Postgraduate Diploma in Fraud Detection Techniques in Claims
Online 800-hour Postgraduate Diploma in Fraud Detection Techniques in Claims. 8 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2016/679 (GDPR) and Python (scikit-learn, PyOD). Assigned tutor and open enrolment.
Postgraduate Diploma in Fraud Detection Techniques in Claims — studying from Australia
Australia has a mature insurance and superannuation sector, with major insurers, reinsurers and financial institutions relying heavily on actuarial analysis and risk modelling. Engineers and data specialists contribute to catastrophe modelling, telematics-based pricing and claims analytics, particularly given the country's exposure to bushfire, flood and cyclone risk. The regulatory environment is shaped by prudential oversight and consumer protection frameworks. Employers include general and life insurers, consultancies, reinsurance firms and government risk agencies.
Fees for Australia: A$1,420 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Does this programme lead to recognition by the Actuaries Institute or another Australian professional body?
SEIUM qualifications are private and are not accredited by the Actuaries Institute, Engineers Australia or any national body. The Actuaries Institute sets its own examination and membership requirements, which a private qualification does not replace. Recognition for any role is decided by the employer or the relevant body, so confirm their requirements before enrolling.
Which Australian insurers and risk employers might consider this training?
Insurers, reinsurers, banks, consultancies and risk management teams in Australia employ people with data and risk skills. Whether they consider a private engineering qualification relevant depends on the role and their own hiring policy. Your demonstrated skills, portfolio and final project are often what matter most to them.
Is the course manageable while working, and can my employer fund it?
Yes, the programmes are delivered entirely online in English, with an assigned technical tutor, so you can fit study around work from Australia. Fees can be paid in instalments, and employer sponsorship is possible. Check the total hours for your chosen programme and agree a realistic weekly schedule before you start.
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
Identify fraud risk points in the claims handling cycle.
Prepare claims data for anti-fraud models, including handling class imbalance.
Train supervised fraud detection models.
Apply unsupervised techniques to uncover anomalous patterns.
Analyse relationship networks to detect organised fraud.
Extract fraud signals from text, images and documents.
Deploy and govern anti-fraud models with criteria for explainability and data protection.
Who it's for
- Anti-fraud data scientist looking to specialise in Fraud Detection Techniques in Claims.
- Claims fraud analyst looking to specialise in Fraud Detection Techniques in Claims.
- Anti-fraud model manager looking to specialise in Fraud Detection Techniques in Claims.
- Insurance data engineer looking to specialise in Fraud Detection Techniques in Claims.
- Fraud network investigator looking to specialise in Fraud Detection Techniques in Claims.
- AI governance manager looking to specialise in Fraud Detection Techniques in Claims.
- Engineering or science graduates seeking to enter insurance companies.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- Law 20/2015 (LOSSEAR), shared files for fraud prevention
- Organic Law 3/2018 (LOPDGDD)
- ISO/IEC 42001 (artificial intelligence management systems)
- ISO/IEC 27001 (information security)
- ISO 31000 (risk management)
8 modules · 800 h
1.1Claims lifecycle and risk points1.2Fraud patterns in motor, home and health1.3Fraud metrics and their quantification1.4Labels: confirmed and suspected fraud1.5Data available at opening and processing1.6Cost of investigating versus paying1.7Legal framework for processing anti-fraud data1.8Organisation of detection within the insurer1.9State of the art in anti-fraud analytics1.10Case study: analysis of a claims portfolio
2.1Integration of policies, claims and payments2.2Data quality and missing values2.3Feature engineering for behavioural variables2.4Temporal and sequence variables2.5Extreme class imbalance2.6Sampling, SMOTE and weightings2.7Information leakage during training2.8Time-based validation partitions2.9Anonymisation and pseudonymisation2.10Laboratory: motor fraud training dataset
3.1Logistic regression and scorecards3.2Trees and Random Forest3.3Gradient boosting with XGBoost and LightGBM3.4Probability calibration3.5Metrics: AUC, precision at k and PR curve3.6Thresholds according to investigative capacity3.7Explainability with SHAP3.8Model stability over time3.9Comparison with expert rules3.10Practice: fraud scoring model with XGBoost
4.1Anomaly and outlier detection4.2Isolation Forest and LOF4.3Autoencoders for unusual patterns4.4Clustering of suppliers and garages4.5Benford's Law applied to amounts4.6Duplicate and similarity analysis4.7Semi-supervised learning4.8Validation without labels4.9Combination with supervised models4.10Laboratory: anomalies in repair invoices
5.1Graphs of people, vehicles and addresses5.2Entity resolution5.3Centrality metrics5.4Community detection5.5Graph databases with Neo4j5.6Graph neural networks applied to fraud5.7Visualisation of suspicious networks5.8Organised fraud and networks of collaborators5.9Integration with individual scoring5.10Practice: detection of a network of linked claims
6.1Natural language processing in statements6.2Language models for summarising case files6.3Detection of inconsistencies in accounts6.4Forensic analysis of images and metadata6.5Detection of manipulated documents6.6Reused images and reverse search6.7Voice and call analysis with ethical limitations6.8Multimodal integration of signals6.9Risks of bias and false positives6.10Workshop: text classifier for friendly settlements
7.1Integration into the claims workflow7.2Real time versus batch processing7.3Alert management and prioritisation7.4Feedback from investigators7.5Monitoring and retraining7.6European AI Regulation and human oversight7.7GDPR and automated decisions7.8Fairness and non-discrimination7.9Measuring return and savings7.10Case study: putting an anti-fraud model into production
8.1Definition of the scope and the problem8.2Preparation of the dataset8.3Exploratory analysis8.4Supervised and unsupervised modelling8.5Network analysis8.6Selection of operational thresholds8.7Economic evaluation8.8Deployment and governance plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 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 Python (scikit-learn, PyOD), Neo4j, FRISS, SAS Fraud Management, spaCy, in free, academic or evaluation versions. Trials are studied using real procedures for fraud scoring models with imbalanced data, anomaly detection in claims, and graph analysis of relationships between those involved. Assessment is continuous across each 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 fraud risk points in the claims handling cycle.
Prepare claims data for anti-fraud models, including handling class imbalance.
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 level 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 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.
You will work with Python (scikit-learn, PyOD), Neo4j, FRISS, SAS Fraud Management and spaCy. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM upon passing the continuous assessment and the 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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