Diploma in Automated Civil Liability Models
Online postgraduate diploma of 800 hours in automated civil liability models. 8 modules covering civil liability for automated systems, product, motor and AI liability law, automated driving failure scenarios, liability allocation models, actuarial liability models, event data recorders and claims automation, insurance products and reinsurance, and an integrative case study, with an assigned technical tutor.
Diploma in Automated Civil Liability Models — studying from Malta
Insurance is one of Malta's established financial sectors, with a regulatory framework that has attracted captive insurers, reinsurers and specialist underwriters. This creates demand for professionals who can model risk, analyse claims data and support actuarial functions. Engineers with statistical skills are valued in pricing, reserving and risk management. Employers include licensed insurers, brokers, consultancies and the regulator itself, where quantitative and technical expertise is increasingly important.
Fees for Malta: €1,090 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Malta
Typical roles
Questions from Malta
Which professional bodies in Malta relate to insurance, risk and actuarial work, and does SEIUM's qualification fit?
SEIUM Institute's qualifications are private qualifications, not official Maltese state degrees, and they are not accredited by any national body. In Malta, insurance and financial services are supervised by the Malta Financial Services Authority, and professional actuarial qualifications are set by international bodies that Maltese firms commonly follow. Whether SEIUM's training is recognised is for employers or the relevant body to decide.
What roles in Malta might this training support in insurance or risk management?
Malta has a significant insurance, reinsurance and financial services sector, and firms there employ people in risk analysis, data and modelling roles. Whether a private engineering-focused qualification counts for a given role depends on the employer's requirements and the qualifications they already expect. Reviewing current job descriptions in Malta and asking employers directly will show how your training would be viewed.
How much time does this need each week, and what language level is required?
Master's programmes total 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours, delivered fully online. Spread over the programme length, this suits people working full-time if they set a steady weekly schedule. Teaching is in English, so you should be comfortable reading and writing technical English. Fees can be paid in instalments, and employer sponsorship is possible.
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
Understand civil liability for automated systems.
Apply motor, product and AI liability law.
Analyse automated driving failure scenarios.
Allocate liability between keepers, operators and manufacturers.
Model liability exposure actuarially.
Use vehicle data to automate claims handling.
Design, price and reinsure liability products.
Who it's for
- Motor and liability underwriters.
- Liability actuaries.
- Claims managers handling motor and product liability.
- Legal counsel in insurance and automotive.
- Risk managers of mobility operators.
- Product managers developing insurance for automated mobility.
- Engineering, law or science graduates seeking to enter liability insurance.
What you'll work with
- Solvency II (Directive 2009/138/EC) (EU prudential regime for insurers)
- IFRS 17 (international accounting standard for insurance contracts)
- Delegated Regulation (EU) 2015/35 (Solvency II implementing rules)
- EIOPA Guidelines (European supervisory guidance for insurance)
- ISO 31000 (risk management guidelines)
- Law 20/2015 (LOSSEAR) (Spanish law on the supervision of insurers)
- Regulation (EU) 2016/679 (GDPR) (personal data protection)
- DORA (Regulation (EU) 2022/2554) (digital operational resilience)
- AI Act (Regulation (EU) 2024/1689) (EU rules on artificial intelligence)
- R
- Python (pandas, scikit-learn)
- SAS
- Prophet
- ResQ
8 modules
1.1From driver liability to system liability1.2Levels of driving automation (SAE J3016)1.3Automated systems beyond vehicles: robots and drones1.4Stakeholders: users, manufacturers, software providers and operators1.5Third-party liability insurance1.6Product liability insurance1.7Recourse between insurers and manufacturers1.8Market developments1.9Ethical issues1.10Guided exercise: liability map of an automated shuttle service
2.1Motor Insurance Directive 2009/103/EC as amended2.2Product Liability Directive (EU) 2024/28532.3AI Act, Regulation (EU) 2024/16892.4UK Automated and Electric Vehicles Act 20182.5UK Automated Vehicles Act 20242.6Germany's Road Traffic Act amendments for automated driving2.7UNECE Regulation No 157 (ALKS)2.8Spanish motor liability law (RDL 8/2004)2.9Strict liability versus fault-based regimes2.10Exercise: comparative liability analysis
3.1Sensors, perception and decision systems3.2Software and cybersecurity failures3.3Over-the-air updates3.4Human-machine handover3.5Operational design domain3.6Mixed traffic interactions3.7Failure modes and accident scenarios3.8Safety assurance and validation3.9Remote operation3.10Workshop: accident scenario analysis
4.1Strict liability of the keeper or operator4.2Manufacturer liability4.3Software provider liability4.4Shared liability and apportionment4.5Recourse mechanisms4.6Compensation funds4.7No-fault schemes4.8Contractual allocation in fleets4.9Litigation trends4.10Exercise: liability allocation in a mixed scenario
5.1Frequency and severity of liability claims5.2Bodily injury claims modelling5.3Impact of automation on claim frequency5.4Severity shifts and repair costs5.5Scenario-based modelling5.6GLMs for liability exposure5.7Large loss and tail modelling5.8Uncertainty in emerging risks5.9Model validation5.10Laboratory: liability exposure model in R
6.1Event data recorders and DSSAD6.2Data access for insurers6.3Data protection and the Data Act6.4Automated claims triage6.5Causation analysis with vehicle data6.6Fraud detection6.7Subrogation automation6.8Claims KPIs6.9AI governance in claims6.10Laboratory: claims triage model in Python
7.1Motor liability products for automated vehicles7.2Product liability and recall covers7.3Fleet and mobility operator policies7.4Cyber liability components7.5Pricing approaches7.6Deductibles and limits7.7Reinsurance of emerging liability7.8Accumulation and systemic risk7.9Portfolio monitoring7.10Exercise: pricing an automated fleet policy
8.1Case definition and fleet profile8.2Legal framework8.3Technology and failure scenarios8.4Liability allocation8.5Exposure model8.6Claims process design8.7Product and pricing8.8Reinsurance strategy8.9Final report and presentation8.10Final project: tutor review of the liability model
How you'll learn
A 100% online programme 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 each intake, so it can be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, in free, academic or evaluation versions. Case studies are built on real workflows from an actuarial modelling environment, a claims data platform and a catastrophe scenario simulator. Assessment is continuous by module, plus the final integrated project.
Standards covered: Solvency II (Directive 2009/138/EC), IFRS 17, Delegated Regulation (EU) 2015/35, EIOPA Guidelines, ISO 31000, Law 20/2015 (LOSSEAR), Regulation (EU) 2016/679 (GDPR), DORA (Regulation (EU) 2022/2554), AI Act (Regulation (EU) 2024/1689).
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
- Prior technical training or professional experience in the sector. No university degree is required.
- An 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 800 hours over 6–8 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 (pandas, scikit-learn), SAS, Prophet and ResQ, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), 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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