Course in IoT Use in Industrial Vehicle Insurance
300-hour online course in IoT use in industrial vehicle insurance. 6 modules covering SAE J1939 (communication in heavy vehicles), ISO 15638 (telematics systems for regulated commercial vehicles) and AWS IoT Core. Assigned tutor and enrolment open.
Course in IoT Use in Industrial Vehicle Insurance — studying from Singapore
Singapore is a major insurance and reinsurance hub in Asia, regulated by the Monetary Authority of Singapore. Engineering skills support risk modelling, catastrophe analysis, data analytics and actuarial work for insurers and reinsurers. The sector draws on data science and quantitative methods, with demand from insurers, brokers and risk consultancies. The city's financial ecosystem makes it a common base for global risk and actuarial teams.
Fees for Singapore: S$639 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body. In Singapore, actuarial work is linked to the Singapore Actuarial Society, and insurance is regulated by the Monetary Authority of Singapore. Recognition of a private qualification is decided by employers or the relevant body, so confirm with them directly before you enrol.
How might this training be used by employers in Singapore?
Singapore's insurance, banking and risk functions employ people with analytical and data skills. An engineering-based risk and data programme may complement such roles, but it does not guarantee a position or a specific career outcome. Employers usually weigh professional exams, experience and demonstrated analytical ability, so your final project and work history will matter as much as the qualification title.
Can I study this alongside a full-time job, and can my employer help fund it?
Because the course is fully online and taught in English, you can study from Singapore without travelling, and no student visa is needed for online study. The workload is substantial, so plan carefully around your job. Fees can be paid in instalments, and employer sponsorship is possible; many Singapore companies support job-relevant study, so discuss it with your manager.
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 risks and assets of connected industrial vehicles.
Describe the architecture of sensors, data bus and IoT platform.
Build risk indicators from driving and machine data.
Apply predictive maintenance models to prevent breakdowns and claims.
Use IoT data in underwriting, pricing and claims management.
Meet the requirements for data access, privacy and cybersecurity.
Design the implementation of an IoT programme for an insured fleet.
Who it's for
- Fleet telematics analyst who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- Industrial fleet underwriter who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- IoT solutions engineer who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- Fleet risk prevention manager who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- Insurance data scientist who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- Connected insurance product manager who wants to specialise in IoT Use in Industrial Vehicle Insurance.
- Engineering or science graduates looking to enter road freight transport.
What you'll work with
- SAE J1939 (communication in heavy vehicles)
- ISO 15638 (telematics systems for regulated commercial vehicles)
- Regulation (EU) 2023/2854 (Data Act)
- Regulation (EU) 165/2014 (tachographs)
- Regulation (EU) 2016/679 (GDPR)
- Regulation (EU) 2024/2847 (cyber resilience)
- UN Regulation No. 155 (vehicle cybersecurity)
6 modules · 300 h
1.1Asset types: lorry, tractor unit, semi-trailer, crane and machinery1.2Construction vehicles and self-propelled agricultural machinery1.3Forklifts and warehouse handling equipment1.4Insured risks: damage, liability, breakdown and immobilisation1.5Machinery breakdown and the cost of downtime1.6Risk of overturning, entrapment and load falling1.7Theft of vehicle, cargo and equipment1.8Maintenance and its direct relationship with loss frequency1.9Which decisions would benefit from knowing the actual condition of the asset1.10Workshop: risk map of an industrial fleet
2.1CAN bus and native data of industrial vehicles2.2Tachograph, manufacturer telematics and add-on devices2.3Load, tilt, temperature and pressure sensors2.4Vibration sensors and component condition sensors2.5Location tracking and geofencing2.6Connectivity: coverage, local storage and deferred transmission2.7Ingestion and time-series storage platform2.8Normalisation across brands and models2.9Security of the device and the channel2.10Practice: designing the data architecture of a fleet
3.1Driving: speed, braking, acceleration and engine use3.2Overloading and incorrect load distribution3.3Operating hours and use outside working hours3.4Risky manoeuvres on sites and in warehouses3.5Temperature and pressure as signs of incipient failure3.6Vibration and degradation of bearings and transmission3.7Compliance with maintenance intervals3.8Composite risk indices per vehicle3.9Validating indicators against real claims3.10Practice: indicator dashboard for a lorry fleet
4.1Corrective, preventive and predictive maintenance4.2Anomaly detection in sensor series4.3Estimating the remaining useful life of a component4.4Alerts and their integration with the workshop4.5Avoided costs and programme return4.6Relationship between maintenance and breakdown cover4.7Policy conditions linked to maintenance4.8Manufacturer data and warranties4.9Limits of predictive maintenance4.10Case study: transmission failure anticipated by sensors
5.1Information that IoT provides to the underwriter5.2Pricing based on actual use and behaviour5.3Discounts for sensor installation and telemetry5.4Reconstructing the claim with vehicle data5.5Evidence of overloading or improper manoeuvring5.6Loss adjustment supported by the asset's history5.7Detection of undeclared claims and misuse5.8Recovery from third parties with telematic evidence5.9Prevention programmes agreed with the insurer5.10Practice: claim file with sensor data
6.1Digitalisation roadmap for a fleet6.2Programme costs and break-even point6.3Driver data and protection of employee data6.4Negotiation with the works council6.5Agreements with the telematics provider and exit plan6.6Governance of models and alerts6.7Training for drivers and fleet managers6.8Success indicators for the programme6.9Common mistakes when deploying IoT in industrial fleets6.10Final project: IoT and insurance programme for an industrial fleet
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is AWS IoT Core, Azure IoT Hub, Python (scikit-learn), Grafana, Webfleet, in free, academic or evaluation versions. Tests are studied using real procedures for analysing CAN data from lorry fleets, predictive maintenance models using sensor data, and calculation of risk indicators per driver and vehicle. Assessment is continuous across modules, 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 the risks and assets of connected industrial vehicles.
Describe the architecture of sensors, data bus and IoT platform.
Build risk indicators from driving and machine data.
Apply predictive maintenance models to prevent breakdowns and claims.
Use IoT data in underwriting, pricing and claims management.
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 comprises 300 hours in total, spread over 3 to 5 months. That is an average of between 10 and 15 hours per week, compatible with a full-time job.
You work with AWS IoT Core, Azure IoT Hub, Python (scikit-learn), Grafana, Webfleet. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the final integrative 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 recognised towards the higher programme. Check the recognition policy when you enrol.
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