Master'sInsurance, Risk & Actuarial Data Applications open

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.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma

Master’s in Claims Management and Claims Analytics Engineering (SIU/Anti-Fraud) — studying from Canada

Canada has a major insurance and financial services sector, with headquarters and operations concentrated in Toronto and Montréal. Insurers and reinsurers rely on actuarial science, risk modelling and data analytics for pricing, reserving and capital management. Federal and provincial regulators oversee solvency, conduct and consumer protection. Engineers and data specialists with risk and modelling skills find roles at insurers, brokers, consultancies and regulatory bodies.

Fees for Canada: C$12,400 (Canadian dollar), with instalments and employer sponsorship available.

Relevant bodies in Canada

  • Office of the Superintendent of Financial Institutions (OSFI)
  • Canadian Institute of Actuaries (CIA)
  • Financial Services Regulatory Authority of Ontario (FSRA)
  • Insurance Bureau of Canada (IBC)

Typical roles

  • Actuarial Analyst
  • Risk Analyst
  • Data Scientist – Insurance
  • Reserving Analyst

Questions from Canada

Is this programme recognised by professional bodies in Canada for insurance and actuarial data work?

SEIUM's qualifications are private, not official state degrees, and are not accredited by a national body in Canada. Actuarial credentials in Canada are governed by the Canadian Institute of Actuaries, and actuarial practice is regulated under provincial and federal rules. A private engineering qualification does not confer these credentials, so confirm recognition with employers or the relevant body yourself.

How might this qualification be used by employers in Canada in insurance or risk roles?

Insurers, banks and risk teams in Canada may value engineering-based data analysis and risk modelling skills, but how they regard a private qualification is their decision. Hiring in these sectors often depends on experience, professional exams and specific credentials. Your final project can serve as evidence of applied skills, though it does not guarantee any role or salary outcome.

What are the practical demands of studying this online from Canada?

Study is fully online and in English, and no student visa is required if you study from home. Programme lengths range from 300 to 1,900 hours, so consider how many hours a week you can commit alongside work. You are assigned a technical tutor for support. Fees can be paid in instalments, and some students receive employer sponsorship, which is possible but not guaranteed.

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Learning outcomes

What you will be able to do

01

Apply technical and legal criteria in claims management.

02

Manage and exploit claims data with quality.

03

Design claims indicators and dashboards.

04

Build predictive models for triage, severity and litigation.

05

Organise and lead special investigation units.

06

Detect and investigate fraud in line with the legal framework.

07

Optimise recoveries and litigated claims.

Profile

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.
Standards & tools

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)
Syllabus

12 modules · 1900 h

  1. 1.1Coverages and their interpretation in claims
  2. 1.2Coverage verification
  3. 1.3Deductibles, limits and sub-limits
  4. 1.4Underinsurance and the average clause
  5. 1.5Concurrent insurance
  6. 1.6Insured's obligations after a loss
  7. 1.7Statutory payment deadlines
  8. 1.8Article 20 late-payment interest
  9. 1.9Claim declinature
  10. 1.10Case study: determining the indemnity with underinsurance
  1. 2.1Claims data model
  2. 2.2Data quality in claim files
  3. 2.3Structured and unstructured data
  4. 2.4Integration with policies and payments
  5. 2.5Supplier data
  6. 2.6External data and enrichment
  7. 2.7Catalogue of claims variables
  8. 2.8Claims data governance
  9. 2.9Privacy in claims data
  10. 2.10Practical exercise: quality profiling of a claims database
  1. 3.1Frequency, average cost and loss ratio
  2. 3.2Cycle times
  3. 3.3Reopening rates
  4. 3.4Litigation ratio
  5. 3.5Leakage and its measurement
  6. 3.6Claims handler productivity
  7. 3.7Claims dashboards
  8. 3.8Benchmarking
  9. 3.9Root-cause analysis of deviations
  10. 3.10Workshop: claims dashboard in Power BI
  1. 4.1Claims triage by complexity
  2. 4.2Severity prediction
  3. 4.3Litigation prediction
  4. 4.4Duration prediction
  5. 4.5Optimal allocation of claims handlers
  6. 4.6Recovery models
  7. 4.7Reopening prediction
  8. 4.8Explainability for claims handlers
  9. 4.9Deployment in claims systems
  10. 4.10Lab: triage model with gradient boosting
  1. 5.1Functions of a special investigation unit
  2. 5.2Organisation and profiles
  3. 5.3Case referral criteria
  4. 5.4Investigation workflow
  5. 5.5Workload management
  6. 5.6Relationship with claims handlers
  7. 5.7Collaboration with external investigators
  8. 5.8SIU indicators
  9. 5.9Training investigators
  10. 5.10Case study: designing an SIU
  1. 6.1Expert rules and red flags
  2. 6.2Supervised fraud models
  3. 6.3Unsupervised anomaly detection
  4. 6.4Social network analysis of claims
  5. 6.5Text analysis of statements
  6. 6.6Image and document analysis
  7. 6.7Combining scores
  8. 6.8Threshold tuning and false positives
  9. 6.9Feedback from investigations
  10. 6.10Practical exercise: network analysis of parties involved in claims
  1. 7.1Investigation planning
  2. 7.2Interviews and statements
  3. 7.3Inspection of the loss scene
  4. 7.4Reconstruction of events
  5. 7.5Digital evidence
  6. 7.6Open sources
  7. 7.7Document verification
  8. 7.8Investigation report
  9. 7.9Evidential value in court
  10. 7.10Case study: investigation of a suspicious fire
  1. 8.1Fraud in the Criminal Code
  2. 8.2Insurance fraud offence
  3. 8.3Insurance Contract Law and wilful misconduct
  4. 8.4GDPR and anti-fraud processing
  5. 8.5Industry shared databases
  6. 8.6Private detectives and the Private Security Law
  7. 8.7Reporting and cooperation with the police
  8. 8.8Recovery of amounts
  9. 8.9Rights of the insured under investigation
  10. 8.10Workshop: legal analysis of a fraud file
  1. 9.1Basis of subrogation
  2. 9.2Identifying recovery opportunities
  3. 9.3Agreements between insurers
  4. 9.4Recoveries against manufacturers
  5. 9.5International recoveries
  6. 9.6Recovery analytics
  7. 9.7Management of recovery providers
  8. 9.8Recovery litigation
  9. 9.9Recovery indicators
  10. 9.10Practical exercise: prioritising a recovery portfolio
  1. 10.1Managing litigated claims
  2. 10.2Selecting lawyers
  3. 10.3Defence costs
  4. 10.4Mediation and settlements
  5. 10.5Assessment of bodily injury
  6. 10.6Liability claims
  7. 10.7Large losses
  8. 10.8Coordination with reinsurers
  9. 10.9Reserve management in litigation
  10. 10.10Case study: strategy for an injury litigation case
  1. 11.1Measuring anti-fraud savings
  2. 11.2Return on investment in analytics
  3. 11.3Claims handling quality
  4. 11.4Claim file audits
  5. 11.5Customer experience versus control
  6. 11.6Training claims handlers
  7. 11.7Continuous improvement
  8. 11.8Communicating results
  9. 11.9Medium-term claims strategy
  10. 11.10Workshop: business case for an anti-fraud platform
  1. 12.1Selection of the line of business and problem
  2. 12.2Claims data analysis
  3. 12.3Design of indicators
  4. 12.4Predictive or anti-fraud model
  5. 12.5Organisational proposal
  6. 12.6Legal analysis
  7. 12.7Business case
  8. 12.8Conclusions
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

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.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Law 50/1980, the Criminal Code, the GDPR and ISO/IEC 27037 applied to real cases, not as a theoretical annex.
Industry software:Guidewire ClaimCenter, SAS Fraud Management, Neo4j, Power BI and Python (scikit-learn), in free, academic or evaluation versions.
Master's Thesis with defence:A claims analytics or anti-fraud solution for a line of business, with its organisational, legal and business case, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

Problem and data analysis
Select a line of business and problem and analyse its claims data.
Indicator design
Design the indicators and dashboard to monitor the problem.
Predictive or anti-fraud model
Build a triage, severity or fraud detection model.
Organisation, legal and business case
Define the organisational proposal, legal analysis and business case.
Master's Thesis
A claims analytics or anti-fraud solution for a line of business, defended before a panel.
Career opportunities

Where this takes you

Claims managerSIU investigatorClaims analystComplex claims handlerRecovery managerAnti-fraud manager
Admissions & scholarships

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.

FAQ

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.

Master’s in Claims Management and Claims Analytics Engineering (SIU/Anti-Fraud)Enrol
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