Key Takeaways
By the end of the course, participants will be able to:
- Demystify Risk Domains: Comprehend the primary risk management categories (market, credit, operational) and confidently tackle their unique industry challenges.
- Implement Robust Governance: Build, evaluate, and scale model risk management (MRM) frameworks across diverse business scenarios.
- Deploy Advanced Machine Learning: Construct high-performance ML models to quantify and mitigate financial risks.
- Ensure Regulatory-Grade AI: Leverage Explainable AI (XAI) techniques like SHAP and LIME to justify complex model predictions and guarantee fairness.
- Lead Future AI Trends: Design and deploy next-generation solutions utilising autonomous AI Agents and agentic platforms to optimise risk controls and prepare for upcoming market shifts.
Who Should Attend
This course is designed for aspiring data analysts and data scientists who wish to build a career in the finance industry and specifically in risk management. This course is also suitable for existing finance/risk management professionals who wish to utilise AI to solve their real-life risk management problems.
This includes:
- Aspiring Data Scientists & Analysts seeking to build an elite, specialised career in the financial services sector.
- Risk & Compliance Officers wanting to understand how to govern, audit, and validate complex machine learning models.
- Financial & Quantitative Professionals looking to upscale their technical programming skills and build intelligent risk models.
- FinTech & RegTech Developers and Consultants aiming to build scalable, AI-powered compliance and risk-mitigation software.
Pre-requisites
- A basic conceptual understanding of machine learning modelling (both transparent white-box and complex black-box models).
- Proficiency and hands-on comfort with programming in Python.
What to Bring
No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.
If you are bringing a laptop, please see below for the tech specs:
| Minimum | Recommended |
Operating Systems | • Windows 7, 8, 10 or
• Mac OS | Laptop running the latest
version of either Windows or
Mac OS |
System Type | 32-bit | 64-bit |
Memory | 8 GB RAM | 16+ GB RAM |
Hard Drive | 256 GB disk size | |
Others | • An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) | DirectX 10 graphics card for graphics hardware acceleration
|
What Will Be Covered
- Analyse an overview of market risk & common industry challenges; investigate how ML is applied to solve market risk problems, including time series/volatility modelling (ARIMA, GARCH models) augmented with LSTMs to improve Value at Risk (VaR) and Expected Shortfall calculations
- Examine an overview of credit risk & common industry challenges; investigate modelling credit risk and predicting default probabilities using classical linear, logit, and probit regressions
- Evaluate an overview of operational risk & common industry challenges; assess approaches for modelling operational risk, such as fraud detection and anti-money laundering (AML) using autoencoders
- Define key components of a robust model risk management framework, including model inventory, validation, monitoring, and governance
- Identify regulatory governance expectations for model risk management (MAS Thematic Model Risk Management Review, EU AI Act) and explain how these regulations impact the use of AI models in risk management
- Provide clear explanations for complex risk models; use model-agnostic techniques to justify predictions, such as explaining why a portfolio stress test flagged certain assets (local explanations) or understanding overall drivers of risk exposure (global explanations)
- Ensure responsibility, fairness and compliance in AI-driven financial services; evaluate models for bias and align them with regulatory principles from MAS, GDPR, and the EU AI Act to prevent discriminatory lending or investment practices
- Explore future trends of AI in risk management, increasing demand for transparency in AI-driven risk decisions and real-time analysis of risks.
Fees & Subsidies
SkillsFuture Singapore (SSG) Funding 2026 (Effective 1 July)
| Fee Component | Full Course Fees | Singapore Citizens & PRs Aged 21 Years and Above (70% Funding Support) | Singapore Citizens Aged 40 Years and Above (90% Funding Support) | Enhanced Training Support for SMEs (ETSS) (90% Funding Support) |
|---|
| Full Course Fee | S$3,800.00 | S$3,800.00 | S$3,800.00 | S$3,800.00 |
| SSG Funding | - | S$2,660.00 | S$2,660.00 | S$2,660.00 |
| Nett Course Fee | S$3,800.00 | S$1,140.00 | S$1,140.00 | S$1,140.00 |
| 9% GST on Nett Course Fee | S$342.00 | S$102.60 | S$102.60 | S$102.60 |
| Additional Funding if Eligible Under Various Schemes | - | - | S$760.00 | S$760.00 |
| Total Nett Course Fee Payable, Including GST | S$4,142.00 | S$1,242.60 | S$482.60 | S$482.60 |
|---|
Note:
- SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
- All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
- SME fees are applicable only to participants who are sponsored by small and medium enterprises.
- SSG funding is subjected to availability.
Certificate
The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course and pass the required assessments.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.