Postgraduate Course

MSc Financial Technology

(Computer Science)

How to apply

Overview

The details

Course: Financial Technology (Computer Science)

Start date: October 2025

Study mode: Full-time

Duration: 1 year

Location: Colchester Campus

Based in: Computer Science and Electronic Engineering (School of)

To be competitive in our modern financial industry, it is not enough for a scientist to know how to write software, nor is it enough that one can mathematically analyse markets based on simplifying assumption by ignoring the computation required.

Our MSc Financial Technology (Computer Science) course give you the understanding of the specifics and the intricacies of financial and economics markets, the ability to apply advanced computing methods and the ability to design and program solutions that successfully exploit the synergy between finance, economics, and computation.

Our course will cover areas including:

financial markets
big data analytics
blockchain
AI
python programming

Our course will give you both the theoretical and technical skills that are specific for the finance industry, as well as transferable skills such as the ability to develop and present an argument, and the ability to work independently and in groups.

The MSc Financial Technology suite of courses has three unique variants for you to explore so you can tailor your Masters in financial technology to suit your interests and goals. In addition to our MSc Financial Technology (Computer Science) course, there is also the option of MSc Financial Technology (Finance) and MSc Financial Technology (Economics), giving you the chance to specialise in your preferred area of study.

You will work with coleagues from our Centre for Computational Finance and Economic Agents (CCFEA), which is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation. Postgraduate study within CCFEA will give you rigorous training in the principles of quantitative finance and microeconomics along with computational skills.

Why we're great.

Essex has excellent research and teaching in computer science, economics, and business – this course is an interdisciplinary degree benefiting from the expertise in three departments.
Study a variety of topics including microeconomics and big data, software development and the underpinnings of the financial system.
Benefit from our proximity to London as well as our connections with employers.

Our expert staff

Our course is taught in collaboration with the Centre for Computational Finance and Economic Agents (CCFEA) within the School of Computer Science and Electronic Engineering, the Essex Finance Centre (EFiC) within Essex Business School, and the Department of Economics, all of which have an international reputation.

We bring together experts with both academic and industrial expertise in the financial and IT sectors. Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management, among others.

Key academic members of staff you are likely to have contact with:

Michael Kampouridis is a Senior Lecturer in Computational Finance at Essex. His main research activities are in the use of machine learning algorithms for solving real-world problems from the fields of economics and finance. He has done extensive work in algorithmic trading and financial forecasting, and also in weather derivatives.
Panagiotis Kanellopoulos is a Lecturer in Computational Finance at Essex. His research mainly focuses on algorithmic game theory, an area in the intersection of economics (game theory) and computer science (analysis of algorithms) that aims to understand and design algorithms in strategic environments, such as auctions and markets.
Dr Maria Kyropoulou is a Senior Lecturer at the School of Computer Science and Electronic Engineering, University of Essex. Her research interests lie in the areas of algorithmic game theory, algorithmic mechanism design, blockchain, and the design and analysis of algorithms. She is also the academic supervisor in a Knowledge Transfer Partnership (funded by InnovateUK) with a London-based law firm.
Alexandros Voudouris is a Lecturer at the School of Computer Science and Electronic Engineering. His research interests lie at the intersection of theoretical computer science, artificial intelligence, and microeconomic theory. In particular, he works on problems related to algorithmic game theory, algorithmic mechanism design, computational social choice, and fair resource allocation.
Dr Themistoklis Melissourgos is a Lecturer at the School of Computer Science and Electronic Engineering. His research interests mainly revolve around Algorithmic Game Theory. He also enjoys computational social choice and the intersection of theoretical computer science and economics.

Specialist facilities

You will acquire practical and "hands-on" experience in our labs by using:

Matlab to implement quantitative methods in finance and economics, and their application to investment, risk management and trading
Python to model and develop machine learning algorithms with emphasis on the financial industry
Optimisation Solvers (such as Gurobi) in financial optimisation problems

Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.

We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
All computers run either Windows 10 or are dual boot with Linux
Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)

Your future

Our MSc Financial Technology (Computer Science) course will equip you with the skillset at the intersection of computer science, economics and finance. You'll gain expertise that is in high demand in the finance industry in the UK, and will become competitive in one of the primary recruiting sectors in the country and beyond.

With a skillset that combines both technical and analytical know-how from internationally known hubs within our University, our graduates will be able to find employment in very competitive financial institutions.

We also offer a range of postgraduate research degrees (such as a PhD) in areas of computer science and electronic engineering, and computational finance.

Entry requirements

UK entry requirements

A 2:2 degree in one of the following subjects (with no module requirements):

Computer Science
Economics
Engineering
Finance
Maths
Physics
Statistics

We will consider applicants with any other 2:2 degree or above which includes:

At least one Maths or Econometrics module, such as:

Calculus
Differential Equations
Econometrics
Probability
Statistics
Stochastic Processes

International & EU entry requirements

We accept a wide range of qualifications from applicants studying in the EU and other countries. Get in touch with any questions you may have about the qualifications we accept. Remember to tell us about the qualifications you have already completed or are currently taking.

Sorry, the entry requirements for the country that you have selected are not available here. Please contact our Graduate Admissions team at pgquery@essex.ac.uk to request the entry requirements for this country.

English language requirements

If English is not your first language, we require IELTS 6.0 overall with a minimum component score of 5.5.

If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.

Additional Notes

The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.

Structure

Course structure

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The following modules are based on the current course structure and may change in response to new curriculum developments and innovation.

We understand that deciding where and what to study is a very important decision for you. We'll make all reasonable efforts to provide you with the courses, services and facilities as described on our website and in line with your contract with us. However, if we need to make material changes, for example due to significant disruption, we'll let our applicants and students know as soon as possible.

Components and modules explained

Components

Components are the blocks of study that make up your course. A component may have a set module which you must study, or a number of modules from which you can choose.

Each component has a status and carries a certain number of credits towards your qualification.

Status	What this means
Core	You must take the set module for this component and you must pass. No failure can be permitted.
Core with Options	You can choose which module to study from the available options for this component but you must pass. No failure can be permitted.
Compulsory	You must take the set module for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Compulsory with Options	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Optional	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.

The modules that are available for you to choose for each component will depend on several factors, including which modules you have chosen for other components, which modules you have completed in previous years of your course, and which term the module is taught in.

Modules

Modules are the individual units of study for your course. Each module has its own set of learning outcomes and assessment criteria and also carries a certain number of credits.

In most cases you will study one module per component, but in some cases you may need to study more than one module. For example, a 30-credit component may comprise of either one 30-credit module, or two 15-credit modules, depending on the options available.

Modules may be taught at different times of the year and by a different department or school to the one your course is primarily based in. You can find this information from the module code. For example, the module code HR100-4-FY means:

100

The department or school the module will be taught by.

In this example, the module would be taught by the Department of History.

The module number.

The UK academic level of the module.

A standard undergraduate course will comprise of level 4, 5 and 6 modules - increasing as you progress through the course.

A standard postgraduate taught course will comprise of level 7 modules.

A postgraduate research degree is a level 8 qualification.

The term the module will be taught in.

AU: Autumn term
SP: Spring term
SU: Summer term
FY: Full year
AP: Autumn and Spring terms
PS: Spring and Summer terms
AS: Autumn and Summer terms

COMPONENT 01: CORE

CCFEA MSc Dissertation

(40 CREDITS)

This dissertation is worth% and is submitted to FASer and the school in week 48. The presentation is worth 10% and takes place in weeks 49/50.

View CCFEA MSc Dissertation on our Module Directory

COMPONENT 02: COMPULSORY

Computational Market Microstructure for FinTech and the Digital Economy

(20 CREDITS)

Equip yourself with principles of allocation and mechanism design from an operational perspective. Auction design and market microstructure of the stock market, liquidity provision in electronic financial markets such as dark pools, and capital adequacy of centralized clearing platforms are some of the specific applications that will be studied in the first part of this module. During the second part, you will be introduced to complexity economics of self-organisation, network modules, and strategic proteanism. Finally, you'll use network models to study economic interactions.

View Computational Market Microstructure for FinTech and the Digital Economy on our Module Directory

COMPONENT 03: COMPULSORY

Introduction to Programming in Python

(20 CREDITS)

The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them.

View Introduction to Programming in Python on our Module Directory

COMPONENT 04: COMPULSORY

Big Data in Finance

(20 CREDITS)

Big data - where datasets are so large they cannot be processed using traditional techniques – is useful to financial organisations. This module explores how to analyse big data and covers areas such as predictive analytics, risk modelling and corporate finance. You also learn about the application of data analytics in high frequency finance, fraud and personal finance.

View Big Data in Finance on our Module Directory

COMPONENT 05: COMPULSORY

Machine Learning for Finance

(20 CREDITS)

This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.

View Machine Learning for Finance on our Module Directory

COMPONENT 06: COMPULSORY WITH OPTIONS

(20 CREDITS)

COMPONENT 07: OPTIONAL

(40 CREDITS)

Teaching

Lectures introduce you to cutting-edge computational and evolutionary methods to analyse and simulate markets and to design real-time trading and risk management systems.
Lab sessions allow you to gain experience in implementing quantitative methods in finance and economics, modelling and developing machine learning algorithms as well as optimisation algorithms in financial settings.
Industry expert lectures give you a unique opportunity to appreciate the latest developments and issues faced by leading practitioners in the areas of quantitative finance and risk management.

Assessment

You will be assessed throughout the course by means of written exams and coursework, including the dissertation towards the end of the course. You will also be asked to give presentations on your own work.

Dissertation

In order to complete your dissertation, you will consider and address a pertinent question in the field of financial technology. You'll make use of the skills you've acquired during the course in order to tackle an important topic of practical importance. The dissertation is aimed to be of publishable quality.

Fees and funding

Home/UK fee

£12,125

International fee

£23,500

Masters fees and funding information

Research (e.g. PhD) fees and funding information

What's next

We hold Open Days for all our applicants throughout the year. Our Colchester Campus events are a great way to find out more about studying at Essex, and give you the chance to:

tour our campus and accommodation
find out answers to your questions about our courses, student finance, graduate employability, student support and more
meet our students and staff

If the dates of our organised events aren’t suitable for you, feel free to get in touch by emailing tours@essex.ac.uk and we’ll arrange an individual campus tour for you.

2025 Open Days (Colchester Campus)

Wednesday 19 March 2025 - Postgraduate Open Evening

Find out more

You can apply for this postgraduate course online. Before you apply, please check our information about necessary documents that we'll ask you to provide as part of your application.

We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.

For information on our deadline to apply for this course, please see our ‘how to apply' information.

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Set within 200 acres of award-winning parkland - Wivenhoe Park and located two miles from the historic city centre of Colchester – England's oldest recorded development. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.

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If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tour allows you to explore the Colchester Campus from the comfort of your home. Check out our accommodation options, facilities and social spaces.

At Essex we pride ourselves on being a welcoming and inclusive student community. We offer a wide range of support to individuals and groups of student members who may have specific requirements, interests or responsibilities.

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The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include, but are not limited to: strikes, other industrial action, staff illness, severe weather, fire, civil commotion, riot, invasion, terrorist attack or threat of terrorist attack (whether declared or not), natural disaster, restrictions imposed by government or public authorities, epidemic or pandemic disease, failure of public utilities or transport systems or the withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications. The University would inform and engage with you if your course was to be discontinued, and would provide you with options, where appropriate, in line with our Compensation and Refund Policy.

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