Advanced Technology for Financial Computing MSc

Overview of the degree

Advanced Technology for Financial Computing MSc is a 12-month long programme.

This is a pioneering programme designed to make you one of tomorrow’s tech leaders in the financial services sector. You will master cutting-edge technology in data, computing, and AI, and enhance it with business acumen to drive innovation in financial systems.

The MSc in Advanced Technology for Financial Computing provides a unique interdisciplinary education that propels you to the forefront of the fintech industry. By integrating expertise from the School of Informatics, University of Edinburgh Business School, and the School of Mathematics, you gain both technical prowess and business acumen crucial for success in this evolving field.

In the first seven months, you will attend classes and engage in individual and group projects to master advanced data, computing, and artificial intelligence methods.

You will also take at least 20 credits of courses from the Business School, ensuring a solid grounding in financial systems, management, and digital transformation.

The final four months will be dedicated to a research project, where you will apply your knowledge to address real-world challenges, and potentially collaborate with industry leaders.

Compulsory Courses

The following courses will be compulsory:

• Financial Networks INFR11283
• Machine Learning in Financial Services INFR11284
• Informatics Project Proposal 20 (IPP20) - 20 credits of coursework in Semester 2.
• MSc Dissertation (Informatics) - 60 credits in the summer.

Information about IPP20 and Dissertation

Optional courses

Between 20 and 40 credits must be chosen from the following Business School courses:

• Digital Innovation Management CMSE11626
• Innovation-driven Entrepreneurship-Data Science, Technology and Innovation CMSE11515
• Digital Business: Competing in the Age of Platforms CMSE11536
• Digital Business: Transforming Organisations in the Digital Era CMSE11537
• Technology Entrepreneurship and Commercialisation CMSE11576

Between 20 and 40 credits must be chosen from the following courses from Informatics, Mathematics, and EPCC:

• Numerical Algorithms for High Performance Computing EPCC11006
• Design and Analysis of Parallel Algorithms EPCC11007
• Algorithmic Game Theory and its Applications INFR11020
• Blockchains and Distributed Ledgers INFR11144
• Text Technologies for Data Science INFR11145
• Case Studies in AI Ethics (CSAI) INFR11206
• Machine Learning Systems INFR11269
• Financial Mathematics MATH10003
• Finance, Risk and Uncertainty MATH11088
• Fundamentals of Optimization MATH11111
• Large Scale Optimization for Data Science MATH11147
• Credit Scoring MATH11148
• Discrete-Time Finance MATH11153

Between 0 and 20 credits can be chosen from these lists:
Level 10 courses in Schedules O and P

Specific course options and requirements are listed in your Degree Programme Table, and are built into the Path programme builder, which you can use to help you put together a set of courses that works for you.

Degree Programme Tables

Path programme builder