MSc Advanced Technology for Financial Computing

1 Year On Campus Masters Program

University of Edinburgh

Program Overview

This one-year full-time MSc is designed to equip students with technical and business skills at the intersection of finance, data science, and computing. It suits candidates with strong numeric or computing backgrounds who wish to become leaders in financial technology, risk, or data-driven decision-making.

Curriculum Structure

The first part of the programme (approximately seven months) involves core compulsory modules including Financial Networks, Machine Learning in Financial Services, Data-driven Business and Behaviour Analytics, Informatics Research Review, and Informatics Project Proposal. Students also take business-school courses to gain insight into finance, risk, and digital transformation.
The final part (about four months) is devoted to a substantial MSc Dissertation, where students apply their learning to solve real-world problems in fintech, financial risk, or financial computing systems under supervision

Focus areas: financial computing • machine learning in finance • business analytics • risk & uncertainty • algorithm design • blockchains & distributed ledgers • optimization • business-technology integration •

Learning outcomes: ability to design and analyse fintech systems • apply advanced machine learning and data analytics in finance • understand risk and financial business models • integrate technical and business competencies • execute an independent research or design project •

Professional alignment (accreditation): The programme is interdisciplinary and draws from excellence in Edinburgh’s Informatics, Mathematics, and Business Schools; while not a regulated engineering or accounting qualification, it is highly regarded in industry and finance tech.

Reputation (employability rankings): University of Edinburgh is consistently ranked among the top universities globally for Computer Science and Informatics; graduates from this programme are expected to secure roles in financial institutions, fintech companies, or quantitative / technology-intensive consultancies.

Experiential Learning (Research, Projects, Internships etc.)

Students in this MSc programme are given substantial opportunities to put theory into practice through group and individual projects, real-world finance-tech case studies, and a significant dissertation. They benefit from interdisciplinary work with the Business School, Informatics, and Mathematics, drawing on tools and support from research centres and industry partners.

Here are some concrete examples of the practical / applied parts of the programme:

Projects at the Bayes Centre allow students to work on external partner problems: companies, charities or government bodies propose real-world challenges which students can take on as MSc-level research or design projects.
The programme includes an Informatics Project Proposal (IPP20) module which involves coursework to define a project (often data-driven or AI/ML based) that students will carry forward to the dissertation stage.
The MSc Dissertation (60 credits) is a key experiential component: students carry out independent work over the summer applying advanced technology, computing, and AI methods to financial computing tasks or problems.
Optional modules allow students to learn high-performance computing or risk / credit scoring, blockchains, distributed ledgers etc., often involving labs or computational toolsets suitable for handling

Progression & Future Opportunities

Graduates progress into roles such as Quant Developer, Data Scientist, Financial Systems Architect, or FinTech Consultant. The blend of computing, AI, and finance equips them for fast-growing careers in financial services and technology.

Career support: Dedicated Informatics Career Consultants, internship links, and employer networking.
Employment & salary: Median salary ~£27,500 within 15 months, rising with experience.
Industry links: Graduates recruited by UBS, NatWest, Huatai Securities, and fintech firms.
Accreditation value: Highly regarded for cross-disciplinary expertise in finance and computing.
Outcomes: Strong employability in finance, risk analytics, and technology-driven roles.

Further Academic Progression: Students may continue to PhD studies in areas such as Machine Learning, Quantitative Finance, or Distributed Computing.

Additional Information & Requirements

A UK 2:1 honours degree, (60-69%), in informatics, artificial intelligence, cognitive science, computer science, electrical engineering, linguistics, mathematics, philosophy, physics or psychology.
Competence in programming is essential. During your degree you must have completed a programming course in at least one of the following: C/C++, Java, Python, R, Matlab, Haskell, ML.
USA- You should have an undergraduate degree from a recognised university or college with a minimum 3.25 GPA.
India -The minimum score you should have achieved is 60% or more / GPA of 8 on a 10-point scale in your Bachelor's study. If you are studying at university listed on the UoE Indian Universities List you should achieve 55% or more / GPA of 7.0 on a 10-point scale.
IELTS Academic: total 7.0 with at least 6.5 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
TOEFL-iBT (including Home Edition): total 100 with at least 23 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.