Diploma in InsurTech Compliance Auditing
Online postgraduate diploma of 800 hours in InsurTech compliance auditing, structured in 8 modules covering fundamentals, regulatory framework, analysis methods, design, systems integration, risk management, validation and an integrative case study, with an assigned technical tutor.
Diploma in InsurTech Compliance Auditing — studying from Singapore
Singapore is a major insurance and reinsurance hub in Asia, regulated by the Monetary Authority of Singapore. Engineering skills support risk modelling, catastrophe analysis, data analytics and actuarial work for insurers and reinsurers. The sector draws on data science and quantitative methods, with demand from insurers, brokers and risk consultancies. The city's financial ecosystem makes it a common base for global risk and actuarial teams.
Fees for Singapore: S$1,280 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body. In Singapore, actuarial work is linked to the Singapore Actuarial Society, and insurance is regulated by the Monetary Authority of Singapore. Recognition of a private qualification is decided by employers or the relevant body, so confirm with them directly before you enrol.
How might this training be used by employers in Singapore?
Singapore's insurance, banking and risk functions employ people with analytical and data skills. An engineering-based risk and data programme may complement such roles, but it does not guarantee a position or a specific career outcome. Employers usually weigh professional exams, experience and demonstrated analytical ability, so your final project and work history will matter as much as the qualification title.
Can I study this alongside a full-time job, and can my employer help fund it?
Because the course is fully online and taught in English, you can study from Singapore without travelling, and no student visa is needed for online study. The workload is substantial, so plan carefully around your job. Fees can be paid in instalments, and employer sponsorship is possible; many Singapore companies support job-relevant study, so discuss it with your manager.
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
Master the technical fundamentals of InsurTech compliance auditing and their regulatory vocabulary.
Apply Solvency II (Directive 2009/138/EC), IFRS 17 and Delegated Regulation (EU) 2015/35 to real design and validation decisions.
Build and correlate calculation models with R and Python (pandas, scikit-learn).
Plan and run test campaigns in an actuarial modelling environment and on a claims data platform.
Analyse operating data and turn it into engineering decisions.
Prepare the technical and compliance dossier required by the client or the regulator.
Lead a InsurTech compliance auditing project technically from start to finish.
Who it's for
- Non-life actuaries moving into InsurTech compliance auditing.
- Risk analysts who need a solid technical grounding in the field.
- Technical underwriters handling this type of cover.
- Loss adjusters dealing with claims in this line of business.
- Pricing managers designing or reviewing tariffs.
- Technical reserves managers.
- Engineering or science graduates looking to enter motor insurance.
What you'll work with
- Solvency II (Directive 2009/138/EC) (EU prudential regime for insurers)
- IFRS 17 (accounting standard for insurance contracts)
- Delegated Regulation (EU) 2015/35 (Solvency II implementing rules)
- EIOPA Guidelines (European supervisory guidance)
- ISO 31000 (risk management)
- Law 20/2015 (LOSSEAR) (Spanish insurance supervision law)
- Regulation (EU) 2016/679 (GDPR) (data protection)
- DORA (Regulation (EU) 2022/2554) (digital operational resilience)
- AI Regulation (EU) 2024/1689 (AI Act)
- R
- Python (pandas, scikit-learn)
- SAS
- Prophet
- ResQ
8 modules
1.1Key concepts and quantities in InsurTech Compliance Auditing1.2Technology landscape and state of the art1.3Technical terminology and reference documentation1.4Physical and engineering principles applied to the case1.5Functional architecture: from requirements to components1.6Interfaces and dependencies with adjacent systems1.7Typical use cases in the sector1.8Common errors and how to detect them early1.9Data sources and quality criteria1.10Guided exercise: initial diagnosis
2.1Applicable regulatory framework: overview2.2Regulatory hierarchy and conflicts between standards2.3Mandatory requirements versus good practice2.4Acceptance criteria and required evidence2.5Technical documentation and traceability2.6Change management and configuration control2.7Audits, inspections and non-conformities2.8Responsibilities and division of scope2.9Interpretation of critical clauses2.10Exercise: regulatory checklist
3.1Setting up the model: assumptions and simplifications3.2Selection of tools and working environment3.3Preparation of input data3.4Workflow and good modelling practice3.5Discretisation and case preparation3.6Boundary conditions and load cases3.7Convergence control and numerical verification3.8Sensitivity analysis and parametric study3.9Automation and reuse of models3.10Exercise: building a base model
4.1Design requirements and starting criteria4.2Solution alternatives and comparative analysis4.3Sizing and safety margins4.4Selection of materials, equipment or components4.5Integration with the rest of the system4.6Cost, schedule and manufacturing constraints4.7Reliability, maintainability and service life4.8Sustainability and efficiency in design4.9Design review and risk management4.10Exercise: documented design proposal
5.1System architecture and functional breakdown5.2Interface definition and control5.3Protocols and data exchange5.4Progressive integration and interface testing5.5Electromagnetic and environmental compatibility5.6Configuration management of the whole system5.7Resolving conflicts between subsystems5.8Commissioning of the integrated system5.9Traceability from requirements to verification5.10Exercise: interface matrix
6.1Hazard identification and preliminary analysis6.2Failure modes and effects analysis6.3Fault trees and quantitative analysis6.4Allocation of reliability targets6.5Barriers, redundancy and fault tolerance6.6Human factors and operating errors6.7Safety evidence and argumentation6.8Risk management throughout the life cycle6.9Indicators and monitoring in operation6.10Exercise: risk analysis of a case
7.1Verification and validation strategy7.2Test plan: scope and coverage7.3Instrumentation and data acquisition7.4Laboratory testing versus field testing7.5Correlation between model and measurement7.6Handling uncertainty and tolerances7.7Failure analysis and corrective actions7.8Commissioning and acceptance criteria7.9Documentation of the validation file7.10Exercise: validation report
8.1Case definition and success criteria8.2Collection of input data and requirements8.3Work planning and deliverables8.4Technical development guided by the tutor8.5Applying the regulatory framework to the case8.6Verification of results and critical analysis8.7Documentation of the deliverable8.8Technical defence and peer review8.9Lessons learned and continuous improvement8.10Final project: final assessment and programme close-out
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, in free, academic or evaluation versions. Testing is studied on real procedures from an actuarial modelling environment, a claims data platform and a catastrophe scenario simulator. Assessment is continuous by module, plus the final integrative project.
Standards covered: Solvency II (Directive 2009/138/EC), IFRS 17, Delegated Regulation (EU) 2015/35, EIOPA Guidelines, ISO 31000, Law 20/2015 (LOSSEAR), Regulation (EU) 2016/679 (GDPR), DORA (Regulation (EU) 2022/2554), AI Regulation (EU) 2024/1689
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
- Prior technical training or professional experience in the sector. No university degree is required.
- An internet connection and a computer able to run 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with R, Python (pandas, scikit-learn), SAS, Prophet and ResQ, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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