Course in Underwriting Autonomous Vehicle Policies
Online 300-hour course in underwriting policies for autonomous vehicles. 6 modules covering SAE J3016 (levels of automation), UN Regulation No. 157 (ALKS) and Python (pandas). Assigned tutor and open enrolment.
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 what changes in underwriting an autonomous vehicle compared with a conventional one.
Interpret the regulatory framework and comparative liability models.
Technically assess the driving system and its operational design domain.
Price the risk and structure the insurance programme.
Determine the cause of a claim from vehicle data.
Analyse the evolution of the autonomous mobility insurance market.
Prepare a complete underwriting proposal for an autonomous operator.
Who it's for
- Autonomous mobility risk underwriter who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Fleet underwriter who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Automotive risk engineer who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Motor pricing actuary who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Complex claims handler who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Mobility broker who wants to specialise in Underwriting Autonomous Vehicle Policies.
- Graduates in engineering or science looking to join insurance companies.
What you'll work with
- SAE J3016 (levels of automation)
- UN Regulation No. 157 (ALKS)
- Automated and Electric Vehicles Act 2018 (UK)
- Automated Vehicles Act 2024 (UK)
- Directive 2009/103/EC (motor vehicle insurance)
- ISO 21448 (safety of the intended functionality, SOTIF)
- ISO 26262 (functional safety of road vehicles)
6 modules · 300 h
1.1Levels of automation and their effect on underwriting1.2From driver to system: shifting risk1.3Manufacturer, integrator, operator and owner1.4Private vehicle, fleet and transport service1.5Declared operating environment and its limits1.6Dependence on maps, connectivity and updates1.7Data available for underwriting and access to it1.8Scarcity of historical experience1.9Social expectation and regulatory pressure1.10Workshop: underwriting sheet for an automated vehicle
2.1Type approval of automated driving systems2.2Circulation authorisations and imposed conditions2.3Compulsory insurance and accreditation for automated vehicles2.4Keeper liability versus product liability2.5Software and update liability2.6Event data recording and insurer access2.7Allocation between motor insurer and product insurer2.8Direct action by the injured party and subsequent recovery2.9Data protection for occupants and the environment2.10Guided reading of a circulation authorisation
3.1Operational design domain and its verification3.2Perception capability and known limitations3.3Management of the handover of control to the driver3.4Degraded modes and minimal risk manoeuvre3.5Update history and version management3.6Cybersecurity of the vehicle and connectivity3.7Disengagement and programme incident data3.8Sensor maintenance and calibration3.9Information the underwriter should require from the manufacturer3.10Practice: underwriting technical questionnaire
4.1Exposure: mileage, environment and driving mode4.2Pricing with scarce experience and use of expert judgement4.3Premium separation by automation tiers4.4Deductibles and insured's participation4.5Limits and sublimits by type of liability4.6Cyber risk and data loss coverage4.7Cover for high-value onboard equipment4.8Layered programmes and reinsurance4.9Periodic price review based on experience4.10Practice: structure of a programme for an automated fleet
5.1Immediate protocol after a claim with an active system5.2Access to vehicle data and its integrity5.3Determining who was driving: person or system5.4Event reconstruction and technical assessment5.5Coordination with manufacturer and authorities5.6Recovery from the manufacturer and software supplier5.7Managing the injured party's expectations5.8Public communication and reputational risk5.9Incorporating lessons learned into underwriting5.10Case study: collision with an automated system active
6.1Current appetite of the insurance market6.2Capacity and role of reinsurance6.3Expected evolution of the motor line6.4Effect on frequency and average repair cost6.5Cost of sensor repair and recalibration6.6Transition of mixed portfolios over the years6.7Collaboration with manufacturers and data access6.8Training of underwriters and loss adjusters6.9Common mistakes when underwriting these risks6.10Final project: underwriting policy for automated vehicles
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: students progress at their own pace within the intake, which makes it possible to combine the course with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas), R, Advanced Excel, Guidewire PolicyCenter, Power BI, in free, academic or evaluation versions. Tests are studied using real technical assessment procedures for an automated driving system for underwriting, structuring a programme for an autonomous fleet, and analysis of claims cases involving automated systems. Assessment is continuous per module plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify what changes in underwriting an autonomous vehicle compared with a conventional one.
Interpret the regulatory framework and comparative liability models.
Technically assess the driving system and its operational design domain.
Price the risk and structure the insurance programme.
Determine the cause of a claim from vehicle data.
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 reading.
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 comprises 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The course uses Python (pandas), R, Advanced Excel, Guidewire PolicyCenter and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM's own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.
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