Advanced Technology for Financial Computing MSc
Be at the forefront of technological change in the financial sector.
At a glance
A UK 2:1 honours degree, or its international equivalent, in informatics, artificial intelligence, cognitive science, computer science, electrical engineering, linguistics, mathematics, physics, or another numerate degree.
Competence in programming is essential.
Applications now closed for September 2021 entry
Our new innovative MSc in Advanced Technologies for Financial Computing will provide you with a critical and practical appreciation of how data, computing and artificial intelligence technologies can be used and developed to deliver value in organisations with finance, risk and decision-making related digitalisation from both technology and business perspectives.
The programme offers an interdisciplinary approach with a focus on advanced technologies such as natural language processing and text technologies, machine learning and deep learning, data science, blockchain and distributed ledgers provided by the world-renowned research expertise of the School of Informatics. A strong collaboration with the School of Mathematics adds in-depth quantitative modelling and optimisation knowledge. Edinburgh Business School contributes insights into digital business skills with an emphasis on finance elements.
Edinburgh is Scotland's financial capital and a vibrant Festival City. It is ranked 17 in the Global Financial Centres Index 2020, 2 in the UK after London. With the backing of the UK and Scottish governments for Data-Driven Innovation through the Edinburgh and South East Scotland City Region Deal in 2017 it is set to become Europe's Data capital.
The University of Edinburgh is consistently ranked as one of the top 50 universities in the world. It's ranked 20th in the 2020 QS World University rankings, 23rd in the Computer Science subject ranking, 22nd in Times Higher Education subject ranking for Computer Science.
The University hosts the Blockchain Lab led by Professor Aggelos Kiayias which aims at studying all aspects of distributed ledger technology.
The University of Edinburgh is also home to the Bayes Centre innovation hub for Data Science and Artificial Intelligence.
School of Informatics is a world leader in research areas such as natural language processing. Edinburgh has a long tradition of work in Artificial Intelligence, going back to the 1960s when it was one of the few centres in the world working on AI and its applications.
Facts and numbers
#1 - Edinburgh is ranked best NLP research centre in the world by CSRankings
#2 - Edinburgh is ranked 2nd in the UK and 17th in the world in the Global Financial Centres Index
#20 - the University of Edinburgh is ranked 20th in the QS World Ranking 2020
#22 - the School of Informatics is ranked 22nd in Times Higher Education World Subject Ranking for Computer Science 2021
#23 - the School of Informatics is ranked 23rd in QS World Ranking for Computer Science 2020
55 - Artificial Intelligence as a subject has been studied in Edinburgh for 55 years (the Experimental Programming Unit was established in 1965)
Graduates will be equipped with skills to pursue careers as – to name a few - quant developer, financial system architect, application engineer, financial system consultant, software developer, data scientist or financial system engineer.
IT firms, banks, investment and finance companies, as well as government, public sector and academia, are all likely to seek our graduates. Tencent, Fintech Scotland and RBS are among potential employers.
By and large, the School of Informatics graduates have excellent career prospects. Popular employers include Google, Facebook, Amazon, IBM, SkyScanner, Intel, ARM, Samsung, NVIDIA, Keysight, RockStar North as well as RBS, JP Morgans, Citigroup.
This MSc consists of approximately seven months of taught courses across two semesters and up to four months of project work. Students are required to take Data-driven Business and Behaviour Analytics and Introductory Applied Machine Learning, and choose from a range of optional courses offered by Informatics, Maths and Business Schools listed below. Please note, new courses from Maths and Business Schools have been added for the 2021 start.
Electives can be chosen from courses offered by Informatics, Maths and Business Schools: Introduction to Risk Management in Banks, Credit Risk Management, Digital Business, Text Technologies for Data Science, Blockchains and Distributed Ledgers, Natural Computing, Fundamentals of Optimization, Algorithmic Game Theory and its Applications, Distributed Systems, Artificial Intelligence, Present and Future, Large Scale Optimization for Data Science.
|Dr Rik Sarkar||
Informatics Research Review
Research Interests: data mining, algorithms, network science, location data and privacy, sensor networks and IoT.
|Dr Aurora Constantin||
Informatics Project Proposal
Research interests: human-computer interaction, digital learning, multimodal interaction, assistive technologies, educational technology, technology for autism, reference models.
|Dr Richard Mayr||
Research interests: automated verification, automata theory, temporal logic, model-checking and semantic equivalence checking, formal verification of real-time and probabilistic systems, infinite-state Markov chains, Markov decision processes and stochastic games.
|Dr Valerio Restocchi||
Data-driven Business and Behaviour Analytics
Research interests: socio-technological system simulation, algorithms and computational methods, visualisation and data handling software, programming languages and libraries
|Dr Tiejun Ma||
Introductory Applied Machine Learning
Research interests: risk analysis and decision-making using quantitative modelling and real-time Big data analysis techniques applies to fintech, cyber-risk, and resilience
|Dr Galina Andreeva||
Credit Risk Management
Research Interests: alternative finance, fintech and improving access to credit, new types of information in credit risk assessment, psychology of credit behaviour, financial education and its effect on credit behaviour, legislative environment of credit scoring models, discrimination in credit, national and international differences in risk profiles, behavioural finance, profit scoring
|Dr Yizhe Dong||
Introduction to Risk Management in Banks
Research interests: banking and financial institutions, corporate finance and governance, ESG investing, climate and green finance, credit risk assessment, alternative finance and Fintech, equity valuation and efficiency and productive analysis
Research interests: digital platforms, inter-organisation relationship between tech start-ups and incumbent firms in traditional sectors (e.g., finance, automotive), financial innovation
|Dr Kousha Etessami||
Algorithmic Game Theory and its Applications
Research interests: automated verification, logic, algorithms and computational complexity theory, algorithmic game theory, equilibrium computation, analysis of probabilistic systems, Markov decision processes, stochastic games, automata theory, model checking, analysis of infinite-state systems, finite model theory and descriptive complexity.
|Prof Aggelos Kiayias||
Blockchains and Distributed Ledger
Research interests: computer security, information security, applied cryptography and foundations of cryptography with a particular emphasis in blockchain technologies and distributed systems, e-voting and secure multiparty protocols as well as privacy and identity management
|Dr Walid Magdy||
Text Technologies for Data Science
Research interests: computational social science, data science, social media, data mining and Arabic NLP
|Dr Michael Herrmann||
Research interests: autonomous robots, robot swarms, biorobotics, prosthetics, computational neuroscience, self-organised criticality, neural avalanches, neural fields, self-organising maps, metaheuristic optimisation, cognitive psychology, biomedical data processing
|Prof Alper Yildirim||
Fundamentals of Optimization
Research interests: mathematical optimization, continuous optimization, convex and nonconvex optimization, algorithm design, analysis and implementation, optimization in real-life applications, operational research
|Prof Jacek Gondzio||
Large Scale Optimization for Data Science
Research interests: theory and implementation of optimization methods for linear, quadratic and nonlinear programming, use of linear algebra techniques and sparse matrix factorisation methods applied in optimization, use of parallel and distributed computing for solving real-life very large optimization problems arising in telecommunications, energy sector and finance