Postgraduate Diploma in Data Quality and Traceability of Actuarial Models
Online 800-hour Postgraduate Diploma in Data Quality and Traceability of Actuarial Models. 8 modules covering Directive 2009/138/EC (Solvency II, Article 82 data quality), Delegated Regulation (EU) 2015/35 (data quality requirements) and Great Expectations. Assigned tutor and open enrolment.
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
Relate data quality to the reliability of actuarial models.
Design a governance framework for actuarial data.
Measure data quality using criteria of accuracy, completeness and appropriateness.
Document data lineage and traceability.
Develop reproducible models with version control.
Validate actuarial models with traceable evidence.
Automate controls and their periodic supervision.
Who it's for
- Data actuary seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Data governance manager seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Actuarial data engineer seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Actuarial model validator seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Data quality analyst seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Actuary in the actuarial function seeking to specialise in Data Quality and Traceability of Actuarial Models.
- Graduates in engineering or science looking to enter insurance companies.
What you'll work with
- Directive 2009/138/EC (Solvency II, Article 82 data quality)
- Delegated Regulation (EU) 2015/35 (data quality requirements)
- ASOP No. 23 (data quality)
- ISO 8000 (data quality)
- ISO/IEC 25012 (data quality model)
- BCBS 239 (risk data aggregation)
- TAS 100 (actuarial technical principles)
8 modules · 800 h
1.1Pricing and reserving models1.2Internal models under Solvency II1.3Data dependency in calculations1.4Article 82 of the Solvency II Directive1.5Delegated Regulation 2015/35 on data quality1.6Accuracy, completeness and appropriateness1.7The actuarial function and its responsibilities1.8IFRS 17 and data requirements1.9Data errors with material impact1.10Case study: data map of technical provisions
2.1Data quality policy2.2Actuarial data dictionary2.3Data owners and custodians2.4DAMA-DMBOK reference framework2.5BCBS 239 principles as a reference2.6Solvency II data directory2.7Master data management2.8External and reinsurer data2.9Data governance committee2.10Workshop: data dictionary for a life portfolio
3.1Data quality dimensions3.2Business validation rules3.3Accounting reconciliation controls3.4Actuarial reasonableness controls3.5Outlier detection in portfolios3.6Data profiling with Great Expectations3.7Data quality indicators and thresholds3.8Incident logging3.9Remediation and correction at source3.10Laboratory: validation suite with Great Expectations
4.1Data lineage from the source4.2Automated lineage tools4.3Traceability across systems and models4.4Version control of technical bases4.5Versioning of actuarial assumptions4.6Reproducibility of calculations4.7Audit trails4.8Management of manual adjustments4.9Documentation of transformations4.10Practice: lineage of a technical provision
5.1Actuarial programming in R and Python5.2Version control with Git5.3Unit testing of calculations5.4Reproducible environments with containers5.5Actuarial packages and libraries5.6Peer code review5.7Continuous integration and deployment5.8Replacing critical spreadsheets5.9EUC risk management5.10Practice: refactoring a calculation in R with tests
6.1Independent model validation6.2Analysis of movements between periods6.3Sensitivity testing of assumptions6.4Backtesting of provisions6.5Validation of pricing models6.6Proxy models and their validation6.7Actuarial function report6.8Findings and action plans6.9Internal audit review6.10Case study: validation of non-life provisions
7.1Automation of closing processes7.2Data flow orchestration7.3Automatic controls and alerts7.4QRT reports and data quality7.5EIOPA XBRL templates7.6Supervisory requirements7.7Data quality dashboards7.8Continuous improvement of controls7.9Management of regulatory change7.10Workshop: data quality dashboard
8.1Selection of the actuarial process8.2Inventory of data used8.3Design of quality rules8.4Implementation of automatic controls8.5Complete process lineage8.6Reproducibility testing8.7Data governance plan8.8Report for the actuarial function8.9Dissertation and presentation8.10Final review with the tutor
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 with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Great Expectations, dbt, Git, R (targets), Apache Atlas, in free, academic or evaluation versions. Case studies are based on real data quality diagnostic procedures for a reserving model, end-to-end data lineage mapping, and reproduction of actuarial results with version control. Assessment is continuous by module 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.
Relate data quality to the reliability of actuarial models.
Design a governance framework for actuarial data.
Measure data quality using criteria of accuracy, completeness and appropriateness.
Document data lineage and traceability.
Develop reproducible models with version control.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is 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.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The programme uses Great Expectations, dbt, Git, R (targets) and Apache Atlas. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is own training, not an official title from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please check the credit recognition policy when you enrol.
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