Claims Automation with AI Course
Online 300-hour course in AI-driven claims automation. 6 modules covering Regulation (EU) 2024/1689 (AI Act), Regulation (EU) 2016/679 (GDPR, Art. 22) and Python (scikit-learn). Dedicated tutor and enrolment open.
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 claims lifecycle phases with the greatest automation potential.
Prepare structured and document-based data to train classification and extraction models.
Design an automated process that combines business rules, RPA and AI models.
Define human oversight points in line with the GDPR and the AI Act.
Evaluate the performance, fairness and explainability of deployed models.
Measure the impact of automation on timescales, costs and policyholder satisfaction.
Develop a claims automation case from start to finish.
Who it's for
- Process automation engineer looking to specialise in AI-driven claims automation.
- Data scientist in insurance looking to specialise in AI-driven claims automation.
- Claims transformation manager looking to specialise in AI-driven claims automation.
- Fraud analyst looking to specialise in AI-driven claims automation.
- AI governance specialist looking to specialise in AI-driven claims automation.
- Intelligent automation project manager looking to specialise in AI-driven claims automation.
- Engineering or science graduates seeking to enter general insurance.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- Regulation (EU) 2016/679 (GDPR, Art. 22)
- Law 50/1980 on Insurance Contracts
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- Regulation (EU) 2022/2554 (DORA)
- Directive 2009/138/EC (Solvency II)
6 modules · 300 h
1.1What can and cannot be automated1.2Rules-based automation versus models1.3High-volume, low-complexity cases1.4Benefits in time, cost and consistency1.5Risk of systematic errors at scale1.6The role of the claims handler after automation1.7Sector vocabulary and acronyms1.8Required data and process maturity1.9Common unfounded expectations1.10Exercise: prioritising automatable tasks
2.1Claim file data and its quality2.2Extracting information from documents and images2.3Triage and assignment models2.4Automatic cost estimation2.5Detecting fraud indicators2.6Model validation and test sets2.7Model drift and its monitoring2.8Bias and its periodic review2.9Explainability of the decision2.10Practice: designing a triage model
3.1End-to-end flow and decision points3.2Automation thresholds and exceptions3.3Human in the loop and its design3.4Automatic settlement and its controls3.5Automatic communication to the policyholder3.6Handling cases returned for review3.7Traceability of each decision3.8Integration with the claims platform3.9Continuity if the system fails3.10Practice: designing an automated workflow
4.1Applicable AI regulation4.2Risk classification of the use case4.3Transparency towards the policyholder4.4Right to human review of the decision4.5Data protection and minimisation4.6Required technical documentation4.7Internal model governance4.8Internal and external audit4.9Reporting to the supervisor4.10Practice: compliance file for a model
5.1Pilot test and success criteria5.2Progressive rollout and control5.3Quality and service indicators5.4Detecting systematic errors5.5Team training and new role5.6Managing the impact on staff5.7Internal and external communication5.8Evolution and retraining5.9Return on investment5.10Practice: deployment and measurement plan
6.1Choosing the process and scope6.2Data and maturity diagnosis6.3Design of the automated workflow6.4Model specification6.5Compliance file6.6Test and deployment plan6.7Indicator and control system6.8Cost and return model6.9Project report6.10Project defence
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. There are no fixed timetables: 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 (scikit-learn), UiPath, Azure AI Document Intelligence, Camunda, Power BI, in free, academic or evaluation versions. Trials are studied on simulated real claims-file automation procedures, training of triage and fraud detection models, and bias auditing of automated decisions. Assessment is continuous per 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.
Identify the claims lifecycle phases with the greatest automation potential.
Prepare structured and document-based data to train classification and extraction models.
Design an automated process that combines business rules, RPA and AI models.
Define human oversight points in line with the GDPR and the AI Act.
Evaluate the performance, fairness and explainability of deployed models.
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 reading.
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 an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with Python (scikit-learn), UiPath, Azure AI Document Intelligence, Camunda and Power BI. Free, academic or evaluation versions are used, and the tutor will indicate the open-source alternative where one exists.
A SEIUM in-house diploma upon passing the continuous assessment and the integrative 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. Please check the credit transfer policy when you enrol.
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