MSc in Financial Technology and Innovation

In order to make sure that your academic journey along our complex, inter-disciplinary MSc course provides you the suitable learning experience, we provide below some necessary information regarding your induction period, recommended pre-study modules,  and recommended preparatory materials of the course’s core modules.

Timetables

You should use the MyTimetable system to view your timetables. You can access the MyTimetable system here. Important: To help you generate an accurate timetable please read this quick guide on how to use the system first.

You can view a pdf copy of your induction timetable here

Questions?

If you have any questions about the student experience on one of our MSc degrees, who better to ask than one of our current MSc students? Our helpful Online Student Ambassadors will be happy to answer your queries.

Induction period

Your induction is a two-week compulsory period in September, where you will get acquainted with your fellow students, the careers team and course faculty, through various in-person and online sessions. It also involves completion of the final stages of the student registration process. Throughout your induction you will also receive a number of preparatory classes related to your course (see below) as well as a range of careers events. These events will be included in your induction timetable as well as in the careers event timetable.

Pre-study modules for the course

Many tutorial exercises and course work of your MSc course instrumentalize Python (e.g. Advanced Data Analytics) and R (e.g. Fundaments of Data Science) as the mandatory coding environment during your studies at Bayes. Therefore, we highly recommend you enrolling and completing a pre-study class in both Python and R.

How could Python and R help me during my studies at Bayes?

Throughout the MSc course, you are expected to implement analytical models for case exercises / assignment in R and/or in Python. Learning particularly about Python also enhances your competences regarding structuring analytical problems and translating them using interpreted languages, empowering the coder to remain system-independent. Furthermore, basic algorithmisation and coding skills are elementary competences in today’s job market. R is still a broadly used environment for (advanced) financial engineering.

You might have already used Python and/or R during your previous studies, and/or received certificates after completing a relevant, topical online course, so the concepts of the pre-study modules would provide you the possibility to refresh your knowledge. Bayes offers you two alternative learning platforms for Python and for R as described below. It is your individual choice to make use of either, or both learning platforms. These platforms assume no prior programming knowledge on the subject matter, they guide you step-by-step starting from the basics.

1: Brightspace

Brightspace is an online learning platform that hosts the so-called Pre-Study Online Modules of Bayes. Your enrolment to this platform is expected to happen from the last week of July. You will be given access only, if you, at that date, will have paid the deposit. You will be informed per e-mail with instructions on how to sign up.

Enrolment to the online modules and available resources is expected from the beginning of August onwards. The resources will be available for the entire academic year. Please do keep in mind that the so-called “Forum” feature for questions will close at the end of September.

Our Pre-Study Online Modules on Brightspace also offer classes in mathematics and in statistics (Fundamentals of Mathematics, Fundamentals of Statistics, respectively). We recommend you make use of these resources to refresh your knowledge.

If your knowledge on statistics has faded a bit, you might as well work through one of the following books:

  • Spiegelhalter, D. (2019). The art of statistics: learning from data. Penguin UK.
  • Rowntree, D. (2000). Statistics without tears.

Statistics is particularly helpful and/or required for both your data-focused (Data Science in Term1, Advanced Data Analytics in Term2) and and your finance-focused modules (Financial markets and financial intermediation in Term1, Decentralised finance and blockchain applications in Term2).

2: Moodle

Moodle is Bayes’ learning management system that will support your studies through the whole academic year, across all your modules. Therefore, you can only make use of the pre-study modules that are hosted via Moodle if you are holding an unconditional offer for the course and have already logged into the online registration system. You will receive an email from the Admissions Team on how to access the pre-study modules.

Both the R and the Python Programming pre-course on Moodle is available from August onwards and is accessible until the end of the registration weeks. Moodle’s Python Programming pre-course offers several in-house and online synchronous Python tutorials during the induction period, too. Such hands-on tutorials are delivered via learning-by-doing approach, and thus, you are expected to have your very own machine during these tutorials.

What coding skills are you expected to achieve?

Regarding Python, you shall be able to: i) understand and use the built-in Python objects; ii) design and operate control flows in Python; iii) manage Python environments; iv) create data pipelines; v) carry out fundamental operating system tasks with Python, in order to assure the smooth participation during your studies. Needless to say; all the core tutorials of your MSc course, that rely on Python, will always offer you help and assistance.

If you complete the R programming module, you should be able to have a first-hand experience programming skills useful in computational machine learning and computational statistics. At Bayes, some electives in Term3 would as well require R implementations, so it is worth considering taking part in this pre-course. Furthermore, the module provides you the fundaments of a stable environment for data-heavy analytical problems that is built on easily accessible standards, and which is widely adopted and diffused by the industry.

Preparatory materials for the course

Your future lecturers have selected a few readings that provide you with a good, first overview of the subjects that they will teach.

Please do not get alarmed by the extensive list. These are not required readings, nor are these textbooks that will be heavily used in class. The lists cover useful materials for you to prepare for your studies at Bayes. All textbooks and articles are accessible via the university library, once you join Bayes.

Generally speaking, to prepare the best for all core modules of this course, we all recommend you to read (and read) recent topical news and headlines on financial markets, intermediaries, on digital transformation and on regulatory developments covered by the Financial Times, the Economist, or the choice of mainstream topical media you have access to. Please note that once you registered, you can make use of Bayes Library’s free access to the Financial Times and to The Economist. News articles are more light-hearted and should be especially helpful to refresh your topical background.

Financial markets and financial intermediation

The module material leverages on the following textbooks:

  • Casu, B.; Girardone, C. and Molyneux, P (2022), Introduction to Banking. Third Edition (Pearson).
  • Cuthbertson and Nitzsche (2008), Investments. SecondEdition (Wiley).
  • Bodie, Z., Kane, A., and Marcus, A. (2021). Investments. Twelfth Edition (McGraw-Hill)

Additional Textbook

  • Berger, A., Molyneux, P., and Wilson, J. (2022). The Oxford Handbook of Banking. Third Edition.

Movie

  • The Big Short (2015)

Additionally, we created preparatory lectures for you, in order to familiarize yourself with the fundamentals of (corporate) Finance and / or to refresh your studies. We highly recommend you to visit the decks, read the content through to enhance your journey into Finance.

Preparatory Lecture 1 in Finance

Preparatory Lecture 2 in Finance

Preparatory Lecture 3 in Finance

Preparatory Lecture 4 in Finance

Preparatory Lecture 5 in Finance

Preparatory Lecture 6 in Finance

Preparatory Lecture 7 in Finance

Preparatory Lecture 8 in Finance

Preparatory Lecture 9 in Finance

Foundations of Fintech

The module material leverages on the following textbooks:

  • Hill J.(2018) FinTech and the Remaking of Financial Institutions. Elsevier.
  • Jeng L (2022) Open Banking. Oxford University Press.
  • Lucian M and Walker T. (2021) The Handbook of Banking Technology. Wiley.

Foundations of Data Science

This module introduces fundamental tools in statistical modelling and data analysis using R/Python. Topics include probability distributions, linear and logistic regression, penalised regression, and flexible modelling approaches such as generalised linear models and generalised additive models.

The pre-study modules that are offered by Bayes help you to prepare for this module. Additionaly, please see below a few resources to explore in preparation:

  • Wood, S.N. (2015). Core Statistics. Cambridge University Press.
    • An excellent introduction to the foundations of statistical thinking, with clear explanations and practical examples.
  • Jones, E., Harden, S., & Crawley, M.J. (2023). The R Book (3rd ed.). Wiley.
    • A comprehensive guide to data analysis and statistical modelling in R. It covers data manipulation, visualisation, linear and generalised linear models, generalised additive models, mixed-effects models, survival analysis, time series, and multivariate methods.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R (2nd ed.). Springer.
    • A widely used and accessible textbook covering regression, classification, and model selection.
  • Faraway, J.J. (2014). Linear Models with R (2nd ed.). CRC Press.
    • A concise and practical guide to applying linear models in R, including multiple regression, dummy variables, and model diagnostics.

Innovation, entrepreneurship and financial technology

The module material leverages on the following articles and textbooks:

  • Cavalho, J.M.S. and Jonker, J. (2015) Creating a balanced value Proposition: Exploring the advanced business creation model. The Journal of Applied Management and Entrepreneurship, 20(2), 49-64
  • Hayton, J.C., Chandler, G.N. and Detienne, D. (2011) Entrepreneurial opportunity identification and new firm development processes: A comparison of family and non-family new ventures. International Journal of Entrepreneurship and Innovation Management, 13(1), 12-31
  • Burns (2018) New Venture Creation: A Franework for Entrepreneurial Start-ups. Red Globe Press.
  • Tapscott, D., Ticoll, D. and Lowy, A. (2000). Digital capital: Harnessing the power of business webs. Ubiquity.
  • Luftman, J. (2003). Assessing IT/business alignment. Information systems management, 20(4).

Digital money and banking

To prepare for the module, the following articles provide some interesting thoughts and arguments for discussion and debates:

  • Dermine, Jean. 2016. Digital Banking and Market Disruption: a sense of déjà vu? Banque de France’s Financial Stability Review 20, pages 17-24.
  • Prasad, Eswar S. 2021. The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance. Harvard University Press.
  • Velde, François. 2016. Money and payments in the digital age: innovations and challenges. Banque de France’s Financial Stability Review 20, pages 103-112.

Regulatory Compliance, Ethics, Social Values

The module material leverages on the following textbooks:

FinTech: Law and Regulation (2021), Elgar Financial Law and Practice series, edited by Jelena Madir

Fintech: Finance, Technology and Regulation (2023). Ross P. Buckley, Douglas W. Arner, Dirk A. Zetzsche, Cambridge University Press, UK. ISBN13: 9781009078214

FinTech, Artificial Intelligence and the Law, Regulation and Crime Prevention. Alison Lui, Nicholas Ryder (Eds), 2021, Routledge.

Data Analytics for Fintech

The pre-study modules that are offered by Bayes help you to prepare for this module. Additionaly, this module will make use of the following core texts in statistical learning:

  • James, G., Witten, D., Hastie, T., and Tibshirani, R. (2023): An Introduction to Statistical Learning with Applications in Python. Springer.
  • Hastie, T., Tibshirani, R., and Friedman, J.H. (2009): The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.

You may also find it useful to use the website https://www.statlearning.com/ which houses the Introduction to Statistical Learning textbook including all of the codes in both R and Python.

Specific examples of data analytics and machine learning applied to finance and fintech can be found on websites like Hugging Face and Kaggle.

Decentralised finance and blockchain applications

To explore, assess and to comprehend the current status-quo and the future trajectory of the industry, the module content is structured along the following articles, readings:

  • Haber and Stornetta (1991), How to Time-Stamp a Digital Document.
  • Nakamoto (2008), Bitcoin: A Peer-to-Peer Electronic Cash System.
  • The Economist (2015), The Great Chain of Being Sure About Things.
  • UK Government Office for Science (2016), Distributed Ledger Technology: Beyond Blockchain.
  • Goldman Sachs Equity Research (2016), Profiles in Innovation: Blockchain.
  • Yermack (2017), Corporate Governance and Blockchains.
  • Brunnermeier, James & Landau (2019), The Digitalization of Money.
  • Howell, Niessner, and Yermack (2020), Initial Coin Offerings.
  • Schär (2021), Decentralized Finance.

Industry-based research project

This is your final module to close the journey of the academic year, and to reflect on the gained knowledge by conquering your final project. Below some interesting readings to put your final project into a broader, design-oriented context:

  • Requirements engineering helps you understanding, conceptualizing and aligning requirements and capabilities of different stakeholders from business and IT, see: Gordijn, J. and Akkermans, J.M., 2003. Value-based requirements engineering: exploring innovative e-commerce ideas. Requirements engineering, 8, pp.114-134.
  • As your study material discusses AI from different perspectives, read this article to understand the impact of AI on creativity as well.
  • Follow the latest news about social media trends and listen to the opinions of industry experts. In case your final project focuses on your own service idea, monitoring social media can help you understand customer perceptions.
  • Are you considering stepping into the entrepreneurial world? See and listen to Professor Sarasvathy’s thoughts regarding entrepreneurial attitudes: https://www.youtube.com/watch?v=Ruvb_kGAMYw