Course in Reinsurance Integration with IoT and Big Data
Online course of 300 h in reinsurance integration with IoT and Big Data. 6 modules covering Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2016/679 (GDPR) and Apache Kafka. Assigned tutor and open enrolment.
Course in Reinsurance Integration with IoT and Big Data — studying from Ireland
Ireland is an established international hub for insurance, reinsurance and captive insurance, with Dublin hosting a large concentration of insurers, brokers and actuarial functions. The Central Bank of Ireland regulates insurance undertakings and the sector's solvency and conduct requirements. Engineers and analysts in this area work on risk modelling, catastrophe analysis, pricing and data-driven underwriting, often combining technical, statistical and regulatory expertise. Professional qualifications and standards are maintained by the Society of Actuaries in Ireland and related bodies.
Fees for Ireland: €546 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Which professional bodies in Ireland relate to this area, and is this qualification recognised by them?
In Ireland, actuarial work is associated with the Society of Actuaries in Ireland, and risk and data professionals may also look to bodies such as the Institute of Banking. SEIUM's qualifications are private and not accredited by any national body, so they do not confer membership or exemptions from these bodies. Confirm any exemption policies directly with the relevant institute before enrolling.
How is training like this typically viewed by employers in Ireland's insurance and risk sector?
Insurers and financial firms in Ireland generally value recognised professional qualifications most highly. A private online qualification is judged on its content and on your demonstrated skills, including your final project. Employers decide whether it is relevant to a given role, so it is best used to support existing experience rather than as a standalone credential.
What is the practical workload and language requirement for studying this online?
Programmes are taught entirely in English and delivered 100% online, so you can study from anywhere in Ireland or abroad. Workload depends on the programme length: specialist courses are 300 hours, postgraduate diplomas 800 hours and master's programmes 1,900 hours. Each student has an assigned technical tutor, and fees can be paid in instalments.
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
Explain how connected data is transforming reinsurance.
Design an architecture for capturing and processing IoT data.
Assess the quality and representativeness of sensor data.
Apply connected data to underwriting and pricing.
Build analytical models for ceded portfolios.
Incorporate contractual clauses on data access and use.
Establish a governance framework compliant with GDPR, DORA and the Data Act.
Who it's for
- Insurance data engineer wishing to specialise in reinsurance integration with IoT and Big Data.
- Data-driven reinsurance underwriter wishing to specialise in reinsurance integration with IoT and Big Data.
- Reinsurance data scientist wishing to specialise in reinsurance integration with IoT and Big Data.
- IoT solutions architect wishing to specialise in reinsurance integration with IoT and Big Data.
- Reinsurance pricing analyst wishing to specialise in reinsurance integration with IoT and Big Data.
- Data governance manager wishing to specialise in reinsurance integration with IoT and Big Data.
- Graduates in engineering or sciences seeking to enter reinsurance.
What you'll work with
- Regulation (EU) 2023/2854 (Data Act)
- Regulation (EU) 2016/679 (GDPR)
- Regulation (EU) 2022/2554 (DORA)
- ISO/IEC 30141 (IoT reference architecture)
- ISO 8000 (data quality)
- Regulation (EU) 2024/1689 on artificial intelligence
- ISO/IEC 27001
6 modules · 300 h
1.1What data contributes to the reinsurer1.2Sensors on fleets, vessels and platforms1.3Telemetry and operational data1.4Satellite and observation data1.5Meteorological and exposure data1.6Cedant data and its granularity1.7Sector vocabulary and acronyms1.8Realistic expectations versus the promise1.9Barriers to data access1.10Exercise: inventory of sources for a line of business
2.1Capture, transmission and storage2.2Normalisation and data model2.3Quality, gaps and outliers2.4Asset identification and traceability2.5Data frequency and latency2.6Infrastructure cost2.7Integration with the reinsurer's systems2.8Data governance2.9Information security2.10Practice: specification of a data pipeline
3.1Risk assessment with real data3.2Fine-grained portfolio segmentation3.3Pricing based on dynamic exposure3.4Detecting cedant deviations3.5Validation of information received3.6Loss prevention and reduction3.7Early warnings for events3.8Parametric products enabled by data3.9Limits of the approach3.10Practice: design of a data-driven product
4.1Predictive models applied to the line4.2Validation and overfitting risk4.3Explainability for the underwriter4.4Integration with catastrophe models4.5Monitoring model performance4.6Drift and retraining4.7Bias and its review4.8Computing infrastructure4.9Teams and necessary skills4.10Practice: validation plan for a model
5.1Data ownership and its transfer5.2Contractual clauses on data5.3Protection of personal data involved5.4Applicable Artificial Intelligence Regulation5.5Confidentiality towards the cedant5.6Transparency with the final insured5.7Governance of data use5.8Reputational risk5.9Return on investment5.10Practice: data clause for a treaty
6.1Choice of line of business and data sources6.2Proposed data architecture6.3Application in underwriting or product6.4Analytical model and its validation6.5Regulatory and contractual analysis6.6Governance and security6.7Cost and return model6.8Implementation plan6.9Project report6.10Project defence
How you'll learn
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 Apache Kafka, Databricks, Azure IoT Hub, Python (scikit-learn), in free, academic or evaluation versions. Tests are studied using real procedures for designing connected data architectures, underwriting models with IoT data, and quality analysis of sensor streams. 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.
Explain how connected data is transforming reinsurance.
Design an architecture for capturing and processing IoT data.
Assess the quality and representativeness of sensor data.
Apply connected data to underwriting and pricing.
Build analytical models for ceded portfolios.
Where this takes you
Requirements
- No prior degree required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient 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 an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with Apache Kafka, Databricks, Azure IoT Hub, Python (scikit-learn). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute own diploma upon passing the continuous assessment and the integrating 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 credited towards the advanced programme. Check the credit recognition policy when you enrol.
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