Advanced Machine Learning for Financial Services

Key Takeaways

Upon completion of this 4-day Advanced Machine Learning for Financial Services course, attendees will learn how to (in the context of finance sector):

• Analyse and select the type of algorithms for their business problems.
• Implement and evaluate both ensemble and deep learning methods for real world financial applications.
• Evaluate the architectural considerations for deployment and monitoring.
• Analyse the role of MLOps and the role of responsible & explainable AI in financial services along with some practical examples.

Who Should Attend

Pre-requisites:

• Working knowledge of the finance sector
• A good initial understanding of AI/ML concepts and "white-box" models
• Comfortable with programming in Python/PyTorch/Tensorflow
• Comfortable using Google Colab notebooks 

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:

What Will Be Covered

This course will cover:

• Deep Learning & Financial Applications
  • Gain a comprehensive overview of deep learning and its various neural network architectures.
  • Apply diverse neural networks to practical finance problems. For example, utilize Convolutional Neural Networks (CNNs) for real-time credit card fraud detection and Generative Adversarial Networks (GANs) to address deficiencies in Value at Risk (VaR) modeling.
  • Apply reinforcement learning to resolve inefficiencies in portfolio hedging strategies.
  • Overcome inaccurate or slow technical indicator analysis by leveraging transfer learning.
• Generative AI & Model Development
  • Utilise Generative AI, AI Agents, and related technologies—such as Agentic IDEs and Model Context Protocols—to streamline and assist in the development of financial models.
• Explainability, Transparency & Compliance
  • Address transparency challenges in financial decision-making. Apply explainable AI (XAI) approaches—such as regression, decision trees, and feature attribution—to clarify the exact reasoning behind credit approvals, risk scores, and investment recommendations.
  • Demystify intricate financial models using model-agnostic techniques. This includes providing both local explanations (e.g., why a portfolio stress test flagged specific assets) and global explanations (e.g., understanding the overarching drivers of risk exposure).
  • Ensure responsibility, fairness, and compliance in AI-driven financial services. Evaluate models for bias and strictly align them with regulatory frameworks—including MAS guidelines, GDPR, and the EU AI Act—to prevent discriminatory lending and investment practices.

Fees & Subsidies

1. SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
2. 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.
3. SME fees are applicable only to participants who are sponsored by small and medium enterprises.
4. SSG funding is subjected to availability.

Certificate

Certificate of Completion
The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course.

Preparing for Your Course

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

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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.