11.7. Business model risks
  8. 11.8. Success and failure stories
  9. 11.9. Market trends
  10. 11.10. Practical exercise: pay-per-use business case
  1. 12.1. Definition of the product or programme
  2. 12.2. Telematics data architecture
  3. 12.3. Scoring model
  4. 12.4. Pricing model
  5. 12.5. Risk prevention plan
  6. 12.6. Privacy analysis
  7. 12.7. Business case
  8. 12.8. Launch plan
  9. 12.9. Thesis report
  10. 12.10. Master'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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