Postgraduate Diploma in Digital Underwriting Models with AI
Online Postgraduate Diploma of 800 h in Digital Underwriting Models with AI. 8 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR), Art. 22 on automated decisions and Python (scikit-learn, XGBoost). Dedicated tutor assigned and enrolment open.
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
Understand the underwriting process and the levers of its digital transformation.
Identify and prepare internal and external data sources useful for risk selection.
Develop risk classification models using artificial intelligence techniques.
Apply intelligent document processing to automate information capture.
Design decision engines that combine business rules and predictive models.
Detect fraud and adverse selection patterns at the contracting stage.
Assess compliance with the AI Act and the GDPR in automated underwriting systems.
Who it's for
- Digital underwriter who wants to specialise in Digital Underwriting Models with AI.
- Underwriting data scientist who wants to specialise in Digital Underwriting Models with AI.
- Rules engine manager who wants to specialise in Digital Underwriting Models with AI.
- AI risk analyst who wants to specialise in Digital Underwriting Models with AI.
- Model governance manager who wants to specialise in Digital Underwriting Models with AI.
- Underwriting transformation consultant who wants to specialise in Digital Underwriting Models with AI.
- Graduates in engineering or science seeking to enter life and health insurers.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR), Art. 22 on automated decisions
- Directive (EU) 2016/97 (IDD)
- Law 10/2010 on the prevention of money laundering
- Directive 2004/113/EC (equal treatment between men and women in access to goods and services)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
8 modules · 800 h
1.1Traditional underwriting process1.2Risk appetite and underwriting guidelines1.3Information asymmetry and adverse selection1.4Underwriting in life, health, non-life and commercial insurance1.5Digitalisation of the application process1.6Straight-through processing and automatic underwriting1.7The role of the augmented underwriter1.8Indicators: acceptance rate and loss ratio1.9Success stories in digital underwriting1.10Case study: process map of a life insurance underwriting process
2.1Digital questionnaires and dynamic responses2.2Third-party sources and pre-filling2.3Medical data and electronic health records2.4Business data and commercial risks2.5Satellite and street-level imagery2.6Data quality and verification2.7Biases in historical data2.8Consent and legal bases2.9Legal limitations on the use of genetic data2.10Laboratory: designing an adaptive questionnaire
3.1Risk classification with machine learning3.2Mortality and morbidity models3.3Gradient boosting for acceptance decisions3.4Calibration and decision thresholds3.5Rule-based models versus learning models3.6Combination with actuarial criteria3.7Validation against subsequent claims experience3.8Overfitting risk in small portfolios3.9Interpretation with SHAP3.10Practical exercise: acceptance model for life insurance risk
4.1OCR and information extraction4.2Document classification4.3NLP in medical reports4.4Language models for summarising case files4.5Data extraction from annual accounts4.6Analysis of industrial risks in reports4.7Detection of inconsistencies4.8Human validation of results4.9Hallucination risks in generative AI4.10Laboratory: extracting data from medical reports
5.1Rules and decision engines5.2Designing automatic underwriting workflows5.3Referral to a human underwriter5.4Risk-adjusted pricing in the decision5.5Integration with policy administration systems5.6APIs for digital channels and intermediaries5.7A/B testing of underwriting workflows5.8Exception management5.9Decision auditing5.10Workshop: decision engine for a home insurance product
6.1Omission and misrepresentation in the declaration6.2Detection of adverse selection patterns6.3Identity verification and KYC6.4Detection of manipulated documents6.5Behavioural analysis during application6.6Coordination with claims6.7Risk signals in digital channels6.8Balancing friction and control6.9Measuring the effectiveness of anti-fraud measures6.10Case study: detecting adverse selection in an online product
7.1Regulation (EU) 2024/1689 and life and health insurance7.2High-level risk assessment7.3Meaningful human oversight7.4GDPR and automated decisions7.5Non-discrimination and Law 15/2022 on equality7.6Explainability for the rejected customer7.7EIOPA guidelines on AI7.8Model validation and auditing7.9ISO/IEC 42001 standard7.10Practical exercise: impact assessment of an underwriting system
8.1Product and risk selection8.2Design of the digital process8.3Data preparation8.4AI risk model8.5Rules and decision engine8.6Fairness and compliance assessment8.7Business impact analysis8.8Implementation plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
A 100 % online programme on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it easy to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn, XGBoost), FICO Blaze Advisor, Azure AI Document Intelligence, SHAP (explainability), Databricks, in free, academic or evaluation versions. Tests are studied using real procedures for a prototype underwriting decision engine, automatic data extraction from questionnaires and medical documents, and bias auditing of acceptance models. 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.
Understand the underwriting process and the levers of its digital transformation.
Identify and prepare internal and external data sources useful for risk selection.
Develop risk classification models using artificial intelligence techniques.
Apply intelligent document processing to automate information capture.
Design decision engines that combine business rules and predictive models.
Where this takes you
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.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
The programme uses Python (scikit-learn, XGBoost), FICO Blaze Advisor, Azure AI Document Intelligence, SHAP (explainability) and Databricks. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM Institute diploma upon passing the continuous assessment and the integrating 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 can be credited towards the advanced programme. Please check the credit recognition policy when you enrol.
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