Data Ethics in Insurtech Course
Online 300-hour course in data ethics in insurtech. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR) and Python (fairlearn). 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 uses of data across the value chain of modern insurance.
Apply principles of fairness, transparency and explainability to insurance models.
Interpret the obligations of the AI Act and the GDPR for high-risk systems in insurance.
Design a model governance framework with roles, controls and records.
Assess bias in pricing and underwriting models using fairness metrics.
Integrate ethical criteria into the design of digital insurance products.
Develop a data ethics project applied to a real or simulated insurtech.
Who it's for
- Data ethics lead in insurtech who wants to specialise in data ethics in insurtech.
- AI model governance specialist who wants to specialise in data ethics in insurtech.
- Data protection officer who wants to specialise in data ethics in insurtech.
- Data scientist in insurance who wants to specialise in data ethics in insurtech.
- Responsible AI consultant who wants to specialise in data ethics in insurtech.
- Digital regulatory compliance analyst who wants to specialise in data ethics in insurtech.
- Engineering or science graduates looking to enter insurtech.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- Directive 2004/113/EC (equal treatment in goods and services)
- Directive (EU) 2016/97 (insurance distribution, IDD)
- Law 15/2022 (equal treatment and non-discrimination)
6 modules · 300 h
1.1Data sources in insurtech1.2Telematics and usage-based insurance1.3Connected vehicle data1.4Third-party data and data brokers1.5Social media data and digital footprint1.6Wearables and connected homes1.7Insurance data lifecycle1.8Data-driven business models1.9Ethical risks specific to the sector1.10Case study: data map of a digital insurer
2.1Risk pooling versus risk personalisation2.2Direct and indirect discrimination2.3Proxy variables and hidden bias2.4Test-Achats ruling and gender2.5Price optimisation and vulnerability2.6Exclusion of insurable groups2.7Algorithmic fairness metrics2.8Transparency towards the policyholder2.9EIOPA principles on AI2.10Workshop: detecting proxies in a pricing model
3.1GDPR and personal data in insurance3.2Automated decisions and Article 223.3Regulation (EU) 2024/1689 on AI: life and health insurance3.4Insurance Distribution Directive3.5Solvency II and model governance3.6Data Act and vehicle data3.7DORA and digital resilience3.8DGSFP supervision3.9EIOPA guidelines on governance3.10Practice: data protection impact assessment
4.1Inventory of models and algorithms4.2Validation and documentation4.3Explainability for customers and supervisors4.4Bias audits4.5Monitoring in production4.6Data ethics committees4.7ISO/IEC 42001 in insurers4.8Management of technology suppliers4.9Incident management4.10Case study: audit of a fraud model
5.1Customer consent and control5.2Data minimisation5.3Privacy by design5.4Clear premium communication5.5Claims management5.6Vulnerable customers5.7Secondary use of data5.8Sharing with reinsurers5.9Trust indicators5.10Case study: launch of a pay-as-you-drive policy
6.1Selection of the insurtech case6.2Data map6.3Ethical risk analysis6.4Regulatory analysis6.5Fairness assessment6.6Governance proposal6.7Transparency plan6.8Recommendations6.9Report and presentation6.10Final 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 timetable: 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 Python (fairlearn), IBM AI Fairness 360, SHAP, Jupyter Notebook, in free, academic or evaluation versions. Case studies are based on real procedures for bias audits of pricing models, algorithm impact assessments and model governance reviews. Assessment is continuous per 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 uses of data across the value chain of modern insurance.
Apply principles of fairness, transparency and explainability to insurance models.
Interpret the obligations of the AI Act and the GDPR for high-risk systems in insurance.
Design a model governance framework with roles, controls and records.
Assess bias in pricing and underwriting models using fairness metrics.
Where this takes you
Requirements
- No prior qualification 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 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
The course works with Python (fairlearn), IBM AI Fairness 360, SHAP and Jupyter Notebook. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
SEIUM's own diploma upon passing the continuous assessment and the final integrating project. It is an in-house training qualification, not an official degree from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher-level programme. Check the recognition policy when you enrol.
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