Course in Actuarial Data Visualisation with BI
Online 300-hour course in actuarial data visualisation with BI. 6 modules covering ASOP No. 41 (actuarial communications), TAS 100 (technical actuarial principles, FRC) and Power BI. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the information needs of each user of actuarial data.
Build a data model suited to analysis in BI tools.
Design clear and effective dashboards for claims, premiums and reserves.
Represent uncertainty and model results without misleading the audience.
Apply access controls, security and data quality checks.
Automate the updating and distribution of dashboards.
Develop a complete actuarial visualisation use case.
Who it's for
- Actuarial data analyst who wants to specialise in Actuarial Data Visualisation with BI.
- BI developer in insurance who wants to specialise in Actuarial Data Visualisation with BI.
- Actuarial reporting analyst who wants to specialise in Actuarial Data Visualisation with BI.
- Actuarial data engineer who wants to specialise in Actuarial Data Visualisation with BI.
- Management information officer who wants to specialise in Actuarial Data Visualisation with BI.
- Insurance analytics consultant who wants to specialise in Actuarial Data Visualisation with BI.
- Graduates in engineering or science seeking to enter insurance companies.
What you'll work with
- ASOP No. 41 (actuarial communications)
- TAS 100 (technical actuarial principles, FRC)
- BCBS 239 (risk data aggregation and reporting)
- Directive 2009/138/EC (Solvency II, Art. 82 data quality)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 27001 (information security)
- WCAG 2.2 (accessibility)
6 modules · 300 h
1.1Audiences: actuarial, underwriting, management and supervisor1.2Questions each dashboard must answer1.3Essential actuarial indicators and their definitions1.4Difference between a regulatory report and a management dashboard1.5Level of aggregation and drill-down capability1.6Frequency and timing of updates1.7The risk of misinterpreting an aggregated figure1.8Metrics catalogue and data dictionary1.9Traceability requirements back to the source data1.10Workshop: defining the indicators of a dashboard
2.1Star schema applied to insurance data2.2Fact tables: policies, premiums and claims2.3Dimensions: time, product, channel, zone and customer2.4Granularity and its effect on what can be analysed2.5Dates: occurrence, reporting, payment and effective date2.6Development triangles in a dimensional model2.7Handling reopenings and partial payments2.8Data quality: controls and reconciliation with accounting2.9Incremental loading and update processes2.10Practice: designing the claims data model
3.1Choosing the chart according to the question3.2Time series of frequency, average cost and ratio3.3Development triangles and their representation3.4Segment comparisons without overloading the view3.5Maps and honest geographic representation3.6Scales, axes and responsible use of colour3.7Filters, hierarchies and level-based navigation3.8Calculated measures and calculation at query time3.9Dashboard performance and pre-aggregations3.10Practice: claims dashboard for a portfolio
4.1Representing intervals and scenario ranges4.2Simulated distributions: histograms and excess curves4.3Predicted versus observed and calibration curves4.4Decile analysis and lift curves4.5GLM results: factors and their interval4.6Comparing model versions4.7Alerts and thresholds on the dashboard4.8How not to exaggerate a weak signal4.9Data storytelling: from chart to conclusion4.10Practice: model validation dashboard for a pricing model
5.1Role-based permissions and sensitive data5.2Pseudonymisation in dashboards accessible to many users5.3Dashboard versioning and change control5.4Documentation for the end user5.5Certification of official indicators5.6Update automation and failure alerts5.7Periodic reconciliation with closing figures5.8User training and real adoption5.9Retiring obsolete dashboards5.10Practice: certified indicator catalogue
6.1Underwriting and portfolio mix dashboard6.2Claims and file ageing dashboard6.3Reserves and development dashboard6.4Solvency and risk dashboard6.5Commercial dashboard: retention and new business6.6Dashboards for the regulator and for audit6.7Measuring usage and continuous improvement6.8Common errors in actuarial dashboards6.9Presenting the dashboard to management6.10Final project: complete actuarial scorecard
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 the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Power BI, Tableau, Qlik Sense, R (ggplot2, Shiny), SQL, in free, academic or evaluation versions. Case studies cover real procedures for building a claims and reserves dashboard, visualising confidence intervals of actuarial models, and reviewing dashboards with management users. Assessment is continuous by 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.
Identify the information needs of each user of actuarial data.
Build a data model suited to analysis in BI tools.
Design clear and effective dashboards for claims, premiums and reserves.
Represent uncertainty and model results without misleading the audience.
Apply access controls, security and data quality checks.
Where this takes you
Requirements
- No prior 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 consists of 300 total hours spread over 3 to 5 months. This is an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with Power BI, Tableau, Qlik Sense, R (ggplot2, Shiny), SQL. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Check the recognition policy when you enrol.
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