AI for Finance

The learning outcomes for this course are designed to ensure that participants can recall key concepts of Artificial Intelligence (AI), covering fundamental topics such as machine learning, neural networks, and deep learning. The course will introduce participants to the origins and evolution of AI, along with its significant use cases in finance. By gaining an understanding of essential terminologies, including supervised and unsupervised learning, participants will be better equipped to comprehend more advanced AI applications. Additionally, they will learn to describe common AI use cases in the financial sector, with specific examples drawn from banking, investment, and insurance. This strong foundational understanding will prepare participants for more advanced topics later in the program.

Key Topics:

• What is AI? Definitions and scope.
• Evolution and milestones of AI.
• Key concepts: machine learning, neural networks, deep learning and Generative AI. This includes a detailed discussion of core terminology and relevant vocabulary (e.g., supervised vs. unsupervised learning, model training, neural architectures) ensuring participants fully grasp the language of AI.
• Use cases of AI in finance: banking, investment, and insurance.
• Opportunities and limitations of current AI technologies.
   

This course aims to deepen participants' understanding of machine learning and its various methods. It will provide insights into the main components of a machine learning model pipeline, which includes data collection, preprocessing, model training, and evaluation. Participants will explore practical applications in finance through real-world use cases, such as credit risk analysis, fraud detection, and algorithmic trading. Additionally, the course will cover key methods of machine learning, including supervised and unsupervised learning, as well as natural language processing. By the end of the course, participants will be equipped to determine whether machine learning is a suitable solution for common business cases in the financial services industry.

Key Topics

• More detailed overview of ML methods: supervised + unsupervised learning, natural language processing
• Ingredients of ML: Data collection, preprocessing, model training, and evaluation.
• Introduction to use cases of machine learning in finance: Credit risk analysis, fraud detection, and algorithmic trading.
• Hands-on coding session in Google Colab e.g. Predicting Loan Default Risk Using Machine Learning. Non-technical users will only need to modify small parameters or observe the outcomes after running the code.
   

This course will provide participants with an in-depth understanding of reinforcement learning and neural networks. It will introduce generative AI, including large language models (LLMs) and multimodal models, which represent a significant recent application of reinforcement learning. Participants will learn how these advanced methods are applied in finance, with a particular focus on process automation, financial forecasting, and report analysis.

The learning outcomes for this course include the ability to differentiate between various types of AI, such as machine learning, deep learning, reinforcement learning, and LLMs. Additionally, participants will describe how generative AI and multimodal models are utilized in finance, emphasizing their roles in automation, financial forecasting, and report analysis. Finally, they will apply prompt engineering techniques to optimize the output of these AI systems, ensuring practical application in real-world scenarios.

Key Topics

• Fundamentals of Reinforcement learning
• Introduction to Generative AI as an application of reinforcement learning: Core concepts, opportunities, risks, and benefits.
• Exploring transformers architecture for text generation and multimodal AI.
• LLMs: How they work and their applications in finance (e.g. forecasting, gathering market intelligence, process automation, and report analysis).
• Multimodal models: Combining text, image, and audio e.g. analyzing diverse data sources (text, image, and audio) for enhanced decision-making and market insights.
• Prompt engineering: Crafting effective prompts for LLMs (zero-shot, single-shot, and few-shot learning).
• Further use cases e.g. automation in financial reporting, and customer support.

In this module, participants will work in groups to solve a real-world case study focused on AI applications in finance. The goal is to apply knowledge gained in Modules 1 to 3 and develop practical solutions.

Collaboration takes place through online peer sessions, enabling knowledge exchange and joint problem-solving. Throughout the module, expert-led checkpoints offer guidance, reflection, and targeted input to support learning and progress.

Key Elements:

• Group-based case study work
• Application of content from Modules 1–3
• Interactive peer exchange
• Expert checkpoints for reflection and feedback

The objective of this course is to equip participants with the knowledge required to develop a robust AI strategy for their organizations. Participants will learn about the key elements involved in creating and implementing such a strategy, including data governance, system integration, and the management of AI-related risks. The course will also highlight best practices for avoiding common pitfalls and ensuring that AI initiatives are closely aligned with broader business goals.

By the end of the course, participants will be able to explain which areas within an organization will be involved in the implementation of AI. They will gain skills in applying change management strategies to facilitate the adoption of AI technologies and will be prepared to develop a comprehensive AI strategy and roadmap that aligns with their organization’s business objectives. Additionally, participants will analyze the core components of an AI strategy to understand their roles in successful AI implementation within a financial organization.

Key Topics

• Building an AI strategy and roadmap for organizational implementation.
• Data governance and the importance of data quality for successful AI implementations.
• Evaluating AI use cases based on potential impact, value proposition, and cost-effectiveness.
• Integrating AI into existing systems and workflows.
• Managing risks in AI implementation: Bias, transparency, and accountability.
• Change management strategies for AI transformation within the organization.
• Analyzing a real-world financial organization’s AI implementation strategy, including lessons learned and challenges faced.
• Case study: DBS’ AI Journey

The objective of this course is to provide participants with a comprehensive understanding of how AI can transform various departments within financial institutions. By exploring practical use cases in areas such as wealth management, credit risk, accounting, and compliance, participants will gain insights into the potential of AI to address specific business challenges.

Through this course, participants will learn to analyze how AI-driven solutions, including robo-advisors, can effectively tackle challenges in wealth management. They will also evaluate the effectiveness of AI applications in enhancing business processes across multiple areas within an organization, enabling them to make informed decisions about AI’s role in driving organizational improvements.

Key Topics

• AI-driven applications in wealth management (e.g., robo-advisors).
• Deep dive machine learning applications: Predictive modeling and risk management.
• AI in compliance and regulatory monitoring.
• Customer service automation and chatbots.
• Knowledge management with AI.
• Case Study: ChatGPT and Generative AI in Accounting

Equip participants with the knowledge and tools to address the ethical, regulatory, and risk management challenges posed by AI in the financial industry. Provide strategies for ensuring compliance with evolving regulations and mitigating risks.

Key Topics (The Risks, Ethics, and Regulatory Implications of AI):

• Ethical challenges in AI: Bias, fairness, and accountability.
• How to prevent and manage AI hallucinations.
• Navigating the EU AI Act and other relevant regulatory frameworks.
• Data privacy and security in AI deployment: Best practices for protecting sensitive information, with an emphasis on GDPR compliance.
• AI-driven decision-making: Ensuring transparency, interpretability, and fairness.
• Case studies of AI failures and ethical dilemmas, and best practices for mitigation.
• Case study: How Aggressively Should a Bank Pursue AI?
• Key Topics (Closing Session):
• Emerging Technologies and how they could converge with AI: Quantum computing, blockchain, and autonomous systems.
• AI’s impact on workforce transformation and skills required for future financial services.

Closing Session

The Future of AI: The closing session, lasting about 1.5h, will offer insights into the future of AI, focusing on trends and emerging technologies that will drive innovation and ensure competitiveness in the financial sector.

In order to get your certificate, you must pass several examinations.

50% Group presentation on the last day of the course

On the first day of the course, you will be divided into small groups by the course leader (approx. 4 people per group).

You will be assigned your topic by the course leader. You now have six weeks to prepare a group presentation.

The group presentation will take place on the last day of the course.

You will have 30 minutes (20 minutes for your presentation, 10 minutes questions). Each member of the group must be included in the presentation.

Your final grade will depend on your individual performance and may differ from your group members.

50% Multiple choice test

MC test in the online campus (Canvas) in the following week after the last day of the course. If you fail to pass the test, you can repeat it one more time in the following week.

You have up to 60 minutes to complete the test.

This text will be an open book with random questions, designed to be solved in a short time.

Passing Requirement

• Minimum 50% overall
  At least 50% in each section