Postgraduate Diploma in Early Fraud Detection in Insurance Claims
800-hour online Postgraduate Diploma in Early Fraud Detection in Insurance Claims. 8 modules covering Law 20/2015 (LOSSEAR, common anti-fraud files), Regulation (EU) 2016/679 (GDPR) and Python (scikit-learn, XGBoost). Assigned tutor and enrolment open.
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What you will be able to do
Characterise the typologies of claims fraud.
Design detection systems based on expert rules.
Train supervised fraud detection models.
Apply unsupervised techniques to uncover anomalous patterns.
Analyse relationship networks to detect organised fraud.
Implement early detection at the point of claim opening.
Govern anti-fraud models in line with data and AI regulations.
Who it's for
- Insurance fraud analyst who wants to specialise in Early Fraud Detection in Insurance Claims.
- Anti-fraud data scientist who wants to specialise in Early Fraud Detection in Insurance Claims.
- Claims investigator who wants to specialise in Early Fraud Detection in Insurance Claims.
- Head of the anti-fraud unit who wants to specialise in Early Fraud Detection in Insurance Claims.
- Specialist claims handler who wants to specialise in Early Fraud Detection in Insurance Claims.
- Compliance consultant who wants to specialise in Early Fraud Detection in Insurance Claims.
- Graduates in engineering or science who wish to enter insurance companies.
What you'll work with
- Law 20/2015 (LOSSEAR, common anti-fraud files)
- Regulation (EU) 2016/679 (GDPR)
- Organic Law 3/2018 (personal data protection and guarantee of digital rights)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Law 10/2010 on the prevention of money laundering
- ISO 37001 (anti-bribery management systems)
- ISO/IEC 42001 (AI management systems)
8 modules · 800 h
1.1Typologies of claims fraud1.2Opportunistic fraud versus organised fraud1.3Estimates of the scale of fraud1.4Warning indicators by line of business1.5Article 16 of the Ley de Contrato de Seguro1.6Fraud investigation units1.7Fraud in motor, home and health insurance1.8Cost of fraud in the premium1.9Ethics and presumption of innocence1.10Case study: fraud patterns in motor claims
2.1Catalogue of alert indicators2.2Business rules engines2.3Additive scoring by indicator2.4Internal lists of suspicious entities2.5TIREA sector files2.6Periodic review of effectiveness2.7Managing false positives2.8Integration at claim opening2.9Auditing and traceability of rules2.10Practical exercise: rules engine on historical claims
3.1Building labels from investigations3.2Extreme class imbalance3.3Logistic regression as a baseline model3.4Gradient boosting with claim variables3.5Precision, recall and the PR curve3.6Expected cost of investigation3.7Threshold selection by capacity3.8Interpretability with SHAP values3.9Recalibration against new schemes3.10Laboratory: fraud model with imbalanced data
4.1Anomaly detection in claims4.2Isolation Forest and LOF4.3Autoencoders for atypical patterns4.4Clustering of similar claims4.5Benford’s Law applied to amounts4.6Anomalies in time series of declarations4.7Combination with supervised models4.8Validation with investigation teams4.9Managing unlabelled alerts4.10Practical exercise: anomalies in garage invoices
5.1Graphs of policyholders, vehicles and suppliers5.2Entity resolution and duplicates5.3Community detection with Louvain5.4Centrality and betweenness measures5.5Neo4j graph databases5.6Identification of organised rings5.7Visualisation for investigators5.8Graph neural networks5.9Use of analysis in legal proceedings5.10Laboratory: ring detection in Neo4j
6.1Scoring at first notification6.2Telematic collision signals6.3Automated analysis of documentation6.4Detection of manipulated images6.5Analysis of claim statement texts6.6Real-time scoring6.7Prioritisation of cases for investigation6.8Investigation and resolution workflow6.9Feedback of results to the model6.10Case study: early alerts in home claims
7.1Data processing under legitimate interest7.2Common files and data protection7.3AI regulation in the insurance sector7.4Risk of indirect discrimination7.5Auditing anti-fraud models7.6Chain of custody of evidence7.7Sector collaboration and ICEA7.8Anti-fraud effectiveness indicators7.9Training of claims handlers7.10Workshop: corporate fraud detection policy
8.1Definition of the use case8.2Data preparation and labelling8.3Design of the rules system8.4Training of the supervised model8.5Entity network analysis8.6Integration into the claims workflow8.7Assessment of expected savings8.8Regulatory compliance plan8.9Report and presentation8.10Final 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 timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn, XGBoost), Neo4j, SAS Fraud Management, Shift Technology, R, in free, academic or evaluation versions. Practical work is based on real procedures for fraud scoring models at claim opening, analysis of networks of those involved, and review of alerts with investigators. Assessment is continuous by 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.
Characterise the typologies of claims fraud.
Design detection systems based on expert rules.
Train supervised fraud detection models.
Apply unsupervised techniques to uncover anomalous patterns.
Analyse relationship networks to detect organised fraud.
Where this takes you
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 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 an average of 10 to 15 hours per week, compatible with a full-time job.
The programme uses Python (scikit-learn, XGBoost), Neo4j, SAS Fraud Management, Shift Technology and R. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An SEIUM own diploma upon passing the continuous assessment and the integrative project. This is private training, not an official qualification from the Ministry.
Yes. Modules passed in a course or diploma in the same area can be credited towards the advanced programme. Please consult the credit recognition policy when you enrol.
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