Course in Data Analytics for Shared Mobility Insurance
300-hour online course in data analytics for shared mobility insurance. 6 modules covering Regulation (EU) 2016/679 (GDPR), Organic Law 3/2018 (LOPDGDD) and Python (pandas, scikit-learn). Assigned tutor and open enrolment.
Course in Data Analytics for Shared Mobility Insurance — studying from New Zealand
New Zealand's insurance and financial services sector is concentrated in Auckland and Wellington, with a strong focus on general insurance, life insurance and risk management. Natural hazard risk, particularly from earthquakes, is a major consideration, and the Earthquake Commission provides residential natural disaster cover. Actuarial and risk data professionals work for insurers, reinsurers, regulators and consultancies, with oversight from the Reserve Bank of New Zealand and the Financial Markets Authority.
Fees for New Zealand: NZ$1,080 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
Which New Zealand bodies recognise actuarial and risk qualifications?
The Actuarial Society of New Zealand and the New Zealand Institute of Actuaries work alongside international bodies such as the Institute and Faculty of Actuaries. Membership and professional credentials in these fields are set by those bodies, not by private providers. SEIUM's qualifications are private and not accredited by any national body, so their standing depends on how your employer or the relevant body assesses them.
Do insurers and financial firms in New Zealand hire people with data and risk skills?
New Zealand's insurance, banking and government sectors do employ people in risk, pricing and data analysis, and the Reserve Bank and insurers both draw on these skills. Hiring usually depends on demonstrated technical ability and professional credentials. A SEIUM qualification can support your application, but whether it is valued is up to each employer.
How is the programme delivered and paid for?
The programme is 100% online and taught in English, so no student visa is needed to study from New Zealand. You are assigned a technical tutor and complete a final project. Fees are payable in instalments, and employer sponsorship is possible. Time commitment varies by programme, from 300 hours for specialist courses to 1,900 hours for master's programmes.
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
Identify the data generated by shared mobility platforms and its insurance value.
Build an analytical base integrating trips, vehicles, users and incidents.
Segment users and zones through descriptive and geospatial analysis.
Train predictive models of incidents and claims per trip.
Translate results into pricing, prevention and operational decisions.
Apply principles of privacy, minimisation and data governance.
Develop a complete analytics project for a shared mobility operator.
Who it's for
- Mobility data analyst who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Insurance data scientist who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Usage-based products actuary who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Risk manager at mobility operators who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Insurtech product analyst who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Data governance manager who wants to specialise in Data Analytics for Shared Mobility Insurance.
- Engineering or science graduates looking to enter shared mobility.
What you'll work with
- Regulation (EU) 2016/679 (GDPR)
- Organic Law 3/2018 (LOPDGDD)
- Directive 2009/103/EC (motor vehicle insurance)
- Directive (EU) 2021/2118 (amendment of motor insurance)
- Regulation (EU) 2024/1689 (AI Act)
- General Bikeshare Feed Specification (GBFS)
- Mobility Data Specification (MDS)
6 modules · 300 h
1.1Service models: carsharing, motorcycle sharing, e-scooters and vehicles with a driver1.2Vehicle with many users: implications for analysis1.3Booking, unlocking, trip and return records1.4Exposure measured in minutes, trips and vehicle-days1.5Identifying the user driving on each trip1.6Vehicle and battery status data1.7Operating zones, free-floating parking and stations1.8Fleet rotation and vehicle lifespan1.9Data quality and gaps in the records1.10Workshop: data structure of a carsharing operator
2.1Unit of analysis: trip, user, vehicle or period2.2Linking a claim to a specific trip2.3Claims without a report and damage detected at inspection2.4Attributing damage when several users follow each other2.5User variables: tenure, prior use and time of day2.6Vehicle variables: model, age and condition2.7Context variables: zone, weather and day of the week2.8Normalisation by actual exposure2.9Handling parking damage2.10Practical: building the analysis table
3.1Frequency and average cost by service type3.2Damage distribution by zone and by hour3.3High-risk users: identification and stability of the pattern3.4Learning curve of the new user3.5Vehicles with abnormal claim rates3.6Analysis of the small-damage tail3.7Geographic visualisation of claims3.8Comparison between cities and between fleets3.9Seasonality and the effect of one-off events3.10Practical: descriptive report for an urban fleet
4.1Claim prediction per trip and per user4.2Classification models and their calibration4.3Prediction of damage cost4.4User scoring and its operational use4.5Risk models by parking zone4.6Detection of vehicle misuse4.7Prediction of undeclared damage4.8Temporal validation and model stability4.9Ethical limits in user scoring4.10Practical: risk model per trip
5.1Designing the service tariff and damage surcharge5.2User excess and its effect on behaviour5.3Fleet decisions: models, zones and redistribution5.4Access restrictions for high-risk users5.5Preventive maintenance and review after incidents5.6Negotiating fleet insurance with your own data5.7Risk sharing between operator and insurer5.8Self-insurance programmes and excess funds5.9Measuring the effect of each measure5.10Practical: claims reduction plan using data
6.1Legal basis for processing user and trip data6.2Minimisation and retention on mobility platforms6.3Informing users about the use of their data6.4Sharing data with the insurer: limits and agreements6.5Fairness in user scoring6.6Model governance and accountable owners6.7Industrialising calculation and data pipelines6.8Operator indicators and dashboard6.9Common mistakes in shared mobility analytics6.10Final project: complete analytics for a mobility operator
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), PostgreSQL/PostGIS, Kepler.gl, Power BI, QGIS, in free, academic or evaluation versions. Tests are studied using real analysis procedures for e-scooter and shared bicycle trips, user segmentation by usage pattern, and incident probability models per trip. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the data generated by shared mobility platforms and its insurance value.
Build an analytical base integrating trips, vehicles, users and incidents.
Segment users and zones through descriptive and geospatial analysis.
Train predictive models of incidents and claims per trip.
Translate results into pricing, prevention and operational decisions.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
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 300 hours spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The course uses Python (pandas, scikit-learn), PostgreSQL/PostGIS, Kepler.gl, Power BI, QGIS. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own-award diploma from SEIUM upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a Course or Postgraduate Diploma in the same area are credited towards the higher-level programme. Check the recognition policy when you enrol.
Related programmes
Course in Innovative Business Models in Insurtech
Online 300-hour course in innovative business models in insurtech. 6 modules covering Directive (EU) 2016/97 (insurance distribution, IDD), Law 7/2020 (digital transformation of the financial system,…
Insurance and Blockchain Course under Regulatory Frameworks
Online 300-hour course in insurance and blockchain under regulatory frameworks. 6 modules covering Regulation (EU) 2022/2554 (Digital Operational Resilience Act, DORA), Regulation (EU) 2016/679 (GDPR) and…
Claims Management in Aviation Course
Online 300-hour course in claims management in aviation. 6 modules covering the Montreal Convention 1999 (international air transport), Regulation (EC) 785/2004 (insurance requirements for air carriers…
More in Insurance, Risk & Actuarial Data
All Insurance, Risk & Actuarial Data programmes → · How to apply · Fees & financing · Scholarships