Master’s in Claims Management and Claims Analytics Engineering (SIU/Anti-Fraud)
Online master's programme of 1,900 hours in claims management and claims analytics engineering (SIU/anti-fraud): 12 modules covering technical claims handling, claims data, KPIs, predictive models, the special investigation unit, fraud detection and investigation, the anti-fraud legal framework, recoveries, litigation and performance improvement, with an assigned technical tutor.
Master’s in Claims Management and Claims Analytics Engineering (SIU/Anti-Fraud) — studying from the United Kingdom
Insurance and risk analysis are important sectors in the United Kingdom, with London's market, the City of London and Edinburgh's financial services hub employing large numbers of actuaries, risk analysts and data specialists. Engineering knowledge is increasingly valuable in assessing risk for vehicles, infrastructure, energy and emerging technologies. Insurers and reinsurers use modelling, catastrophe analysis and telematics data. Regulation by the Prudential Regulation Authority and the Financial Conduct Authority shapes professional practice in this area.
Fees for the United Kingdom: £6,600 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies relate to actuarial and risk qualifications, and does SEIUM's training count towards their exams?
In the UK, actuarial professional status is governed by the Institute and Faculty of Actuaries, which runs its own examinations and accreditation. SEIUM's training is not accredited by the IFoA or any national body, and it does not confer exemptions from those exams unless the IFoA itself decides so. Confirm exemption policies directly with the IFoA before enrolling.
Do UK insurers and risk firms hire people with private online engineering or data qualifications?
UK insurers and risk consultancies recruit on the basis of skills, experience and recognised credentials. A private qualification may support an application where it demonstrates applied data or risk modelling, but employers decide its value case by case. Practical project work often carries more weight, so present your final project clearly on your CV and in interviews.
How much study time is needed, and can I pay for it in instalments while working in the UK?
Programmes range from 300 hours for specialist courses to 1,900 hours for master's programmes, all delivered online in English. You are assigned a technical tutor for support. Fees are payable in instalments, and employer sponsorship is possible. Plan weekly study hours realistically, as the total hours are substantial for a part-time learner.
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
Apply technical and legal criteria in claims management.
Manage and exploit claims data with quality.
Design claims indicators and dashboards.
Build predictive models for triage, severity and litigation.
Organise and lead special investigation units.
Detect and investigate fraud in line with the legal framework.
Optimise recoveries and litigated claims.
Who it's for
- Claims managers at insurers.
- SIU investigators.
- Claims analysts building indicators and predictive models.
- Complex claims handlers.
- Recovery managers.
- Anti-fraud managers.
- Engineering or science graduates seeking to enter the insurance sector.
What you'll work with
- Law 50/1980 (Spanish Insurance Contract Law)
- Organic Law 10/1995 (Spanish Criminal Code)
- Regulation (EU) 2016/679 (GDPR, data protection)
- Law 5/2014 (Spanish Private Security Law)
- Law 35/2015 (motor accident damages assessment system)
- Organic Law 3/2018 (LOPDGDD, Spanish data protection and digital rights law)
- ISO/IEC 27037:2012 (digital evidence)
- Guidewire ClaimCenter
- SAS Fraud Management
- Neo4j
- Power BI
- Python (scikit-learn)
12 modules · 1900 h
1.1Coverages and their interpretation in claims1.2Coverage verification1.3Deductibles, limits and sub-limits1.4Underinsurance and the average clause1.5Concurrent insurance1.6Insured's obligations after a loss1.7Statutory payment deadlines1.8Article 20 late-payment interest1.9Claim declinature1.10Case study: determining the indemnity with underinsurance
2.1Claims data model2.2Data quality in claim files2.3Structured and unstructured data2.4Integration with policies and payments2.5Supplier data2.6External data and enrichment2.7Catalogue of claims variables2.8Claims data governance2.9Privacy in claims data2.10Practical exercise: quality profiling of a claims database
3.1Frequency, average cost and loss ratio3.2Cycle times3.3Reopening rates3.4Litigation ratio3.5Leakage and its measurement3.6Claims handler productivity3.7Claims dashboards3.8Benchmarking3.9Root-cause analysis of deviations3.10Workshop: claims dashboard in Power BI
4.1Claims triage by complexity4.2Severity prediction4.3Litigation prediction4.4Duration prediction4.5Optimal allocation of claims handlers4.6Recovery models4.7Reopening prediction4.8Explainability for claims handlers4.9Deployment in claims systems4.10Lab: triage model with gradient boosting
5.1Functions of a special investigation unit5.2Organisation and profiles5.3Case referral criteria5.4Investigation workflow5.5Workload management5.6Relationship with claims handlers5.7Collaboration with external investigators5.8SIU indicators5.9Training investigators5.10Case study: designing an SIU
6.1Expert rules and red flags6.2Supervised fraud models6.3Unsupervised anomaly detection6.4Social network analysis of claims6.5Text analysis of statements6.6Image and document analysis6.7Combining scores6.8Threshold tuning and false positives6.9Feedback from investigations6.10Practical exercise: network analysis of parties involved in claims
7.1Investigation planning7.2Interviews and statements7.3Inspection of the loss scene7.4Reconstruction of events7.5Digital evidence7.6Open sources7.7Document verification7.8Investigation report7.9Evidential value in court7.10Case study: investigation of a suspicious fire
8.1Fraud in the Criminal Code8.2Insurance fraud offence8.3Insurance Contract Law and wilful misconduct8.4GDPR and anti-fraud processing8.5Industry shared databases8.6Private detectives and the Private Security Law8.7Reporting and cooperation with the police8.8Recovery of amounts8.9Rights of the insured under investigation8.10Workshop: legal analysis of a fraud file
9.1Basis of subrogation9.2Identifying recovery opportunities9.3Agreements between insurers9.4Recoveries against manufacturers9.5International recoveries9.6Recovery analytics9.7Management of recovery providers9.8Recovery litigation9.9Recovery indicators9.10Practical exercise: prioritising a recovery portfolio
10.1Managing litigated claims10.2Selecting lawyers10.3Defence costs10.4Mediation and settlements10.5Assessment of bodily injury10.6Liability claims10.7Large losses10.8Coordination with reinsurers10.9Reserve management in litigation10.10Case study: strategy for an injury litigation case
11.1Measuring anti-fraud savings11.2Return on investment in analytics11.3Claims handling quality11.4Claim file audits11.5Customer experience versus control11.6Training claims handlers11.7Continuous improvement11.8Communicating results11.9Medium-term claims strategy11.10Workshop: business case for an anti-fraud platform
12.1Selection of the line of business and problem12.2Claims data analysis12.3Design of indicators12.4Predictive or anti-fraud model12.5Organisational proposal12.6Legal analysis12.7Business case12.8Conclusions12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, so it fits around your job.
Each module combines theory, guided reading of the original regulations and applied exercises. The working software is Guidewire ClaimCenter, SAS Fraud Management, Neo4j, Power BI and Python (scikit-learn), in free, academic or evaluation versions. Exercises are based on real procedures: predictive claims triage, investigation of suspicious claims and network analysis of the parties involved. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Law 50/1980 on Insurance Contracts, Organic Law 10/1995 (Criminal Code), Regulation (EU) 2016/679 (GDPR), Law 5/2014 on Private Security, Law 35/2015 reforming the motor accident damages assessment system, Organic Law 3/2018 (LOPDGDD), ISO/IEC 27037:2012.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
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 totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Guidewire ClaimCenter, SAS Fraud Management, Neo4j, Power BI and Python (scikit-learn), using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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