Financial Risk Management MSc


This MSc programme, which has been designed in conjunction with leading risk professionals, aims to meet the growing demand for professionals who are highly skilled in quantitative risk management. Students gain core competencies in risk analysis and have the opportunity to tailor the programme to their own interests and needs through the wide variety of options available.


Mode of study

  • Full-time 1 year

Tuition fees

  • UK/EU Full-time: £10,450
  • Overseas Full-time: £21,700

Application date

  • All applicants: 30 June 2014

More details in Application section.


What will I learn?

Students will be educated to a high level in programming and computing and will gain mathematical, statistical and computational modelling skills. They will have a clear appreciation of different types of risk within the industry, and of the managerial and psychological issues related to risk control.

Why should I study this degree at UCL?

There is major interest in the Bank of England, the Financial Services Authority and the Financial Services Industry to raise the level of quantitative analytics used in risk management and compliance. UCL, in collaboration with the BoE/FSA, aims set a new benchmark in this area, based on turning out risk professionals who are good scientists in the area of risk management.

UCL Computer Science is recognised as a world leader in teaching and research. Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.


Students undertake modules to the value of 180 credits. The programme consists of four core modules (60 credits), four options (60 credits) and the research dissertation (60 credits).

Core Modules

  • Stochastic Methods in Finance
  • Compliance Risk and Regulation
  • Market Risk, Measures and Portfolio Theory
  • Financial Data and Statistics

Options

  • Option modules are expected to include the following:
  • Introductory Programming
  • Financial Institutions and Markets
  • Quantitative and Computational Finance
  • Numerical Analysis for Finance
  • Advanced Stochastic Analysis and Models
  • Equities, Foreign Exchange and Commodities Modelling
  • Programming and Mathematical Methods for Machine Learning
  • Interest Rates and Credit Modelling
  • Applied Computational Finance in C++/Mathematica
  • Statistical Inference
  • Supervised Learning
  • Complex Networks and Web
  • Information Retrieval and Data Mining
  • Financial Information Systems

Dissertation/report

Students undertake a summer work placement in an industry environment. The modelling, research and data analysis which takes place over the summer placement forms the basis of the 10,000-word dissertation.

Teaching and Learning

The programme is delivered through a combination of lectures, seminars, tutorials and project work. Modules are assessed by written papers and/or coursework. The research project is assessed by a written report and (optional) oral examination.

Further details available on subject website:


Funding

We are offering four MSc Excellence Scholarships worth £4,000 to UK/EU offer holders with a record of excellent academic achievement. Please note that the closing date for applying for this is 30 June 2014.

Scholarships available for this department

Brown Family Bursary

This award is based on financial need.

Further information about funding and scholarships can be found on the Scholarships and funding website.


Entry requirements

A minimum of an upper second class UK Bachelor's degree, in a relevant discipline, or an overseas qualification of an equivalent standard, with a strong quantitative component evidenced by good performance (>60%) in relevant mathematics, statistics or computation options.

International equivalencies

Select your country for equivalent alternative requirements

English language proficiency level: Good

How to apply

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

The deadline for applications is 30 June 2014.

Who can apply?

The programme is aimed at students with a first degree in mathematics, finance, economics, physics or computing who wish to gain the skills necessary to work within quantitative risk management. Candidates will be expected to have established competency in probability, statistics, differential equations and the use of a computer to solve numerical problems.

What are we looking for?

When we assess your application we would like to learn:

  • why you want to study Financial Risk Management at graduate level
  • why you want to study Financial Risk Management at UCL
  • what particularly attracts you to this programme
  • how your academic and professional background meets the demands of this programme
  • what programming experience you have
  • where you would like to go professionally with your degree
Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.


Career

The first cohort of students on the Financial Risk Management MSc is due to graduate in 2013, therefore no specific career destinations are currently available. Recent graduates within the department of Computer Science have gained positions with IT companies such as Google and IBM. Others have entered the financial sector or set up new businesses e.g. Satalia, or pursued doctoral study.

Employability

Students acquire mathematical, statistical and computational skills which are highly sought after by the financial industry to assess, quantify, model, simulate and edge risk.


Next steps

Contact

Ms Julija Melesko

T: +44 (0)20 7679 1373

Department

Computer Science

Register your interest

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Make an application

APPLY HERE


Prospectus subject

Computer Science

Faculty overview

Engineering Sciences


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