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Master’s in Data Governance and MLOps for Actuaries Engineering

Online master's programme of 1,900 hours in data governance and MLOps engineering for actuaries, structured in 12 modules covering data governance, actuarial data quality, lineage and architecture, the model life cycle, MLOps, deployment and monitoring, responsible AI, privacy and security, model risk and transformation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design data governance frameworks tailored to an insurer.

02

Implement quality and lineage controls on actuarial data.

03

Manage the full life cycle of actuarial models.

04

Build reproducible and scalable MLOps pipelines.

05

Monitor drift and performance of models in production.

06

Apply the requirements of the AI Act and the GDPR to models.

07

Assess and mitigate model risk before the supervisor.

Profile

Who it's for

  • MLOps engineers.
  • Actuarial data stewards.
  • Data governance managers.
  • Model validators.
  • Insurance data architects.
  • Responsible AI leads.
  • Engineering or science graduates seeking to enter the insurance sector.
Standards & tools

What you'll work with

  • Directive 2009/138/EC (Solvency II) (EU prudential regime for insurers)
  • Regulation (EU) 2024/1689 (artificial intelligence)
  • Regulation (EU) 2022/2554 (DORA) (digital operational resilience)
  • Regulation (EU) 2016/679 (GDPR) (data protection)
  • ISO/IEC 42001:2023 (AI management systems)
  • ISO/IEC 23894:2023 (AI risk management)
  • ISO/IEC 27001:2022 (information security management)
  • ISO 8000 (data quality)
  • MLflow
  • Apache Airflow
  • Databricks
  • Kubernetes
  • Collibra
Syllabus

12 modules · 1900 h

  1. 1.1The DAMA-DMBOK framework
  2. 1.2Roles: data owner, data steward and custodian
  3. 1.3Data policies and standards
  4. 1.4Data quality under Solvency II
  5. 1.5Data dictionary and business glossary
  6. 1.6Technical and business metadata
  7. 1.7Data governance committees
  8. 1.8Measuring governance maturity
  9. 1.9Critical data for regulatory reporting
  10. 1.10Workshop: designing a data governance model
  1. 2.1Dimensions: accuracy, completeness and timeliness
  2. 2.2Automated validation rules
  3. 2.3Profiling policy and claims data
  4. 2.4Managing quality incidents
  5. 2.5Reconciliation with accounting
  6. 2.6External data and providers
  7. 2.7Quality of telematics data
  8. 2.8Quality indicators and thresholds
  9. 2.9Data quality tools
  10. 2.10Practical exercise: quality rules with Great Expectations
  1. 3.1End-to-end data lineage
  2. 3.2Enterprise data catalogues
  3. 3.3Lakehouse architecture
  4. 3.4Data mesh and data products
  5. 3.5Insurance industry data models
  6. 3.6Customer master data management
  7. 3.7Security and access control
  8. 3.8Classification of sensitive data
  9. 3.9Retention and archiving
  10. 3.10Laboratory: lineage of a reserving data item
  1. 4.1Phases of a model's life cycle
  2. 4.2Model inventory and criticality
  3. 4.3Reproducible development
  4. 4.4Version control with Git
  5. 4.5Environment and dependency management
  6. 4.6Model documentation
  7. 4.7Independent validation
  8. 4.8Approval and promotion to production
  9. 4.9Model retirement
  10. 4.10Case study: inventory record for a pricing model
  1. 5.1Principles of MLOps
  2. 5.2MLOps maturity levels
  3. 5.3Continuous integration and delivery for models
  4. 5.4Model and experiment registry
  5. 5.5Feature stores
  6. 5.6Pipeline orchestration
  7. 5.7Containers and Kubernetes
  8. 5.8Infrastructure as code
  9. 5.9Cost and scalability in the cloud
  10. 5.10Laboratory: training pipeline with MLflow
  1. 6.1Batch versus real-time inference
  2. 6.2REST APIs for models
  3. 6.3Canary and shadow deployment
  4. 6.4A/B testing of models
  5. 6.5Latency and performance
  6. 6.6Integration with core systems
  7. 6.7Endpoint security
  8. 6.8Rollback and failure management
  9. 6.9Logging and traceability of predictions
  10. 6.10Practical exercise: deploying a frequency model as an API
  1. 7.1Data drift and concept drift
  2. 7.2Performance metrics in production
  3. 7.3Population stability index
  4. 7.4Alerts and thresholds
  5. 7.5Fairness monitoring
  6. 7.6Feedback from actual claims
  7. 7.7Scheduled and event-driven retraining
  8. 7.8Model health dashboards
  9. 7.9Reporting to the risk committee
  10. 7.10Case study: drift detection in a pricing model
  1. 8.1EU principles for trustworthy AI
  2. 8.2AI governance according to EIOPA
  3. 8.3AI Act and high-risk systems in insurance
  4. 8.4AI risk management with ISO/IEC 23894
  5. 8.5ISO/IEC 42001 AI management system
  6. 8.6Model explainability
  7. 8.7Human oversight
  8. 8.8Algorithmic impact assessments
  9. 8.9Logging of automated decisions
  10. 8.10Workshop: impact assessment of an underwriting model
  1. 9.1GDPR principles
  2. 9.2Privacy by design
  3. 9.3Pseudonymisation and anonymisation
  4. 9.4Synthetic data and differential privacy
  5. 9.5Federated learning
  6. 9.6International transfers
  7. 9.7Security according to ISO/IEC 27001
  8. 9.8Digital operational resilience under DORA
  9. 9.9Managing cloud providers
  10. 9.10Practical exercise: generating synthetic policy data
  1. 10.1Model risk as operational risk
  2. 10.2Model risk management frameworks
  3. 10.3Expectations of European supervisors
  4. 10.4Internal models under Solvency II
  5. 10.5Validation of reserving and capital models
  6. 10.6Model risk under IFRS 17
  7. 10.7Internal audit of models
  8. 10.8Documentation for inspections
  9. 10.9Model risk culture and training
  10. 10.10Case study: validation plan for an internal model
  1. 11.1Data and analytics operating models
  2. 11.2Centres of excellence
  3. 11.3Multidisciplinary actuarial and data teams
  4. 11.4Prioritising use cases
  5. 11.5Measuring the value generated
  6. 11.6Change management
  7. 11.7Vendors and build versus buy
  8. 11.8Transformation roadmap
  9. 11.9Data training and culture
  10. 11.10Workshop: MLOps roadmap for an insurer
  1. 12.1Diagnosis of the starting situation
  2. 12.2Definition of objectives and scope
  3. 12.3Design of the governance framework
  4. 12.4Proposed technical architecture
  5. 12.5MLOps pipeline prototype
  6. 12.6Monitoring and validation plan
  7. 12.7Regulatory compliance analysis
  8. 12.8Implementation plan and costs
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training 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 the intake, so the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, Apache Airflow, Databricks, Kubernetes and Collibra, in free, academic or evaluation versions. Testing is studied through real procedures for deploying and monitoring actuarial models in production, auditing the quality and lineage of regulatory data and assessing the impact of AI systems in insurance. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2009/138/EC (Solvency II), Regulation (EU) 2024/1689, Regulation (EU) 2022/2554 (DORA), Regulation (EU) 2016/679 (GDPR), ISO/IEC 42001:2023, ISO/IEC 23894:2023, ISO/IEC 27001:2022, ISO 8000.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Solvency II, the AI Act and DORA applied to real cases, not as a theoretical annex.
Industry software:MLflow, Apache Airflow, Databricks, Kubernetes and Collibra, in free, academic or evaluation versions.
Master's Thesis with defence:a complete data governance and MLOps framework for an insurer, from diagnosis and architecture to an MLOps pipeline prototype, monitoring, compliance analysis and implementation plan, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

Governance and quality
Design a data governance model and implement quality rules with Great Expectations.
Lineage and inventory
Trace the lineage of a reserving data item and document a pricing model in the inventory.
MLOps pipeline
Build a training pipeline with MLflow and deploy a frequency model as an API.
Monitoring and responsible AI
Detect drift in a pricing model and assess the impact of an underwriting model.
Master's Thesis
Design and prototype a data governance and MLOps framework for an insurer, defended before a panel.
Career opportunities

Where this takes you

MLOps engineerActuarial data stewardData governance managerModel validatorInsurance data architectResponsible AI lead
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's 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.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MLflow, Apache Airflow, Databricks, Kubernetes and Collibra, 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), 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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