This 4-hour course provides a comprehensive introduction to Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) in financial services. Participants will explore how generative AI differs from traditional predictive models, understand its reliance on data, and examine its growing role in transforming banking operations, compliance, risk management, and customer engagement. Through practical examples and demonstrations, the course explains foundational concepts such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI governance frameworks. Participants will gain insights into opportunities, risks, and regulatory considerations (e.g., FEAT principles and MAS Project MindForge), preparing them to harness GenAI responsibly and effectively in financial institutions.

Assistant Relationship Managers, Relationship Managers, Team Leads, Investment professionals (e.g., fund managers), C-suite and decision makers in financial services, technology and digital transformation leads, individuals seeking to leverage generative AI in finance.

• Differentiate between traditional Machine Learning (Predictive AI) and Generative AI, explaining their underlying architectures and objectives.
• Describe how data forms the foundation of GenAI and contributes to its continuous improvement through feedback loops.
• Identify key GenAI applications in financial services, such as chatbots, email bots, and voice bots for customer service, risk analysis, and workflow automation.
• Evaluate opportunities and risks in adopting AI across financial institutions, including issues of bias, hallucination, data privacy, and model governance.
• Interpret Singapore’s regulatory and ethical frameworks (e.g., MAS FEAT Principles, Veritas Toolkit, and Project MindForge) guiding responsible AI use.
• Apply GenAI techniques — such as prompt design, RAG integration, and model fine-tuning — to design simple prototypes or AI-enabled workflows.
• Analyse emerging AI trends (e.g., autonomous agents, domain-specialised models) and their implications for the future of finance.

Part 1: Principles and Concepts of AI and GenAI

• Overview of AI, Machine Learning, and Deep Learning.
• Differences between Predictive AI and Generative AI.
• How LLMs work: architecture, training, and prompt mechanics.
• Predictive vs. Generative applications in banking.
• Case studies: NLP chatbots, intent classification, and LLM chatbots.
• Key watchouts: model drift, hallucination, data bias, and explainability.

Part 2: Synergistic Relationship Between Data and GenAI

• The role of data as the foundation of GenAI models.
• Mutual reinforcement between data and AI — continuous improvement loops.
• Quality and diversity over quantity: data curation for fairness and stability.
• Using GenAI to generate and refine new data (labelling, augmentation).
• How proprietary datasets build competitive advantage for FIs.
• Exercise: identifying valuable data sources within financial organisations.

Part 3: Risks and Opportunities of AI and ML in Finance

• Opportunities in Predictive AI and Generative AI for banks and insurers.
• Risk taxonomy: data bias, hallucination, cybersecurity, and regulatory compliance.
• FEAT principles: Fairness, Ethics, Accountability, Transparency.
• Overview of MAS initiatives (Veritas, AI MRM, and Project MindForge).
• Frameworks for responsible AI governance and risk mitigation.
• Hands-on assessment: analysing an AI use-case scenario in finance.

Assessment – MCQ