FinTech

MScFinancial Technology

Why this course?

Financial Technology (FinTech) is concerned with the use of technology to make financial transactions more efficient. Graduates in this field will have a good understanding of finance, accounting, business information systems and analytic methods.

Our MSc FinTech is jointly provided by the departments of Accounting & Finance and Management Science where relevant expertise resides across all core competencies both in teaching and research. This is a transitional masters degree and we accept students from a variety of background who want a career is this area.

You'll gain a comprehensive skill set using a blended learning approach that combines theory, intensive practice and industrial engagement.

What you’ll study

The course is 12 months full-time, with two semesters of classes followed by an MSc dissertation project during the summer.

The first semester is designed to give you the fundamental understanding of finance and analytical methods within the context of development in FinTech practice and application.

The second semester will extend your core skills, expanding your knowledge of analytics methods, deepening your understanding of information systems and enhancing your understanding of financial markets.

'Becoming an Effective Financial Technology Analyst is a class led by industrialists in partnership with academics concerned with FinTech problems. It lets you experience the practical reality of applying analytical methods in financial technology business. Working with industrialists on real issues presents a variety of challenges. For example, data may of poor quality, decision makers can be vague concerning their requirements or the analytical technique required may not be obvious as well as convincing industrialists of the merits of the methods would all be covered in this class. Your understanding will be reinforced by experience.

The final component of the MSc course will be a FinTech focused summer dissertation project. This can be completed either through a client-based project or a desk-based research project, depending on your interests. You'll submit your dissertation in September to complete your degree requirements.

Facilities

The course will involve big data analytics using the University’s servers. Some of the content will involve interaction with the Bloomberg Trading Simulation Laboratory in the Business School.

Course content

Compulsory classes

Semester 1

Regulations & Technology

This class has two separate components which will be integrated at the end. The first component is an introduction to Python, a key language in Fintech. The second is an introduction to financial regulation and compliance. One goal of the course is to enable students to write simple “smart contracts” in Python.

Principles of Finance

This class provides an introduction to the basic principles of financial decision taking and the theory of finance. It will develop the basic principles of valuation, the nature of risk and uncertainty, and the relationship between risk and returns.

Quantitative Business Analysis

The first part provides an introduction to the basic theory and application of statistical modelling. Topics covered include:

  • data analysis
  • probability theory
  • distributions and moments
  • estimation
  • hypothesis testing

The second part focuses mainly on two areas:

  • regression modelling
  • multivariate analysis

While key background theory will be presented, the emphasis is on the generation and interpretation of output from commercially available software.

Big Data Fundamentals

This class aims to endow students with an understanding of the new challenges posed by the advent for big data, as they refer to its modelling, storage, and access, along with an understanding of the key algorithms and techniques which are embodied in data analytics solutions.

Semester 2

Business Analytics

The class adopts a process-based approach, all discussion follows the logic of the business processes. The class will provide you with conceptual knowledge introduced in the lectures, as well as hands-on experience gained in tutorials using appropriate packages of the various IS categories.

Business Information Systems

The class will build on the fundamental multivariate statistics by developing both visualisation and advanced analysis techniques relevant in the area of big data. The focus will be on application and interpretation of techniques and there will be an investigation of what makes good data. The class will develop both new theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.

Security Analysis

The aims of this class are to develop an appreciation of the investment characteristics of different types of securities, particularly bonds and shares, and to develop an understanding of how such securities are valued. This class will build on the analysis developed in the first semester class, Principles of Finance.

Risk Management for Banks

The class employs some of the basic principles of financial analysis to consider the application of risk analysis. It explores issues relating to risk management in the banking sector, with a particular focus on the regulatory requirements stemming from the Basel Accord.

Semester 1 & 2

Becoming an Effective Technology Analyst

This class is led by industrialists in partnership with academics concerned with FinTech problems. The practical reality of applying analytical methods in financial technology business is often far removed from the classroom. While traditional teaching can alert students to such issues, understanding needs to be reinforced by experience, which is acquired in this module.

Elective classes

Choose one Accounting & Finance class from:

Portfolio Theory & Management

The aim of this class is to examine the Markowitz (1952) approach to optimal portfolio selection. The class explores issues relating to optimal portfolio choice and issues in practical fund management. This would be useful for those who wish to specialise in Roboadivsors.

Derivatives

This class will provide you with a strong grounding in derivatives that may be used to manage the financial risks faced by individuals, financial institutions and business corporations. This would be useful for those who wish to focus on FinTech for market instruments.

Financial Management for Banks

The class aims to provide you with the knowledge and understanding required for managing the financial aspects of a bank’s business. This would be useful for those who wish to focus on Open banking and Disruptor Banks.

Choose one Management Science class from:

Stochastic Modelling for Analytics

This class introduces fundamental stochastic methods and techniques and then provides applications to large scale modelling. These methods are crucial for a better understanding of uncertainty and risk and these approaches are closely related to many important fields from forecasting to simulation.

Business Simulation Modelling

The class will focus on the main two forms of business simulation:

  • discrete-event simulation (DES)
  • system dynamics (a continuous simulation technique)

For DES, the class will start with an introduction, aiming to familiarise students with the concept and its use. For system dynamics the class will provide a background to system dynamics including its links to other modelling techniques.

Risk Analysis & Management

This class will explore the entire process of structuring a risk problem, modelling it, supporting and communicating recommendations, both theoretically and in practice.

Choose one Computer Science class from:

Big Data Tools & Techniques

This class will continue the focus on machine learning and data mining. It will also provide an introduction to data visualisation and big data platforms.

Fundamentals of Machine Learning for Data Analytics

To aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of basic approaches, along with the knowledge of how and when to apply the techniques.

Learning & teaching

The programme is mainly taught through a combination of lectures, tutorials and computer labs.

"Becoming an Effective Financial Technology Analyst" focuses on experiential learning. A series of semi- or unstructured business problems, typically introduced by an industrialist from the Financial Technology sector, acting as clients, outlining real problems on projects & cases that they have been involved in. Students are formed into small working groups and placed in the role of analysts/consultants with a brief of advising the client on how to tackle the problem.

Assessment

Assessment will vary across the classes but will include exams, written assignments as well as practical team and individual projects.

Entry requirements

Pre-Masters preparation course

The Pre-Masters Programme is a preparation course for international students (non EU/UK) who do not meet the entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.

To find out more about the courses and opportunities on offer visit isc.strath.ac.uk or call today on +44 (0) 1273 339333 and discuss your education future. You can also complete the online application form. To ask a question please fill in the enquiry form and talk to one of our multi-lingual Student Enrolment Advisers today.

Fees & funding

How much will my course cost?

All fees quoted are for full-time courses and per academic year unless stated otherwise.

Scotland/EU

  • 2017/18 - £12,000

Rest of UK

  • 2017/18 - £12,000

International

  • 2017/18 - £19,500

How can I fund my course?

Check our Scholarship Search for more help with fees and funding.

Scottish and non-UK EU postgraduate students

Scottish and non-UK EU postgraduate students starting in 2017 may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students a living cost loan. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

Students coming from England

Students ordinarily resident in England can apply for Postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

Students coming from Wales

Postgraduate students starting in 2017 who are ordinarily resident in Wales can apply for support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. We are waiting on further information being released about this support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

Students coming from Northern Ireland

Postgraduate students starting in 2017 who are ordinarily resident in Northern Ireland can apply for support from Student Finance NI. The support is a tuition fee loan of up to £5,500. We are waiting on further information being released about this support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

International students

We have a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.

Please note

The fees shown are annual and may be subject to an increase each year. Find out more about fees.

Careers

The course is tailored to make the graduates eminently employable in this exciting new field that cuts across traditional disciplinary boundaries.

Research indicates that students with programming, data analytics and finance skills get premium starting salaries compared to graduates from any one of these disciplines.

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Qualification: MSc, Start date: Sep 2017, Mode of delivery: attendance, full-time

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Qualification: MSc, Start date: Sep 2018, Mode of delivery: attendance, full-time

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