MSc Business Analytics and Artificial Intelligence

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £20,000
  International, including EU, students (per annum): £35,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We know that student finance can be complicated. The links below provide further information to help guide you.

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Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.

The course covers various topics at the intersection of data science and FinTech including the evolution of FinTech, banks’ data generation processes, survey of various data science models relevant in banking industry, and practical considerations while applying the analytical tools and techniques students learn through other modules or previous studies. While there are no particular prerequisites for this elective, students are expected to be proactive and keen to enrich their knowledge on data analytics where needed. Relevant resources will be provided to guide students through the learning process where necessary.

The aim of this course is to provide students with an understanding of data science in practice with specific focus on applications in banking and FinTech. Broadly the course will expose students to the data generation processes as well as prevalent data science models applicable in various banking functions and products including financial performance analytics, customer analytics, risk analytics, robo-advising, and text analytics. It will discuss several data science models that help solving business problems imperative for incumbent banks as well as FinTech startups. Key focus will be on helping students envision various real life scenarios in which they can apply analytical and quantitative as well as computational skills they gain through our course modules in the MSc Business Analytics, MSc in Data Science, or MSc in Finance programs. The course will also include discussion of relevant case studies and some hands-on exercises using software tool, R or Python.

At the end of the course unit, student should be able to:

• Understand the fundamentals of data generation processes and digitization imperatives for banking and finance,
• Understand a variety of data science models relevant to banking industry, such as churn model (customers likely to leave the bank), underwriting model (predicting the likelihood of default), next best product model (likelihood of buying a financial product), etc.,
• Discuss the role of reporting and visualization in influencing decision making in banks and FinTech firms,
• Demonstrate the ability to inscribe their expertise in AI and data science into financial data analytics and prescribe insights for decision making in banking industry based on learnings from case studies.
• Improve teamwork and collaboration skills from group project.

50% Group Project Coursework (40% group report; 10% anonymous peer-assessment by the group members)
50% End Term Exam 
Voluntary weekly self-assessment quizzes (not assessed)

Written, verbally during class and via Blackboard.

Reference book:

• Boobier, T. (2020). AI and the Future of Banking (1st edition). Wiley. (TB)

Case Studies:

• As indicated in the Syllabus.

Journal articles:

• Alfaro, E., Bressan, M., Girardin, F., Murillo, J., Someh, I., & Wixom, B. (2019). BBVA’s Data Monetization Journey. MIS Quarterly Executive, 18(2).
• Fogarty, D., & Bell, P. C. (2014). Should You Outsource Analytics? MIT Sloan Management Review, 55(2), 41–45.
• Ge, R., Feng, J., Gu, B., & Zhang, P. (2017). Predicting and Deterring Default with Social Media Information in Peer-to-Peer Lending. Journal of Management Information Systems, 34(2), 401–424.
• Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265.
• Joshi, M.P., Su, N., Austin, R.D., & Sundaram, A.K. (2021), Why So Many Data Science Projects Fail to Deliver, MIT Sloan Management Review,  62(3).
• Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018). Robo-Advisory—Digitalization and Automation of Financial Advisory. Business & Information Systems Engineering, 60(1), 81–86.
• Wang, Q., & Huang, K.-W. (2018). Exploring the FinTech Jobs-Skills Fit of Financial and Information Technology Professionals: Evidence from LinkedIn. ICIS 2018 Proceedings.

Note: The recommended reading mater