Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £12,850
Overseas: £33,500
EU/EEA/Swiss students
What you'll study
Semester One
In the first semester, you will focus on the compulsory modules. These will provide you with the necessary foundations to succeed in a risk related career. We will cover fundamental risk analytics tools, statistics, data analytics, and machine learning. We will also introduce you to key risk management theories in Enterprise Risk Management and Finance.
Semester Two
In the second semester, you will choose all your modules and tailor the programme to your interests, career plans or professional background. Our elective modules give you the opportunity to specialise in specific areas of risk management, such as:
- Enterprise risk analytics (with Actuarial Risk Management 2)
- Financial risk analytics (with Digital and Real Asset Analytics and Financial Data Analytics)
- Sustainability and Climate Risk Analytics
- Regulatory Risk Analytics.
Semester Three
In the third semester, you will work on your dissertation. This is a unique opportunity to be actively involved in risk-related projects. Working alongside a researcher, you will develop the hands-on skills required to work in various industries, government agencies, or academic institutions. You will also be well positioned to pursue a PhD programme in Risk Analytics or a related field.
Support for those with a non-mathematics background
We have two modules, Foundations of Mathematics and Statistics module, and Data Analytics , both specifically designed to support students from non-mathematical background. These modules provide the essential mathematical, statistical, and computational foundations necessary to confidently engage with more advanced Risk Analytics topics.
Additionally, we offer a wide range of elective modules, allowing students to tailor their learning experience to their interests and career goals. These electives provide opportunities to deepen knowledge in areas such as financial risk, climate risk, and regulatory analytics, ensuring that students can develop expertise in their chosen field.
Structure
- Four compulsory modules
- Four elective modules (chosen from 7)
- Final project and dissertation
Compulsory/Core modules
This module will start by providing an understanding of actuarial advice and how it can be used to meet the needs of stakeholders in both public and private institutions. The module will provide a deep understanding of the actuarial control cycles with their applications. We will also study risk governance, risk identification and classification, risk measurement and responses to risk. We consider scenario analysis, stress-testing and stochastic modeling in the evaluation of risk. The module will focus on capital management and monitoring and it will end with an overview of the general business environment.
This module introduces students to analytical tools used in risk management. After an introduction of basic probability theory and statistics used in physical and life sciences and economics, you will get an overview of statistical models used in risk modelling. You will learn applications of stochastic processes to finance and loss distribution models to liability valuation. This module includes real-world data applications using R.
Each Risk Analytics MSc student is required to complete a 60 credit project dissertation. It not only trains students' ability to apply the risk analytical tools to solve real-world problems, but also provides a chance to practice collaboration and communication skills and data visualisation skills. A student must find a potential supervisor and fill out a Risk Analytics MSc Project Approval Form by the end of Semester B. The supervisor and project must be approved by the Risk Analytics MSc Programme Director, and the process for this, which may involve an interview with the student, takes place as approval forms are submitted. A typical MSc project dissertation consists of about 30 pages, covering a specific research-level topic in Risk Analytics, usually requiring the student to apply risk management tools to measure, predict, or manage certain types of risks. An MSc project may also involve collaboration with a collaborator based in industry. An MSc project should help prepare a good student for PhD research and even allow an excellent student the possibility of doing some research.
This module provides students with a wide-ranging knowledge of financial instruments and markets. It focuses on issues related to the role of a financial system, the functions of different types of financial institutions, and the understanding of financial products commonly traded in each financial market (including the equity market, money market, bond market and derivatives markets with applications using Excel/VBA). Additionally, you will gain an understanding of modern portfolio construction and management. This module will give you the practical knowledge that is essential for a career in investment banking or financial markets.
Data Analytics refers to the use of statistics and machine learning in inferring information from data sets, with the ultimate goal of gaining insight and aiding decision-making. This module introduces statistical modelling, regression analysis, and machine learning, and the use of the R software environment in analyzing data.
Elective modules
This module gives you the practical knowledge that is essential for a career not only in risk management functions, but also in regulatory institutes, e.g., central banks. It is based on Foundations of Mathematics and Statistics and goes deeper, from the lens of regulators specifically. We discuss different types of systemic risks and corresponding strategies to manage them. Then we study models on systemic risk and financial crises, e.g., extreme value theory, network analysis, and learn their recent development and application. Real data on past crises are analysed using the models. To equip you as a future regulator, we introduce the most frontier risk regulation and risk culture across different countries and areas as well. You will take the initiative to propose appropriate risk regulation in the context of risk cultures.
In this module, we discuss contemporary climate risks and sustainability issues, and measure them using various risk analytical models. We first introduce basic analytical tools for climate risk management. Then you are guided to develop appropriate strategies to manage the climate risks and evaluate responses. The module also helps you critically understand the legislation across the world relating to climate risk management and the implications for business. We use real-world data to perform climate risk analytics under different climate scenarios, which predict different climate futures.
Optimisation refers to the selection of the best alternative, according to some criterion, from a set of available alternatives. This module introduces standard models from mathematical optimisation, like network flows and linear programmes, and their use in solving real-world optimisation problems; in staff and project scheduling, commodity trading, production, and sales. Tutorials focus on modelling of real-world optimisation problems based on data, and on the use of software such as R, Excel, and Gurobi to solve optimisation problems and make better decisions.
This module is key for students wishing to further their understanding of the visualisation techniques used in business decision processes using the powerful SAS Visual Analytics software. You will learn Visual Basic for Applications (VBA), the most prevalent programming language in industry and some Structured Query Language (SQL) for data manipulation. The course is taught by an actuary with 15 years industry experience in this area.
This module will introduce students to the elementary mathematics and analytics of investment for digital and real assets. This module will develop, from a practical approach, an understanding of the analytics of several asset classes that are currently included in investment portfolios, such as commodities, real estate, art and cryptoassets, and how these assets' statistical properties fit in the context of the portfolio. The module focuses on the concepts and characteristics of digital and real assets. It will introduce students to the mathematics of the Theory of Storage for commodities, the mathematics of indexes and uses in the real estate and art markets, trading algorithms, and cryptocurrency investment strategies such as staking, De-Fi, and non-fungible tokens. This module is particularly useful for students considering a career in financial mathematics, finance, investment management, investment banking, consultancy or asset management.
This module introduces modern methods of statistical inference for small samples, which use computational methods of analysis, rather than asymptotic theory. The techniques covered in the module include non-parametric tests, bootstrap, and cross-validation. Most of these methods are now used regularly in modern business, finance, and science. Finally, the module includes the implementation of all the proposed methods with the statistics software R.
Assessment
- 67% Modules
- 33%
- You will be assessed by a mixture of formal examinations and coursework in your taught modules
- You will undertake more self-directed work in completing your final Data Analytics Project
Teaching
Our programme is designed to be accessible to students from diverse academic backgrounds, equipping them with practical, industry-relevant skills. Through hands-on learning, real-world case studies, and interactive coursework, you will build your confidence in applying analytical methods to solve complex risk-related challenges.
You will learn primarily through a combination of lectures and tutorials, in addition to a significant amount of independent study and research.
You will be assigned an Academic Adviser who will guide you throughout your time at Queen Mary. The School of Mathematical Sciences also has a dedicated Student Support Officer to provide you with advice and guidance, with a focus on non-academic issues.
Where you'll learn
Facilities
- Our recently refurbished, £18m Mathematical Sciences building with high-quality teaching rooms, private and group study areas and a new social hub
- A shared office and dedicated computer lab with Bloomberg terminals for MSc students
- Library access to 8,000 mathematical books and subscriptions to a large number of mathematical journals
- On-campus accommodation for all new full-time postgraduate students from outside London
Campus
Teaching is based at Queen Mary’s main Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. As well as the Mathematical Sciences building, this includes the new Graduate Centre, providing 7,700 square metres of learning and teaching space.
The campus is 15 minutes by tube from Central London, where you will have access to many of the University of London’s other facilities, such as Senate House.
About the School
School of Mathematical Sciences
Research in the School of Mathematical Sciences covers a range of subjects in pure and applied mathematics, and is consolidated into research groups reflecting the School's key strengths.
We've invested approximately £18 million in our building to provide state-of-the-art facilities for staff and students. We hold an Athena Swan Bronze award.
School of Economics and Finance
- Around 1,000 master’s students from all over the world
- Teaching by research-active academics as well as visiting city professionals
- Ranked 4th in the UK for research output in economics and econometrics in the Research Excellence Framework in 2021
- Wide range of elective modules as well as professional development modules on programming languages, trading platforms such as Bloomberg, etc. (see Programme structure)
- Student investment fund (QUMMIF) provides practical skills in financial analysis and trading as well as opportunities to network with fellow students, academics, and city professionals
Career paths
This programme is designed for students who want a career in risk management or risk regulation anywhere in the world. With such a large number of industries requiring analytical risk management professionals, students who graduate from this programme will have the skills and knowledge to work in a wide range of industries including finance, medicine, IT, science, technology and regulatory bodies.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £33,500
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Part-time study
September 2025 | 2 years
- Home: £6,450
- Overseas: £16,750
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A 2:2 or above at undergraduate level in any subject.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught and research programmes in the School of Mathematics falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.