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Postgraduate DiplomaInsurance, Risk & Actuarial Data Applications open

Postgraduate Diploma in Advanced Analytics for Claims Optimisation

Online 800-hour Postgraduate Diploma in Advanced Analytics for Claims Optimisation. 8 modules covering Law 50/1980 on Insurance Contracts, Law 35/2015 (damages assessment table) and Python (pandas, scikit-learn). Assigned tutor and open enrolment.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Describe the claims cycle and its decision points.

02

Prepare and clean claims data for analysis.

03

Build triage and claims segmentation models.

04

Estimate severity and support reserve calculation.

05

Apply image and text analytics to claims management.

06

Identify levers for optimising claims cost.

07

Govern and implement analytics in day-to-day operations.

Profile

Who it's for

  • Claims data analyst wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Data scientist in insurance wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Claims operations manager wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Reserving actuary wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Claims cost analyst wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Claims transformation consultant wishing to specialise in Advanced Analytics for Claims Optimisation.
  • Engineering or science graduates seeking to enter insurance companies.
Standards & tools

What you'll work with

  • Law 50/1980 on Insurance Contracts
  • Law 35/2015 (damages assessment table)
  • IFRS 17 (insurance contracts)
  • Regulation (EU) 2016/679 (GDPR)
  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • Directive 2009/138/EC (Solvency II)
  • ISO/IEC 42001 (AI management systems)
Syllabus

8 modules · 800 h

  1. 1.1Stages of the claims process
  2. 1.2Opening and first notification
  3. 1.3On-site and remote loss adjustment
  4. 1.4Setting up and reviewing reserves
  5. 1.5Settlement and compensation
  6. 1.6Recoveries between insurers
  7. 1.7Claims handling expenses
  8. 1.8Traditional loss ratio indicators
  9. 1.9Customer experience and NPS
  10. 1.10Case study: process map for motor insurance
  1. 2.1Internal sources of policies and claims
  2. 2.2External and weather data
  3. 2.3Claim forms, reports and documents
  4. 2.4Photographs and videos of damage
  5. 2.5Data quality and completeness
  6. 2.6Data model designed for analytics
  7. 2.7Data lakehouse architecture
  8. 2.8Personal data protection
  9. 2.9Supplier and repairer data
  10. 2.10Practical: analytical claims data model
  1. 3.1Claim complexity models
  2. 3.2Automatic allocation to claims handlers
  3. 3.3Fast-track handling routes
  4. 3.4Initial severity prediction
  5. 3.5Litigation prediction
  6. 3.6Optimal allocation of loss adjusters
  7. 3.7Combining rules and models
  8. 3.8Validating triage in production
  9. 3.9Impact on cycle times
  10. 3.10Lab: triage model with gradient boosting
  1. 4.1Case reserves
  2. 4.2Prediction of ultimate cost
  3. 4.3Severity distributions
  4. 4.4Development triangles
  5. 4.5Chain ladder and Bornhuetter-Ferguson methods
  6. 4.6Machine learning for individual reserves
  7. 4.7Estimating IBNR
  8. 4.8Uncertainty via bootstrap
  9. 4.9Solvency II requirements
  10. 4.10Practical: comparing chain ladder and individual model
  1. 5.1Computer vision for damage assessment
  2. 5.2Automatic estimation of repair cost
  3. 5.3Natural language processing of claim reports
  4. 5.4Automatic document extraction
  5. 5.5Classification of incoming communications
  6. 5.6Language models in claims handling
  7. 5.7Risks of full automation
  8. 5.8Human oversight of decisions
  9. 5.9Selecting technology vendors
  10. 5.10Practical: claims text classifier
  1. 6.1Approved repairer networks
  2. 6.2Supplier performance management
  3. 6.3Loss adjuster route optimisation
  4. 6.4Subrogation and recovery
  5. 6.5Direct settlement versus repair
  6. 6.6Detecting leakage in settlement
  7. 6.7Root-cause analysis of deviations
  8. 6.8Simulation of claims handling processes
  9. 6.9Measuring savings achieved
  10. 6.10Case study: optimising a repairer network
  1. 7.1Key claims indicators
  2. 7.2Operational dashboards
  3. 7.3Controlled experiments in claims handling
  4. 7.4Monitoring models in production
  5. 7.5AI regulation in insurance
  6. 7.6Digital operational resilience with DORA
  7. 7.7Explainability for customers
  8. 7.8Ethics in automated decisions
  9. 7.9Change management for claims handlers
  10. 7.10Workshop: claims dashboard
  1. 8.1Defining the business case
  2. 8.2Data selection and preparation
  3. 8.3Exploratory loss analysis
  4. 8.4Developing the analytical model
  5. 8.5Validation and testing
  6. 8.6Integration into the process
  7. 8.7Measuring economic impact
  8. 8.8Model governance plan
  9. 8.9Written report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned 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 (pandas, scikit-learn), R, Guidewire ClaimCenter, Databricks, Power BI, in free, academic or evaluation versions. Tests are studied using real procedures for claims triage and segmentation models, severity and individual reserve prediction, and text and image analysis of claim files. Assessment is continuous across each module plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test bench, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-focused Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Defining the business case

Describe the claims cycle and its decision points.

Data selection and preparation

Prepare and clean claims data for analysis.

Exploratory loss analysis

Build triage and claims segmentation models.

Developing the analytical model

Estimate severity and support reserve calculation.

Validation and testing

Apply image and text analytics to claims management.

Career opportunities

Where this takes you

Claims data analystData scientist in insuranceClaims operations managerReserving actuaryClaims cost analystClaims transformation consultant
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university 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.

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FAQ

Frequently asked questions

The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

You will work with Python (pandas, scikit-learn), R, Guidewire ClaimCenter, Databricks and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

An own diploma from SEIUM upon passing continuous assessment and the final integrative 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 are credited towards the advanced programme. Check the credit transfer policy when you enrol.

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