Are you numerical, data-driven and interested in new and innovative technology? Do you want to gain vital work experience as you study?
Fintech is changing the financial world - from Cryptocurrency, coding and artificial intelligence to smartphone use in banking and investments. Simply put, Fintech is the technology that works with financial exchanges. Employment roles in the field include being a cybersecurity analyst, an AI and Machine Learning Engineer or financial software and app developer.
Our brand-new MSc in FinTech has been developed jointly by industry leaders and academic experts and sets out to prepare graduates with a strong background in financial theory and integral understanding of the latest innovative technologies imperative in the sector.
Taking a 12-month industrial placement allows you to gain work experience in the UK or overseas as part of your Master's. While the placements are self-sought, the School offers support through extra-curricular engagement with our dedicated placements team. Opting for this course with a placement takes the length of your course to up to two years.
You’ll be gaining knowledge and understanding programming for Python, algorithmic trading, risk management and quantitative methods and optional modules covering areas such as machine learning, forecasting and big data. You will end your Master's with a detailed report, which will allow you to further develop your research skills whilst practically applying the knowledge and skills developed with a dedicated support of an expert supervisor. You will then go on to complete your industrial placement.
A minimum of a second-class UK degree, or an equivalent internationally recognised qualification in areas such as the sciences, engineering, computing, maths, finance and/or business. Quants/programming based background is required.
All applicants are considered on an individual basis and additional qualifications, professional qualifications and relevant experience may also be taken into account when considering applications.
Please see our International Student website for entry requirements by country and other relevant information. Due to visa restrictions, students who require a student visa to study cannot study part-time unless undertaking a distance or blended-learning programme with no on-campus provision.
The University requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree.
For detailed information see our English language requirements web pages.
Applicants who are required to meet an English language condition may be able to study a pre-sessional course in English for Academic Purposes through Kent International Pathways.
Duration: 1 year full-time
This programme is studied over one year full-time and consists of seven compulsory modules and a choice of optional modules in Stage 1 and a research-led project in Stage 2.
Stage 1 aims to provide you with the knowledge and understanding of fintech in contemporary organisations and businesses, including an overview of technologies used in financial services delivery, programming for Python, algorithmic trading, risk management and quantitative methods and optional modules covering areas such as machine learning, forecasting and big data.
Stage 2 consists of a fintech related research project. This allows you to further develop your research skills by carrying out a substantial research project and present the work in the form of a comprehensive written report. whilst practically applying the knowledge and skills developed throughout Stage 1.
The MSc FinTech programme is available with an optional industrial placement, which will require you to complete the Industrial placement Report.
The following modules are indicative of those offered on this programme. This list is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
This module covers key concepts related to financial risk management, especially market risk in financial institutions. It broadly addresses the rationale for practising risk management, followed by approaches to measuring and managing risk.
The course will cover the following indicative topics: taxonomy of Risks, Essential Financial Products, Introduction to Regulation, Modelling Portfolio Risk, Market Risk and VAR, Credit Risk, Risk-Adjusted Performance Measures.
This module will provide students with a core understanding of Financial Technology applications, and specifically how a wide range of disruptive innovations are reshaping the financial system. Particular emphasis will be placed on understanding how banks and other financial institutions can benefit from using these technologies.
The module will cover the following indicative topics: Introduction to FinTech, Payments, cryptocurrencies and blockchain, Digital finance, New forms of lending and crowdfunding, Data and technology in financial services, The role of artificial intelligence and machine learning.
This module will cover the following topics: Investment appraisal techniques and decisions, Stock market efficiency – capital market behaviour, Portfolio theory, The Capital Asset Pricing Model, Sources of finance, Capital Structure.
This module provides a general introduction to the quantitative methods used in financial applications and topics may include: Statistical concepts, Probability distributions, Statistical inference, estimation and hypothesis testing, Correlation, spurious correlation and general dependence measures, Linear regression, Multiple linear regression, Logistic regression, Monte Carlo simulation, Modelling in Excel.
This module will introduce students to Python, a programming language that has become the industry standard. Students will learn how to use Python in order to conduct financial and econometric analysis. Particular emphasis will be placed on programming for specific financial applications such as portfolio optimization, asset valuation, and derivatives pricing.
This module will provide students with a core understanding of algorithmic trading, and specifically how to develop and implement quantitative trading strategies. The module will cover the following indicative topics: High-frequency trading and tick data, Backtesting and automated execution, Mean reversion strategies, Momentum strategies, Arbitrage strategies, Risk management, Performance evaluation.