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The module aims to introduce the students to the basics of portfolio theory. Beginning with a summary of the reasons why both private investors and large institutional investors might wish to own share portfolios, the module progresses to consider how risk and return vary as share prices move and introduces the student to the basics of Markowitz portfolio theory. Illustrative two-asset cases will then be considered before the risk/reward diagram for an N asset portfolio is examined. The notions of
short selling and riskless assets will then be introduced to the student and incorporated into the theory. Finally, the student will learn how to solve the general Markowitz portfolio problem to
determine the Optimum portfolio, the Capital Market Line and the Market Price of Risk. If time permits, discussion will also take place of more advanced models of portfolio theory.

This module explores financial reporting fundamentals through analysing annual reports that stakeholders use to evaluate performance. Students will develop understanding of financial statement preparation and its critical interpretation, examining how accounting standards shape business reporting and stakeholder decision-making while considering sustainability imperatives

Students will be introduced to the regulation of financial reporting; the information perspective to financial reporting; the valuation relevance of financial reporting; economic consequences and Positive Accounting Theory; Earnings management.

Students will be introduced to the regulation of financial reporting; the information perspective to financial reporting; the valuation relevance of financial reporting; economic consequences and Positive Accounting Theory; Earnings management.

The module explores bank regulations as well as theoretical and practical techniques to measure market risk, interest rate risk and credit risk. It also discusses the theoretical and practical aspects of the risk management techniques employed in the financial services industry to hedge market risk, interest rate risk and credit risk.

This module explores traditional financial risk management and bank regulation in the context of an increasingly AI-driven and digitally enabled financial system. It covers core tools for measuring and managing market, credit and interest risk, then examines how AI and other data-driven models reshape risk transmission, shift risks to new players and can accelerate episodes of market stress. The module highlights how these developments both challenge and enhance existing risk frameworks and regulation, and shows how financial risk management must adapt to contemporary risks and opportunities created by AI and digital finance.

This module explores traditional financial risk management and bank regulation in the context of an increasingly AI-driven and digitally enabled financial system. It covers core tools for measuring and managing market, credit and interest risk, then examines how AI and other data-driven models reshape risk transmission, shift risks to new players and can accelerate episodes of market stress. The module highlights how these developments both challenge and enhance existing risk frameworks and regulation, and shows how financial risk management must adapt to contemporary risks and opportunities created by AI and digital finance.

This module explores traditional financial risk management and bank regulation in the context of an increasingly AI-driven and digitally enabled financial system. It covers core tools for measuring and managing market, credit and interest risk, then examines how AI and other data-driven models reshape risk transmission, shift risks to new players and can accelerate episodes of market stress. The module highlights how these developments both challenge and enhance existing risk frameworks and regulation, and shows how financial risk management must adapt to contemporary risks and opportunities created by AI and digital finance.

The module seeks to equip students with essential practical and technical skills that are critical for success in the financial sector. It is designed to develop students' competencies in key areas such as financial data analysis, financial modelling, programming for finance, use of industry-standard databases, and effective communication of financial information. Through a combination of workshops, case studies, simulations, and certifications, students will acquire the applied knowledge and transferable skills that are highly valued by employers across a range of financial careers.

This module examines how financial technologies (FinTech) and applied artificial intelligence (AI) are reshaping financial services. It is deliberately not a model‑building module: core AI/ML concepts are covered at an intuitive level (what they are, where they work, where they fail), and the emphasis is on turning those concepts into practical, auditable workflows that analysts, product teams, operations, risk and compliance functions can use in the age of AI and FinTech.
You will learn how to: (i) design effective prompts for common finance tasks, (ii) use AI copilots to prototype lightweight tools and scripts that support financial work, and (iii) design workflow automation solutions for back‑office and compliance processes.
FinTech topics (cryptocurrencies, decentralised finance (DeFi), open banking and embedded finance, platforms/ecosystems, RegTech/SupTech, digital assets, payments and lending) are used as contexts for applied exercises rather than as the core syllabus. This positioning reduces overlap with modules focused on digital money/banking, banking transformation, or technical machine learning. You will finish the module with hands‑on artefacts (prompt packs, workflow designs, lightweight prototypes and controls documentation) that translate directly into workplace skills.

The module is designed for students on the MSc (Finance) or equivalent programmes. It assumes prior exposure to core finance (corporate finance, investments, basic risk management) and introductory statistics. Prior programming experience is not required; students with stronger technical backgrounds are encouraged to take the companion “AI and Machine Learning in Finance” module for deeper model‑building and coding experience.