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CourseInsurance, Risk & Actuarial Data Applications open

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.

Duration300 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Explain how connected data is transforming reinsurance.

02

Design an architecture for capturing and processing IoT data.

03

Assess the quality and representativeness of sensor data.

04

Apply connected data to underwriting and pricing.

05

Build analytical models for ceded portfolios.

06

Incorporate contractual clauses on data access and use.

07

Establish a governance framework compliant with GDPR, DORA and the Data Act.

Profile

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.
Standards & tools

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
Syllabus

6 modules · 300 h

  1. 1.1What data contributes to the reinsurer
  2. 1.2Sensors on fleets, vessels and platforms
  3. 1.3Telemetry and operational data
  4. 1.4Satellite and observation data
  5. 1.5Meteorological and exposure data
  6. 1.6Cedant data and its granularity
  7. 1.7Sector vocabulary and acronyms
  8. 1.8Realistic expectations versus the promise
  9. 1.9Barriers to data access
  10. 1.10Exercise: inventory of sources for a line of business
  1. 2.1Capture, transmission and storage
  2. 2.2Normalisation and data model
  3. 2.3Quality, gaps and outliers
  4. 2.4Asset identification and traceability
  5. 2.5Data frequency and latency
  6. 2.6Infrastructure cost
  7. 2.7Integration with the reinsurer's systems
  8. 2.8Data governance
  9. 2.9Information security
  10. 2.10Practice: specification of a data pipeline
  1. 3.1Risk assessment with real data
  2. 3.2Fine-grained portfolio segmentation
  3. 3.3Pricing based on dynamic exposure
  4. 3.4Detecting cedant deviations
  5. 3.5Validation of information received
  6. 3.6Loss prevention and reduction
  7. 3.7Early warnings for events
  8. 3.8Parametric products enabled by data
  9. 3.9Limits of the approach
  10. 3.10Practice: design of a data-driven product
  1. 4.1Predictive models applied to the line
  2. 4.2Validation and overfitting risk
  3. 4.3Explainability for the underwriter
  4. 4.4Integration with catastrophe models
  5. 4.5Monitoring model performance
  6. 4.6Drift and retraining
  7. 4.7Bias and its review
  8. 4.8Computing infrastructure
  9. 4.9Teams and necessary skills
  10. 4.10Practice: validation plan for a model
  1. 5.1Data ownership and its transfer
  2. 5.2Contractual clauses on data
  3. 5.3Protection of personal data involved
  4. 5.4Applicable Artificial Intelligence Regulation
  5. 5.5Confidentiality towards the cedant
  6. 5.6Transparency with the final insured
  7. 5.7Governance of data use
  8. 5.8Reputational risk
  9. 5.9Return on investment
  10. 5.10Practice: data clause for a treaty
  1. 6.1Choice of line of business and data sources
  2. 6.2Proposed data architecture
  3. 6.3Application in underwriting or product
  4. 6.4Analytical model and its validation
  5. 6.5Regulatory and contractual analysis
  6. 6.6Governance and security
  7. 6.7Cost and return model
  8. 6.8Implementation plan
  9. 6.9Project report
  10. 6.10Project defence
Methodology, software & labs

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.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Choice of line of business and data sources

Explain how connected data is transforming reinsurance.

Proposed data architecture

Design an architecture for capturing and processing IoT data.

Application in underwriting or product

Assess the quality and representativeness of sensor data.

Analytical model and its validation

Apply connected data to underwriting and pricing.

Regulatory and contractual analysis

Build analytical models for ceded portfolios.

Career opportunities

Where this takes you

Insurance data engineerData-driven reinsurance underwriterReinsurance data scientistIoT solutions architectReinsurance pricing analystData governance manager
Admissions & scholarships

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.

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FAQ

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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