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This course provides students with a comprehensive introduction to Python programming and its applications in financial data analysis. Students will build a strong foundation for working with financial datasets, including data access and collection, analysis, and visualisation. The course also introduces big data techniques, equipping students to handle large-scale datasets and integrate them into financial contexts. These skills will be applied directly to finance-specific use cases, such as trading data analysis, portfolio simulations, and transaction-level data for payments and risk assessment.

This course introduces students to quantitative trading and examines how artificial intelligence and machine learning are transforming trading and investment strategies. Students will begin by learning the core principles of quantitative trading and exploring common trading strategies such as statistical arbitrage, momentum, and factor investing.

This course focuses on how design thinking and innovation processes can be applied to the financial sector, where AI and digital technologies are rapidly reshaping products and services. Students will learn the design framework and apply it to challenges in financial services. The emphasis will be on integrating finance, AI, marketing, and regulatory perspectives into the innovation process. Through a hands-on design challenge, students will practice building human-centric financial solutions that balance technological potential with usability, trust, and compliance.

This course introduces master’s students, particularly those with limited backgrounds in mathematics and programming, to the essential concepts and techniques for evaluating and improving the ethical quality of AI systems used in financial contexts. Focusing on five key areas — robustness, backdoor-freeness, fairness, privacy, and interpretability — the course provides a structured and accessible framework for assessing and enhancing the trustworthiness of AI models, especially those applied to trading, credit scoring, fraud detection, and risk management. Rather than emphasising technical depth, the course highlights practical tools, financial case studies, and ethical reasoning relevant to decision-makers in banks, asset management, and FinTech.

This course equips students with essential economic frameworks for analysing the digital transformation of finance. Blending microeconomic and macroeconomic theory with hands-on case studies, it explores how artificial intelligence and digital platforms reshape competition, innovation, and systemic stability in financial markets.

Students will gain practical tools for understanding how market structures, platform dynamics, and data externalities influence the adoption of AI in payments, lending, trading, and asset management. At the same time, they will explore how macroeconomic forces — from monetary policy to systemic risk — intersect with AI-driven financial systems.

This project-based design core course brings together all the knowledge and skills students have developed throughout the program — from financial systems and AI applications to design thinking and ethical innovation. Working in teams, students will collaborate with faculty mentors and industry advisors to address real-world challenges in the financial sector.

Through a structured design process, students will identify a problem, conduct user and stakeholder research, prototype and test AI-driven financial solutions, and evaluate their feasibility, ethics, and human impact. Emphasis is placed on human-centered design, ensuring that technology serves the needs of users, organisations, and society at large.

The course concludes with a final project presentation where teams showcase their prototypes or strategic proposals to academic and industry panels. By integrating creativity, analytical rigor, and technological understanding, students will graduate with the ability to design responsible, effective, and innovative AI solutions for real financial contexts.

This course is taught by instructors with extensive industry experience and focuses on the application of AI technologies across three key sectors of the financial system: venture capital investment, financial services, and financial organizations.

Part I (VC Investment) provides an in-depth understanding of venture capital (VC) funding in the AI-driven FinTech sphere. Students will learn how to evaluate opportunities, risks, and valuation models for startups and scale-ups leveraging AI in financial technology. Through case studies and hands-on projects, learners will acquire the skills necessary for sourcing deals, performing due diligence, structuring investments, and guiding portfolio companies in emerging AI-enabled FinTech subdomains, including payments, digital assets, lending, investing and insurance.

Part II (Marketing Analysis) explores how artificial intelligence and machine learning are transforming marketing strategy, customer engagement, and behavioral understanding within the financial services industry. Students will learn to bridge marketing analytics with financial domain data, including credit card transactions, customer journeys, and digital touchpoints, to design responsible, data-driven marketing and personalisation strategies.

Part III (AI-Ready Organisations) equips working professionals to diagnose, design, and drive organisational change in the age of AI.  Building on the strategic, political, and cultural lenses of Organisational Processes, students learn how to align AI adoption with corporate goals, navigate power dynamics, and foster adaptive cultures to drive deployment, adoption and value realization of AI initiatives.  Through case studies, foresight exercises and action projects, participants develop the practical tools to turn insights into organisational action — preparing them to lead AI-enabled transformation with ethical foresight and strategic agility.