Master’s in Health and Performance Actuarial Engineering for Athletes and Driver Fleets
Online master's programme of 1,900 hours in Health and Performance Actuarial Engineering for Athletes and Driver Fleets. 12 modules covering personal insurance and health risk, sports and professional driver risk, sensors and wearables, actuarial health modelling, performance analytics, prevention, products, claims and rehabilitation, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Apply actuarial techniques to health and disability risks.
Analyse the specific risks of athletes and professional drivers.
Integrate sensor and wearable data into risk assessment.
Build survival and multi-state models for benefits.
Design prevention programmes with a measurable impact on claims.
Manage injury claims and rehabilitation processes.
Ensure the ethical and legal processing of health data.
Who it's for
- Personal insurance actuaries working on health, disability and accident risks.
- Sports risk underwriters covering athletes, clubs and federations.
- Health and performance analysts working with wearable and training data.
- Fleet road safety managers responsible for driver health and fatigue.
- Injury claims handlers managing rehabilitation and return to work.
- Corporate wellbeing consultants designing prevention programmes.
- Engineering or science graduates aiming to enter life and health insurance.
What you'll work with
- Regulation (EU) 2016/679 (GDPR: personal data protection)
- Law 41/2002 (Spanish law on patient autonomy and clinical information)
- Law 31/1995 (Spanish Occupational Risk Prevention Act)
- Regulation (EC) 561/2006 (driving times and rest periods)
- Directive 2009/138/EC (Solvency II: insurer solvency framework)
- Royal Decree 818/2009 (Spanish General Driver Regulations)
- ISO 39001:2012 (road traffic safety management systems)
- World Anti-Doping Code (global anti-doping rules in sport)
- R (survival)
- Python (pandas, lifelines)
- Catapult OpenField
- Kinomap
- Power BI
12 modules · 1900 h
1.1Health, life and accident insurance1.2Temporary and permanent disability1.3Mortality and morbidity tables1.4PASEM biometric tables1.5Multi-state disability models1.6Medical underwriting1.7Exclusions and waiting periods1.8Group insurance for companies1.9Mathematical reserves in personal insurance1.10Workshop: calculating a disability premium
2.1Injuries in professional sport2.2Epidemiology of sports injuries2.3Compulsory sports insurance2.4Loss-of-licence and career-ending insurance2.5Valuation of the athlete as an asset2.6Insurance for clubs and federations2.7Risk in motorsport2.8Concussions and neurological injuries2.9Return-to-competition protocols2.10Case study: disability policy for an athlete
3.1Work-related road accidents3.2Fatigue and drowsiness at the wheel3.3Driving and rest times3.4Sleep disorders and apnoea3.5Cardiovascular disease in drivers3.6Ergonomics and musculoskeletal disorders3.7Alcohol and drug use3.8Medical examinations for drivers3.9Commuting and on-duty accidents3.10Practical exercise: accident analysis of a fleet
4.1Heart rate and HRV monitoring4.2Accelerometry and training load4.3Sports GPS and performance metrics4.4Fatigue and drowsiness sensors4.5Cab cameras and distraction detection4.6Device accuracy and validation4.7Integration of wearable data4.8Interoperability with HL7 FHIR4.9Health data as a special category4.10Lab: heart rate variability analysis
5.1Injury frequency models5.2Sick leave duration models5.3Survival analysis and Kaplan-Meier5.4Cox proportional hazards model5.5Applied multi-state models5.6Credibility in small groups5.7Experience rating and group premium adjustment5.8Valuation of benefits5.9Uncertainty and scenarios5.10Practical exercise: Cox model for sick leave duration
6.1Acute and chronic training load6.2Injury risk models6.3Machine learning on sports data6.4Safe driving indicators6.5Fatigue scoring in fleets6.6Integration of telematics and physiological data6.7Visualisation for coaching staff6.8Predictive limitations6.9Validation of risk models6.10Case study: injury risk model
7.1Injury prevention programmes7.2Corporate wellbeing and insurance7.3Premium incentives for healthy habits7.4Training in efficient and safe driving7.5Fatigue management in fleets7.6Safe mobility plans7.7Evaluating the effect of interventions7.8Experimental design in prevention7.9Return on investment in prevention7.10Workshop: fatigue management programme
8.1Design of covers for athletes8.2Accident insurance for drivers8.3Group policies for fleets8.4Underwriting based on health data8.5Ethical limits of underwriting8.6Coinsurance and reinsurance of large sums insured8.7Liability covers8.8Group rating8.9Contractual documentation8.10Practical exercise: technical note for a group insurance product
9.1Management of injury claims9.2Assessment of personal injury9.3Road traffic compensation scale (baremo) and assessment9.4Rehabilitation and return to work9.5Management of complex cases9.6Injury fraud9.7Coordination with mutual insurance partners (mutuas)9.8Claims duration indicators9.9Communication with the injured party9.10Case study: managing a serious injury claim
10.1Health data under the GDPR10.2Consent and voluntariness10.3Discrimination based on health and genetics10.4Patient autonomy law10.5Occupational risk prevention10.6Driving time regulations10.7Sporting integrity and anti-doping10.8The AI Act and high-risk systems10.9Governance of sensitive data10.10Workshop: impact assessment of a wearables programme
11.1Profitability of personal insurance11.2Specialised distribution channels11.3Partnerships with clubs and transport companies11.4Retention and policyholder experience11.5Technical indicators11.6Solvency of health underwriting risk11.7Group reinsurance11.8Insurtech innovation in health11.9Business plan for a product11.10Practical exercise: business plan for a fleet insurance product
12.1Selection of the group to be studied12.2Review of medical and actuarial evidence12.3Dataset design12.4Risk modelling12.5Design of a product or prevention programme12.6Economic evaluation12.7Ethical and privacy analysis12.8Implementation plan12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R (survival), Python (pandas, lifelines), Catapult OpenField, Kinomap and Power BI, in free, academic or evaluation versions. Exercises are based on real procedures: modelling sick leave duration in driver groups, analysing training load and injury risk, and evaluating fatigue management programmes in fleets. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2016/679 (GDPR), Law 41/2002 on patient autonomy, Law 31/1995 on Occupational Risk Prevention, Regulation (EC) 561/2006 on driving and rest times, Directive 2009/138/EC (Solvency II), Royal Decree 818/2009 (General Driver Regulations), ISO 39001:2012 and the World Anti-Doping Code.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme 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.
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 (survival), Python (pandas, lifelines), Catapult OpenField, Kinomap and 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, plus a suitable computer 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.
Related programmes
Master’s in Fraud, Anomalies and Advanced Detection in Mobility Insurance Engineering
Online master's programme of 1,900 hours in Fraud, Anomaly and Advanced Detection Engineering in Mobility Insurance. 12 modules covering fraud typologies, detection data, anomaly statistics, supervised…
Master’s in ESG, Green Taxonomy and Energy Transition Insurance Engineering
Online master's programme of 1,900 hours in ESG, green taxonomy and energy transition insurance engineering: 12 modules covering sustainability in insurance, the EU Taxonomy, sustainability disclosure,…
Master’s in Underwriting, Product & Pricing Engineering
Online master's programme of 1,900 hours in underwriting, product and pricing engineering, structured in 12 modules covering the insurance business in mobility, regulation, actuarial premium calculation,…
More in Insurance, Risk & Actuarial Data
All Insurance, Risk & Actuarial Data programmes → · How to apply · Fees & financing · Scholarships