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Postgraduate DiplomaInsurance, Risk & Actuarial Data Applications open

Postgraduate Diploma in Actuarial Validation of Vehicle Big Data

Online Postgraduate Diploma of 800 h in Actuarial Validation of Vehicle Big Data. 8 modules covering Directive 2009/138/EC (Solvency II), Articles 120 to 126 on model validation, Delegated Regulation (EU) 2015/35 (data quality) and Apache Spark. Assigned tutor and open enrolment.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Characterise the sources of vehicle big data and their limitations.

02

Design distributed storage and processing infrastructures.

03

Apply data quality and data governance frameworks to vehicle data.

04

Statistically validate data and variables through stability and representativeness tests.

05

Validate actuarial models using backtesting techniques and sensitivity analysis.

06

Document validation in line with supervisory requirements.

07

Explore advanced applications of vehicle data in insurance.

Profile

Who it's for

  • Actuarial validator looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Insurance data engineer looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Data quality manager looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Internal models actuary looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Model auditor looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Data governance manager looking to specialise in Actuarial Validation of Vehicle Big Data.
  • Engineering or science graduates seeking to enter motor insurers.
Standards & tools

What you'll work with

  • Directive 2009/138/EC (Solvency II), Articles 120 to 126 on model validation
  • Delegated Regulation (EU) 2015/35 (data quality)
  • ISO 8000 (data quality)
  • ISO/IEC 25012 (data quality model)
  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2016/679 (GDPR)
  • Regulation (EU) 2022/2554 (DORA)
Syllabus

8 modules · 800 h

  1. 1.1Sources: telematics, connected vehicles and workshops
  2. 1.2CAN bus data and OBD-II diagnostics
  3. 1.3EDR event records
  4. 1.4Manufacturer data and OEM platforms
  5. 1.5Volume, velocity and variety of data
  6. 1.6Data quality and sources of bias
  7. 1.7Data Act and access to vehicle data
  8. 1.8Actuarial uses of vehicle data
  9. 1.9Risks of data-driven decisions
  10. 1.10Case study: data source inventory for a fleet
  1. 2.1Lakehouse architectures
  2. 2.2Distributed processing with Spark
  3. 2.3Event streaming with Kafka
  4. 2.4Parquet and Delta Lake formats
  5. 2.5Large-scale queries with SQL
  6. 2.6Geospatial processing of trajectories
  7. 2.7Pipeline orchestration
  8. 2.8Computing and storage costs
  9. 2.9Security and access control
  10. 2.10Laboratory: Spark pipeline for vehicle trips
  1. 3.1Data quality dimensions
  2. 3.2Validation of rules and schemas
  3. 3.3Detection of erroneous GPS records
  4. 3.4Duplicates and fragmented trips
  5. 3.5Missing data and survivorship bias
  6. 3.6Lineage and traceability
  7. 3.7Data quality requirements in Solvency II
  8. 3.8Data governance and data owners
  9. 3.9Documentation and data dictionaries
  10. 3.10Practical: quality report on a telematics dataset
  1. 4.1Sample representativeness
  2. 4.2Participant selection bias
  3. 4.3Hypothesis testing and significance
  4. 4.4Temporal stability of distributions
  5. 4.5Population Stability Index (PSI)
  6. 4.6Variable correlation and multicollinearity
  7. 4.7Actuarial credibility of data
  8. 4.8Comparison with market data
  9. 4.9Robustness tests
  10. 4.10Laboratory: PSI and variable stability analysis
  1. 5.1Independent validation framework
  2. 5.2Frequency and severity backtesting
  3. 5.3Discrimination and calibration measures
  4. 5.4Out-of-sample and out-of-time validation
  5. 5.5Sensitivity analysis
  6. 5.6Benchmarking against simple models
  7. 5.7Model risk and its quantification
  8. 5.8Code review and reproducibility
  9. 5.9Validation report
  10. 5.10Practical: validation of a telematics frequency model
  1. 6.1The actuarial function under Solvency II
  2. 6.2EIOPA guidelines on data and governance
  3. 6.3European AI Regulation and high risk
  4. 6.4GDPR applied to vehicle data
  5. 6.5Fairness and non-discrimination in pricing
  6. 6.6Explainability for the supervisor
  7. 6.7Actuarial technical standards
  8. 6.8Internal and external audit
  9. 6.9Regulatory documentation
  10. 6.10Case study: responding to a supervisory request
  1. 7.1Vehicles with assisted driving and ADAS
  2. 7.2Levels of autonomy and liability
  3. 7.3Risk assessment of electric vehicles
  4. 7.4Battery data and fire risk
  5. 7.5Claims reconstruction with EDR
  6. 7.6Fraud detection with vehicle data
  7. 7.7Predictive maintenance and risk
  8. 7.8Reserving models with granular data
  9. 7.9Trends in the software-defined vehicle
  10. 7.10Practical: impact of ADAS on claims frequency
  1. 8.1Selection of the model and data
  2. 8.2Assessment of data quality
  3. 8.3Representativeness analysis
  4. 8.4Model validation
  5. 8.5Sensitivity tests
  6. 8.6Identification of weaknesses
  7. 8.7Improvement recommendations
  8. 8.8Validation report
  9. 8.9Dissertation and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

A 100 % online programme on the SEIUM Institute 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 study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, Databricks, Great Expectations, R (statistical validation), Python (pandas, SciPy), in free, academic or evaluation versions. Assessments are based on real audit procedures for connected-vehicle data quality, stability tests and backtesting of actuarial models, and an independent validation report. Assessment is continuous by module, plus the final integrative project.

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

Build it end to end

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

Selection of the model and data

Characterise the sources of vehicle big data and their limitations.

Assessment of data quality

Design distributed storage and processing infrastructures.

Representativeness analysis

Apply data quality and data governance frameworks to vehicle data.

Model validation

Statistically validate data and variables through stability and representativeness tests.

Sensitivity tests

Validate actuarial models using backtesting techniques and sensitivity analysis.

Career opportunities

Where this takes you

Actuarial validatorInsurance data engineerData quality managerInternal models actuaryModel auditorData governance manager
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient command 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.

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FAQ

Frequently asked questions

The programme totals 800 hours, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.

You will work with Apache Spark, Databricks, Great Expectations, R (statistical validation), Python (pandas, SciPy). Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

A SEIUM Institute diploma upon passing the continuous assessment and the 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 in the advanced programme. Check the recognition policy when you enrol.

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