MSc FinTech and Policy

FinTech is revolutionizing and rapidly changing the global financial services industries. Business models that can facilitate disintermediation, revolutionize how existing firms create and deliver products and services, address privacy, regulatory and law-enforcement challenges, provide new gateways for entrepreneurship, and seed opportunities for inclusive growth. This module provides an introduction to the FinTech sector. Topics may include but are not limited to: blockchain, cryptocurrencies, decentralized applications, smart contracts, payments services, alternative and P2P lending and crowdfunding, AI, machine learning (including applications such as robo-advising) and Insurtech. Students are expected to develop a broad understanding of recent FinTech development, valuation, risk and its impact in the financial industries.

This course introduces modern programming concepts and practice for students with little or no background in computing using the computer language Python. The course will start with a presentation of basic programming concepts, including data types and structures as they exist in Python. Loops and conditional statements will then be introduced, as well as custom functions, along with a wider discussion of structured programming and ways to reuse code. Students will then consider practical applications of programming. They will learn to work with data input and output in different formats, use suitable libraries for scientific computing and data analysis, and create plots and visualizations to display results. Throughout the course, students will engage with professional programming practices and tools (test-driven development, version control, code reviewing, debugging), and will have the opportunity to collaborate with peers to develop their skills.

The module provides the theoretical underpinnings of our MSc Accounting and Finance programme. It introduces the pivotal concepts which form the basis of theoretical finance under three broad headings; Portfolio Theory and Practice, Equilibrium in Capital Markets and Introductory Analysis of Asset Classes. Core concepts include the relationship between risk and return, the Capital Asset Pricing Model (CAPM) and the Efficient Market Hypothesis (EMH) but the module also extends this analysis into new theoretical areas such as Behavioural Finance.

The module has the aim of introducing students to core concepts in the blockchain technology. Students will become familiar with applications of cryptography to financial transactions and the technicalities behind cryptocurrencies and their workings. Using case studies and simulations, students will be exposed to the use of software wallets and engage in blockchain and cryptocurrency related transactions.

Data mining is the process of identifying anomalies, patterns, and correlations within large datasets to predict outcomes. It involves exploring databases to extract potentially useful information such as rules, structures, regularities, and hidden trends. A wide range of analytical techniques is used to transform this information into actionable insights that support decision-making, improve customer relationships, reduce risks, increase revenues, and optimise operational efficiency. In today's data-driven business environment, data mining and business analytics play a critical role in maintaining a competitive advantage. This module introduces key analytical approaches and case studies of well-known applications, including shopping basket analysis (such as loyalty card systems), fraud detection in financial transactions, stock market prediction, risk analysis in banking, web analytics, and social network analysis. In addition, the module introduces AI-driven analytics methods to enhance traditional data mining approaches. These include automated pattern discovery, predictive modelling, and the analysis of unstructured data such as text. Emphasis is placed on how these methods can support more accurate forecasting, deeper insight generation, and improved decision-making in real-world business contexts.

This module provides an introduction to data analytics within a business context, focusing on the foundations of data-driven decision-making and the analytics lifecycle. Students will develop practical skills in data management, business intelligence, visualisation, and introductory machine learning techniques.The module introduces key concepts and frameworks used in business analytics, including the Cross-Industry Standard Process for Data Mining (CRISP-DM), relational databases, structured query language (SQL), exploratory data analysis, and dashboard development. Students will work with real-world datasets to develop analytical solutions that support managerial and operational decision-making.A strong emphasis is placed on transforming data into actionable insights through effective visualisation, storytelling, and business intelligence techniques. The module also introduces fundamental concepts in statistical learning and predictive analytics, providing a foundation for more advanced modules in machine learning and artificial intelligence.

In this module, students develop a holistic understanding of the theory and tools for Entrepreneurship which has a particular relevance in the context of the Digital Economy. This begins with a discussion of key concepts around the Entrepreneur, Opportunity Recognition and the Entrepreneurial Process. In turn, students review key elements for a start-up including resources, the founding team and business modelling. Finally, we look at scaling strategies in the Digital Economy including a range of different business models where start-ups create, deliver and capture value. The Digital Economy enables these innovative business models. For assessment, students research and apply Key Entrepreneurship Topics to case studies and then critically evaluate the scaling strategy for a designated emerging-growth companies in the Digital Economy using Entrepreneurship theory, Digital Economy theory and a business model framework.

This module provides an overview of financial regulation including regulation of banking, capital markets payments and digital assets. Building on prior knowledge of private and public law, it introduces the regulatory frameworks for each of these areas and the policy considerations that animate regulatory policy. The module also examines the impact of financial regulation on emerging FinTech businesses and considers the ways in which new technology challenges traditional models of financial regulation.

This is a compulsory capstone module for the MSc in FinTech and Policy programme. It requires students to independently investigate a specific FinTech and policy issue, applying the analytical, regulatory, and research skills developed across the programme. Students produce a scaffolded Portfolio of Evidence comprising a Research Design Proposal, a Professional Analytical Report in a chosen industry format, and a Reflective Commentary with AI Transparency Statement. The research must be based exclusively on secondary data sources. The module builds directly on the Cases in FinTech and Policy module (MANM521), and students are encouraged to develop their earlier research pitch into a full project. Support sessions and weekly consultations with the Module Convenor are provided throughout the summer term to guide topic selection, research design, and professional report writing.

Cases in FinTech and Policy is a compulsory module that aims to help students develop an understanding of the process involved in successfully analyzing case information to complete a Fintech-related project, in preparation for the FinTech and Policy Project module. It enables students to understand a real-life, complex, multi-layered, global issue and synthesize appropriate information while preparing an analysis on the case and / or critically evaluating the evolving market structure / competitive environment and developments in regulatory policy. The Cases in FinTech and Policy module facilitates the independent learning process and provides students with the knowledge, skills and understanding required to articulate findings in a structured report format.

The module equips students with the knowledge and tools to implement financial models using Python. The course introduces students to the general principles of building financial models, as well as a number of specific financial modelling tools, including matrix calculations, optimization, regression analysis (both time-series modelling and panel data modelling), out-of-sample forecasting and simulation. These methods are applied to a range of practical problems in finance, including passive and active portfolio management, risk management and currency valuation. The emphasis of the course is on practical application of the theory, with lectures on each topic followed by in-depth practical classes, in which students work through real world problems using Python.

As a result of a range of recent corporate scandals an increased emphasis has been put on corporate social responsibility and the need for the board of directors to conform to an ethical code of conduct while managing the affairs of an entity. This module explores how corporate governance examines the system of rules, principles and procedures through which a company is run to promote the best interest of its shareholders, other stakeholders and the wider society, and considers the impact of artificial intelligence on organisations.

Risk management focuses on the following types of financial risk that arise for a business: market risk, credit risk and liquidity risk. A brief introduction to non-financial risks is also given, but is not the main focus. This module considers financial risk management strategies; how risk is evaluated; the methods that are available to hedge against risk and the strategies that are employed to mitigate risk. This is complemented by consideration of key risk management issues for financial institutions including bank capital adequacy. The lessons to be learned from the Financial Crisis of 2007-08 are also investigated. Understanding risk and how to identify and deal with it provides a key input for students of finance, accounting or banking.

This module examines international currency markets and currency transactions, exchange rate systems and the implications of exchange rate volatility. It also explores various currency risks that arise from international operations and examines issues that arise when firms seek financing outside their home markets.

This Innovation Management module is a cornerstone in your business education. This module examines the strategic and tactical dimensions of innovation that a future leader considers when managing an organization or team. It combines established and contemporary conceptual frameworks with practical guidance for analyzing the challenges of leading an innovative organization. In this module, students will discuss the strategy, sources, processes, and implementation of innovation. This module particularly provides students with 1) a holistic understanding of the concept of innovation and its relevance to today’s firm competitiveness; 2) the capability to identify the links between the structures and processes which support specific models of innovation; 3) the capability to recognize the opportunities for, and constraints to innovation in specific business contexts and organizational settings; and 4) the capability to develop practical –oriented skills that are crucial to the management of innovation. In brief, students should come away with models and insights to succeed in each innovation management phase. Finally, the module is designed to provide deep theoretical and practical knowledge by applying theory in case studies and other assignments. The course consists of lectures, seminars, group assignments and an individual assignment.

Students put theory into practice for digital skills by working in a group project with designated companies around a business challenge. The business challenge relates to key skills in the Digital Economy such as digital marketing. In the module, students develop an understanding of key topics related to the theory before applying the key skills. We bring in outside speakers from industry to create an authentic learning experience. For the project, students have two points of assessment to support the learning. Students develop other business skills such as problem-solving, project management, collaboration and resilience which they participate in the project. This supports programme objectives to develop an understanding of theory and practice related to Digital Economy as well as to develop skills relevant for Entrepreneurship.