Generative AI Applied to Insurance Course
Online 300-hour course in generative AI applied to insurance. 6 modules covering Regulation (EU) 2024/1689 on artificial intelligence, Regulation (EU) 2016/679 (GDPR) and Azure OpenAI Service. 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
Explain the operation and limitations of generative language models.
Identify generative AI use cases across the insurance value chain.
Design a retrieval-augmented solution for policy documentation.
Classify the system's risks under the AI Regulation.
Assess data protection and information confidentiality.
Measure the quality, truthfulness and bias of generated responses.
Establish a framework for the responsible governance and deployment of the solution.
Who it's for
- Generative AI engineer in insurance who wants to specialise in generative AI applied to insurance.
- AI solutions product owner who wants to specialise in generative AI applied to insurance.
- AI governance manager who wants to specialise in generative AI applied to insurance.
- Insurance data scientist who wants to specialise in generative AI applied to insurance.
- Data protection officer who wants to specialise in generative AI applied to insurance.
- AI-driven transformation consultant who wants to specialise in generative AI applied to insurance.
- Graduates in engineering or science seeking to join insurance undertakings.
What you'll work with
- Regulation (EU) 2024/1689 on artificial intelligence
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 42001 (AI management systems)
- Regulation (EU) 2022/2554 (DORA)
- ISO/IEC 23894 (AI risk management)
- NIST AI RMF 1.0
- Directive (EU) 2016/97 on insurance distribution (IDD)
6 modules · 300 h
1.1Language models and how they basically work1.2Real capabilities and known limits1.3Hallucinations and why they occur1.4Context window and memory1.5Retrieval-augmented generation1.6Fine-tuning versus instruction-tuning1.7Proprietary and open models1.8Vocabulary and acronyms in the field1.9Unfounded expectations in the sector1.10Exercise: assessing whether a case fits the technology
2.1Customer service and query resolution2.2Drafting and reviewing documentation2.3Extracting data from documents and claim reports2.4Underwriting support and file summarising2.5Claims management and initial analysis2.6Detecting fraud patterns as a support tool2.7Internal training and knowledge management2.8Product development and marketing2.9Prioritising cases by value and risk2.10Practice: prioritising a portfolio of use cases
3.1Model selection and criteria3.2Prompt design and templates3.3Connection to in-house document sources3.4Control of response quality3.5Managing uncertainty and refusals3.6Human in the loop within the decision process3.7Interaction logging and audit3.8Cost per query and spend control3.9Integration with existing systems3.10Practice: specifying an internal assistant
4.1Applicable AI regulation4.2Risk classification of the use case4.3Transparency obligations towards the customer4.4Data protection and minimisation4.5Confidentiality of information sent to the model4.6Bias and discrimination in insurance decisions4.7Required explainability and how to provide it4.8Liability for a system error4.9Requirements of the sector supervisor4.10Practice: risk assessment of a use case
5.1Defining quality metrics5.2Evaluation sets and systematic testing5.3User testing and feedback5.4Phased rollout and impact control5.5Production monitoring and drift5.6Incident and complaints management5.7Training of user staff5.8Internal governance and decision committee5.9Value and return indicators5.10Practice: evaluation plan for an assistant
6.1Choosing the use case and justifying it6.2Functional design of the solution6.3Architecture and knowledge sources6.4Risk assessment and compliance6.5Evaluation plan and metrics6.6Design of human oversight6.7Deployment and training plan6.8Cost and return model6.9Project report6.10Project defence
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 Azure OpenAI Service, LangChain, Hugging Face Transformers, Python, Amazon Bedrock, in free, academic or evaluation versions. Trials are studied using real procedures for prototype assistants with retrieval-augmented generation (RAG), response and hallucination evaluation, and data protection impact analysis. Assessment is continuous, module by module, plus the final capstone project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain the operation and limitations of generative language models.
Identify generative AI use cases across the insurance value chain.
Design a retrieval-augmented solution for policy documentation.
Classify the system's risks under the AI Regulation.
Assess data protection and information confidentiality.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level 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 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Azure OpenAI Service, LangChain, Hugging Face Transformers, Python, Amazon Bedrock. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An in-house SEIUM Institute diploma upon passing continuous assessment and the capstone project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.
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